diff --git a/.agent/skills b/.agent/skills
new file mode 120000
index 0000000000..454b8427cd
--- /dev/null
+++ b/.agent/skills
@@ -0,0 +1 @@
+../.claude/skills
\ No newline at end of file
diff --git a/.claude/settings.json b/.claude/settings.json
new file mode 100644
index 0000000000..72dcb5ec73
--- /dev/null
+++ b/.claude/settings.json
@@ -0,0 +1,22 @@
+{
+ "enabledPlugins": {
+ "feature-dev@claude-plugins-official": true,
+ "context7@claude-plugins-official": true,
+ "typescript-lsp@claude-plugins-official": true,
+ "pyright-lsp@claude-plugins-official": true,
+ "ralph-loop@claude-plugins-official": true
+ },
+ "hooks": {
+ "PreToolUse": [
+ {
+ "matcher": "Bash",
+ "hooks": [
+ {
+ "type": "command",
+ "command": "npx -y block-no-verify@1.1.1"
+ }
+ ]
+ }
+ ]
+ }
+}
diff --git a/.claude/settings.json.example b/.claude/settings.json.example
deleted file mode 100644
index 1149895340..0000000000
--- a/.claude/settings.json.example
+++ /dev/null
@@ -1,19 +0,0 @@
-{
- "permissions": {
- "allow": [],
- "deny": []
- },
- "env": {
- "__comment": "Environment variables for MCP servers. Override in .claude/settings.local.json with actual values.",
- "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
- },
- "enabledMcpjsonServers": [
- "context7",
- "sequential-thinking",
- "github",
- "fetch",
- "playwright",
- "ide"
- ],
- "enableAllProjectMcpServers": true
- }
\ No newline at end of file
diff --git a/.claude/skills/component-refactoring/SKILL.md b/.claude/skills/component-refactoring/SKILL.md
new file mode 100644
index 0000000000..7006c382c8
--- /dev/null
+++ b/.claude/skills/component-refactoring/SKILL.md
@@ -0,0 +1,483 @@
+---
+name: component-refactoring
+description: Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
+---
+
+# Dify Component Refactoring Skill
+
+Refactor high-complexity React components in the Dify frontend codebase with the patterns and workflow below.
+
+> **Complexity Threshold**: Components with complexity > 50 (measured by `pnpm analyze-component`) should be refactored before testing.
+
+## Quick Reference
+
+### Commands (run from `web/`)
+
+Use paths relative to `web/` (e.g., `app/components/...`).
+Use `refactor-component` for refactoring prompts and `analyze-component` for testing prompts and metrics.
+
+```bash
+cd web
+
+# Generate refactoring prompt
+pnpm refactor-component {t('configuration.title')}
+ {t('configuration.title')}
+
File-level coverage (click to expand)
"
+ echo ""
+ echo '```'
+ uv run --project api coverage report -m
+ echo '```'
+ echo "File coverage (lowest lines first)
');
+ console.log('');
+ console.log('```');
+ fileSummaries
+ .sort((a, b) => (a.pct - b.pct) || (b.lines.total - a.lines.total))
+ .slice(0, 25)
+ .forEach(({ file, pct, lines }) => {
+ console.log(`${pct.toFixed(2)}%\t${lines.covered}/${lines.total}\t${file}`);
+ });
+ console.log('```');
+ console.log('Vitest coverage table
');
+ console.log('');
+ console.log(headerRow);
+ console.log(dividerRow);
+ rowsForOutput.forEach((row) => console.log(formatRow(row)));
+ console.log('
+
+
+
+
+
+
@@ -133,6 +139,19 @@ Star Dify on GitHub and be instantly notified of new releases.
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
+#### Customizing Suggested Questions
+
+You can now customize the "Suggested Questions After Answer" feature to better fit your use case. For example, to generate longer, more technical questions:
+
+```bash
+# In your .env file
+SUGGESTED_QUESTIONS_PROMPT='Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: ["question1","question2","question3","question4","question5"]'
+SUGGESTED_QUESTIONS_MAX_TOKENS=512
+SUGGESTED_QUESTIONS_TEMPERATURE=0.3
+```
+
+See the [Suggested Questions Configuration Guide](docs/suggested-questions-configuration.md) for detailed examples and usage instructions.
+
### Metrics Monitoring with Grafana
Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.
diff --git a/api/.env.example b/api/.env.example
index 24f496a7b1..6effeeb88c 100644
--- a/api/.env.example
+++ b/api/.env.example
@@ -75,12 +75,15 @@ REDIS_CLUSTERS_PASSWORD=
# celery configuration
CELERY_BROKER_URL=redis://:difyai123456@localhost:${REDIS_PORT}/1
CELERY_BACKEND=redis
-# PostgreSQL database configuration
+
+# Database configuration
+DB_TYPE=postgresql
DB_USERNAME=postgres
DB_PASSWORD=difyai123456
DB_HOST=localhost
DB_PORT=5432
DB_DATABASE=dify
+
SQLALCHEMY_POOL_PRE_PING=true
SQLALCHEMY_POOL_TIMEOUT=30
@@ -101,6 +104,15 @@ S3_ACCESS_KEY=your-access-key
S3_SECRET_KEY=your-secret-key
S3_REGION=your-region
+# Workflow run and Conversation archive storage (S3-compatible)
+ARCHIVE_STORAGE_ENABLED=false
+ARCHIVE_STORAGE_ENDPOINT=
+ARCHIVE_STORAGE_ARCHIVE_BUCKET=
+ARCHIVE_STORAGE_EXPORT_BUCKET=
+ARCHIVE_STORAGE_ACCESS_KEY=
+ARCHIVE_STORAGE_SECRET_KEY=
+ARCHIVE_STORAGE_REGION=auto
+
# Azure Blob Storage configuration
AZURE_BLOB_ACCOUNT_NAME=your-account-name
AZURE_BLOB_ACCOUNT_KEY=your-account-key
@@ -116,6 +128,7 @@ ALIYUN_OSS_AUTH_VERSION=v1
ALIYUN_OSS_REGION=your-region
# Don't start with '/'. OSS doesn't support leading slash in object names.
ALIYUN_OSS_PATH=your-path
+ALIYUN_CLOUDBOX_ID=your-cloudbox-id
# Google Storage configuration
GOOGLE_STORAGE_BUCKET_NAME=your-bucket-name
@@ -127,12 +140,14 @@ TENCENT_COS_SECRET_KEY=your-secret-key
TENCENT_COS_SECRET_ID=your-secret-id
TENCENT_COS_REGION=your-region
TENCENT_COS_SCHEME=your-scheme
+TENCENT_COS_CUSTOM_DOMAIN=your-custom-domain
# Huawei OBS Storage Configuration
HUAWEI_OBS_BUCKET_NAME=your-bucket-name
HUAWEI_OBS_SECRET_KEY=your-secret-key
HUAWEI_OBS_ACCESS_KEY=your-access-key
HUAWEI_OBS_SERVER=your-server-url
+HUAWEI_OBS_PATH_STYLE=false
# Baidu OBS Storage Configuration
BAIDU_OBS_BUCKET_NAME=your-bucket-name
@@ -162,12 +177,11 @@ SUPABASE_URL=your-server-url
# CORS configuration
WEB_API_CORS_ALLOW_ORIGINS=http://localhost:3000,*
CONSOLE_CORS_ALLOW_ORIGINS=http://localhost:3000,*
-# Set COOKIE_DOMAIN when the console frontend and API are on different subdomains.
-# Provide the registrable domain (e.g. example.com); leading dots are optional.
+# When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). Leading dots are optional.
COOKIE_DOMAIN=
# Vector database configuration
-# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`, `oceanbase`, `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`.
+# Supported values are `weaviate`, `oceanbase`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`, `opengauss`, `tablestore`,`vastbase`,`tidb`,`tidb_on_qdrant`,`baidu`,`lindorm`,`huawei_cloud`,`upstash`, `matrixone`.
VECTOR_STORE=weaviate
# Prefix used to create collection name in vector database
VECTOR_INDEX_NAME_PREFIX=Vector_index
@@ -177,6 +191,18 @@ WEAVIATE_ENDPOINT=http://localhost:8080
WEAVIATE_API_KEY=WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih
WEAVIATE_GRPC_ENABLED=false
WEAVIATE_BATCH_SIZE=100
+WEAVIATE_TOKENIZATION=word
+
+# OceanBase Vector configuration
+OCEANBASE_VECTOR_HOST=127.0.0.1
+OCEANBASE_VECTOR_PORT=2881
+OCEANBASE_VECTOR_USER=root@test
+OCEANBASE_VECTOR_PASSWORD=difyai123456
+OCEANBASE_VECTOR_DATABASE=test
+OCEANBASE_MEMORY_LIMIT=6G
+OCEANBASE_ENABLE_HYBRID_SEARCH=false
+OCEANBASE_FULLTEXT_PARSER=ik
+SEEKDB_MEMORY_LIMIT=2G
# Qdrant configuration, use `http://localhost:6333` for local mode or `https://your-qdrant-cluster-url.qdrant.io` for remote mode
QDRANT_URL=http://localhost:6333
@@ -343,15 +369,6 @@ LINDORM_PASSWORD=admin
LINDORM_USING_UGC=True
LINDORM_QUERY_TIMEOUT=1
-# OceanBase Vector configuration
-OCEANBASE_VECTOR_HOST=127.0.0.1
-OCEANBASE_VECTOR_PORT=2881
-OCEANBASE_VECTOR_USER=root@test
-OCEANBASE_VECTOR_PASSWORD=difyai123456
-OCEANBASE_VECTOR_DATABASE=test
-OCEANBASE_MEMORY_LIMIT=6G
-OCEANBASE_ENABLE_HYBRID_SEARCH=false
-
# AlibabaCloud MySQL Vector configuration
ALIBABACLOUD_MYSQL_HOST=127.0.0.1
ALIBABACLOUD_MYSQL_PORT=3306
@@ -403,6 +420,8 @@ SMTP_USERNAME=123
SMTP_PASSWORD=abc
SMTP_USE_TLS=true
SMTP_OPPORTUNISTIC_TLS=false
+# Optional: override the local hostname used for SMTP HELO/EHLO
+SMTP_LOCAL_HOSTNAME=
# Sendgid configuration
SENDGRID_API_KEY=
# Sentry configuration
@@ -488,6 +507,8 @@ LOG_FILE_BACKUP_COUNT=5
LOG_DATEFORMAT=%Y-%m-%d %H:%M:%S
# Log Timezone
LOG_TZ=UTC
+# Log output format: text or json
+LOG_OUTPUT_FORMAT=text
# Log format
LOG_FORMAT=%(asctime)s,%(msecs)d %(levelname)-2s [%(filename)s:%(lineno)d] %(req_id)s %(message)s
@@ -538,8 +559,32 @@ WORKFLOW_LOG_CLEANUP_BATCH_SIZE=100
# App configuration
APP_MAX_EXECUTION_TIME=1200
+APP_DEFAULT_ACTIVE_REQUESTS=0
APP_MAX_ACTIVE_REQUESTS=0
+# Aliyun SLS Logstore Configuration
+# Aliyun Access Key ID
+ALIYUN_SLS_ACCESS_KEY_ID=
+# Aliyun Access Key Secret
+ALIYUN_SLS_ACCESS_KEY_SECRET=
+# Aliyun SLS Endpoint (e.g., cn-hangzhou.log.aliyuncs.com)
+ALIYUN_SLS_ENDPOINT=
+# Aliyun SLS Region (e.g., cn-hangzhou)
+ALIYUN_SLS_REGION=
+# Aliyun SLS Project Name
+ALIYUN_SLS_PROJECT_NAME=
+# Number of days to retain workflow run logs (default: 365 days, 3650 for permanent storage)
+ALIYUN_SLS_LOGSTORE_TTL=365
+# Enable dual-write to both SLS LogStore and SQL database (default: false)
+LOGSTORE_DUAL_WRITE_ENABLED=false
+# Enable dual-read fallback to SQL database when LogStore returns no results (default: true)
+# Useful for migration scenarios where historical data exists only in SQL database
+LOGSTORE_DUAL_READ_ENABLED=true
+# Control flag for whether to write the `graph` field to LogStore.
+# If LOGSTORE_ENABLE_PUT_GRAPH_FIELD is "true", write the full `graph` field;
+# otherwise write an empty {} instead. Defaults to writing the `graph` field.
+LOGSTORE_ENABLE_PUT_GRAPH_FIELD=true
+
# Celery beat configuration
CELERY_BEAT_SCHEDULER_TIME=1
@@ -549,6 +594,7 @@ ENABLE_CLEAN_UNUSED_DATASETS_TASK=false
ENABLE_CREATE_TIDB_SERVERLESS_TASK=false
ENABLE_UPDATE_TIDB_SERVERLESS_STATUS_TASK=false
ENABLE_CLEAN_MESSAGES=false
+ENABLE_WORKFLOW_RUN_CLEANUP_TASK=false
ENABLE_MAIL_CLEAN_DOCUMENT_NOTIFY_TASK=false
ENABLE_DATASETS_QUEUE_MONITOR=false
ENABLE_CHECK_UPGRADABLE_PLUGIN_TASK=true
@@ -630,8 +676,46 @@ SWAGGER_UI_PATH=/swagger-ui.html
# Set to false to export dataset IDs as plain text for easier cross-environment import
DSL_EXPORT_ENCRYPT_DATASET_ID=true
+# Suggested Questions After Answer Configuration
+# These environment variables allow customization of the suggested questions feature
+#
+# Custom prompt for generating suggested questions (optional)
+# If not set, uses the default prompt that generates 3 questions under 20 characters each
+# Example: "Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: [\"question1\",\"question2\",\"question3\",\"question4\",\"question5\"]"
+# SUGGESTED_QUESTIONS_PROMPT=
+
+# Maximum number of tokens for suggested questions generation (default: 256)
+# Adjust this value for longer questions or more questions
+# SUGGESTED_QUESTIONS_MAX_TOKENS=256
+
+# Temperature for suggested questions generation (default: 0.0)
+# Higher values (0.5-1.0) produce more creative questions, lower values (0.0-0.3) produce more focused questions
+# SUGGESTED_QUESTIONS_TEMPERATURE=0
+
# Tenant isolated task queue configuration
TENANT_ISOLATED_TASK_CONCURRENCY=1
# Maximum number of segments for dataset segments API (0 for unlimited)
DATASET_MAX_SEGMENTS_PER_REQUEST=0
+
+# Multimodal knowledgebase limit
+SINGLE_CHUNK_ATTACHMENT_LIMIT=10
+ATTACHMENT_IMAGE_FILE_SIZE_LIMIT=2
+ATTACHMENT_IMAGE_DOWNLOAD_TIMEOUT=60
+IMAGE_FILE_BATCH_LIMIT=10
+
+# Maximum allowed CSV file size for annotation import in megabytes
+ANNOTATION_IMPORT_FILE_SIZE_LIMIT=2
+#Maximum number of annotation records allowed in a single import
+ANNOTATION_IMPORT_MAX_RECORDS=10000
+# Minimum number of annotation records required in a single import
+ANNOTATION_IMPORT_MIN_RECORDS=1
+ANNOTATION_IMPORT_RATE_LIMIT_PER_MINUTE=5
+ANNOTATION_IMPORT_RATE_LIMIT_PER_HOUR=20
+# Maximum number of concurrent annotation import tasks per tenant
+ANNOTATION_IMPORT_MAX_CONCURRENT=5
+# Sandbox expired records clean configuration
+SANDBOX_EXPIRED_RECORDS_CLEAN_GRACEFUL_PERIOD=21
+SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE=1000
+SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS=30
+
diff --git a/api/.importlinter b/api/.importlinter
index 98fe5f50bb..2dec958788 100644
--- a/api/.importlinter
+++ b/api/.importlinter
@@ -3,9 +3,11 @@ root_packages =
core
configs
controllers
+ extensions
models
tasks
services
+include_external_packages = True
[importlinter:contract:workflow]
name = Workflow
@@ -16,6 +18,7 @@ layers =
graph
nodes
node_events
+ runtime
entities
containers =
core.workflow
@@ -32,6 +35,28 @@ ignore_imports =
core.workflow.nodes.loop.loop_node -> core.workflow.graph
core.workflow.nodes.loop.loop_node -> core.workflow.graph_engine.command_channels
+[importlinter:contract:workflow-infrastructure-dependencies]
+name = Workflow Infrastructure Dependencies
+type = forbidden
+source_modules =
+ core.workflow
+forbidden_modules =
+ extensions.ext_database
+ extensions.ext_redis
+allow_indirect_imports = True
+ignore_imports =
+ core.workflow.nodes.agent.agent_node -> extensions.ext_database
+ core.workflow.nodes.datasource.datasource_node -> extensions.ext_database
+ core.workflow.nodes.knowledge_index.knowledge_index_node -> extensions.ext_database
+ core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_database
+ core.workflow.nodes.llm.file_saver -> extensions.ext_database
+ core.workflow.nodes.llm.llm_utils -> extensions.ext_database
+ core.workflow.nodes.llm.node -> extensions.ext_database
+ core.workflow.nodes.tool.tool_node -> extensions.ext_database
+ core.workflow.graph_engine.command_channels.redis_channel -> extensions.ext_redis
+ core.workflow.graph_engine.manager -> extensions.ext_redis
+ core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> extensions.ext_redis
+
[importlinter:contract:rsc]
name = RSC
type = layers
diff --git a/api/.ruff.toml b/api/.ruff.toml
index 5a29e1d8fa..8db0cbcb21 100644
--- a/api/.ruff.toml
+++ b/api/.ruff.toml
@@ -1,4 +1,8 @@
-exclude = ["migrations/*"]
+exclude = [
+ "migrations/*",
+ ".git",
+ ".git/**",
+]
line-length = 120
[format]
@@ -36,17 +40,20 @@ select = [
"UP", # pyupgrade rules
"W191", # tab-indentation
"W605", # invalid-escape-sequence
+ "G001", # don't use str format to logging messages
+ "G003", # don't use + in logging messages
+ "G004", # don't use f-strings to format logging messages
+ "UP042", # use StrEnum,
+ "S110", # disallow the try-except-pass pattern.
+
# security related linting rules
# RCE proctection (sort of)
"S102", # exec-builtin, disallow use of `exec`
"S307", # suspicious-eval-usage, disallow use of `eval` and `ast.literal_eval`
"S301", # suspicious-pickle-usage, disallow use of `pickle` and its wrappers.
"S302", # suspicious-marshal-usage, disallow use of `marshal` module
- "S311", # suspicious-non-cryptographic-random-usage
- "G001", # don't use str format to logging messages
- "G003", # don't use + in logging messages
- "G004", # don't use f-strings to format logging messages
- "UP042", # use StrEnum
+ "S311", # suspicious-non-cryptographic-random-usage,
+
]
ignore = [
@@ -91,18 +98,16 @@ ignore = [
"configs/*" = [
"N802", # invalid-function-name
]
-"core/model_runtime/callbacks/base_callback.py" = [
- "T201",
-]
-"core/workflow/callbacks/workflow_logging_callback.py" = [
- "T201",
-]
+"core/model_runtime/callbacks/base_callback.py" = ["T201"]
+"core/workflow/callbacks/workflow_logging_callback.py" = ["T201"]
"libs/gmpy2_pkcs10aep_cipher.py" = [
"N803", # invalid-argument-name
]
"tests/*" = [
"F811", # redefined-while-unused
- "T201", # allow print in tests
+ "T201", # allow print in tests,
+ "S110", # allow ignoring exceptions in tests code (currently)
+
]
[lint.pyflakes]
diff --git a/api/Dockerfile b/api/Dockerfile
index ed61923a40..a08d4e3aab 100644
--- a/api/Dockerfile
+++ b/api/Dockerfile
@@ -48,16 +48,39 @@ ENV PYTHONIOENCODING=utf-8
WORKDIR /app/api
+# Create non-root user
+ARG dify_uid=1001
+ARG NODE_MAJOR=22
+ARG NODE_PACKAGE_VERSION=22.21.0-1nodesource1
+ARG NODESOURCE_KEY_FPR=6F71F525282841EEDAF851B42F59B5F99B1BE0B4
+RUN groupadd -r -g ${dify_uid} dify && \
+ useradd -r -u ${dify_uid} -g ${dify_uid} -s /bin/bash dify && \
+ chown -R dify:dify /app
+
RUN \
apt-get update \
+ && apt-get install -y --no-install-recommends \
+ ca-certificates \
+ curl \
+ gnupg \
+ && mkdir -p /etc/apt/keyrings \
+ && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource.gpg \
+ && gpg --show-keys --with-colons /tmp/nodesource.gpg \
+ | awk -F: '/^fpr:/ {print $10}' \
+ | grep -Fx "${NODESOURCE_KEY_FPR}" \
+ && gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource.gpg \
+ && rm -f /tmp/nodesource.gpg \
+ && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_${NODE_MAJOR}.x nodistro main" \
+ > /etc/apt/sources.list.d/nodesource.list \
+ && apt-get update \
# Install dependencies
&& apt-get install -y --no-install-recommends \
# basic environment
- curl nodejs \
+ nodejs=${NODE_PACKAGE_VERSION} \
# for gmpy2 \
libgmp-dev libmpfr-dev libmpc-dev \
# For Security
- expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
+ expat libldap-2.5-0=2.5.13+dfsg-5 perl libsqlite3-0=3.40.1-2+deb12u2 zlib1g=1:1.2.13.dfsg-1 \
# install fonts to support the use of tools like pypdfium2
fonts-noto-cjk \
# install a package to improve the accuracy of guessing mime type and file extension
@@ -69,24 +92,30 @@ RUN \
# Copy Python environment and packages
ENV VIRTUAL_ENV=/app/api/.venv
-COPY --from=packages ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+COPY --from=packages --chown=dify:dify ${VIRTUAL_ENV} ${VIRTUAL_ENV}
ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
# Download nltk data
-RUN python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger')"
+RUN mkdir -p /usr/local/share/nltk_data \
+ && NLTK_DATA=/usr/local/share/nltk_data python -c "import nltk; from unstructured.nlp.tokenize import download_nltk_packages; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger'); nltk.download('stopwords'); download_nltk_packages()" \
+ && chmod -R 755 /usr/local/share/nltk_data
ENV TIKTOKEN_CACHE_DIR=/app/api/.tiktoken_cache
-RUN python -c "import tiktoken; tiktoken.encoding_for_model('gpt2')"
+RUN python -c "import tiktoken; tiktoken.encoding_for_model('gpt2')" \
+ && chown -R dify:dify ${TIKTOKEN_CACHE_DIR}
# Copy source code
-COPY . /app/api/
+COPY --chown=dify:dify . /app/api/
+
+# Prepare entrypoint script
+COPY --chown=dify:dify --chmod=755 docker/entrypoint.sh /entrypoint.sh
-# Copy entrypoint
-COPY docker/entrypoint.sh /entrypoint.sh
-RUN chmod +x /entrypoint.sh
ARG COMMIT_SHA
ENV COMMIT_SHA=${COMMIT_SHA}
+ENV NLTK_DATA=/usr/local/share/nltk_data
+
+USER dify
ENTRYPOINT ["/bin/bash", "/entrypoint.sh"]
diff --git a/api/README.md b/api/README.md
index 45dad07af0..794b05d3af 100644
--- a/api/README.md
+++ b/api/README.md
@@ -15,8 +15,8 @@
```bash
cd ../docker
cp middleware.env.example middleware.env
- # change the profile to other vector database if you are not using weaviate
- docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
+ # change the profile to mysql if you are not using postgres,change the profile to other vector database if you are not using weaviate
+ docker compose -f docker-compose.middleware.yaml --profile postgresql --profile weaviate -p dify up -d
cd ../api
```
@@ -26,6 +26,10 @@
cp .env.example .env
```
+> [!IMPORTANT]
+>
+> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
+
1. Generate a `SECRET_KEY` in the `.env` file.
bash for Linux
@@ -80,7 +84,7 @@
1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
```bash
-uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,priority_pipeline,pipeline
+uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
```
Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
diff --git a/api/app_factory.py b/api/app_factory.py
index 449e67971d..8d3d13d12a 100644
--- a/api/app_factory.py
+++ b/api/app_factory.py
@@ -1,8 +1,12 @@
import logging
import time
+from opentelemetry.trace import get_current_span
+from opentelemetry.trace.span import INVALID_SPAN_ID, INVALID_TRACE_ID
+
from configs import dify_config
from contexts.wrapper import RecyclableContextVar
+from core.logging.context import init_request_context
from dify_app import DifyApp
logger = logging.getLogger(__name__)
@@ -18,15 +22,40 @@ def create_flask_app_with_configs() -> DifyApp:
"""
dify_app = DifyApp(__name__)
dify_app.config.from_mapping(dify_config.model_dump())
+ dify_app.config["RESTX_INCLUDE_ALL_MODELS"] = True
# add before request hook
@dify_app.before_request
def before_request():
- # add an unique identifier to each request
+ # Initialize logging context for this request
+ init_request_context()
RecyclableContextVar.increment_thread_recycles()
+ # add after request hook for injecting trace headers from OpenTelemetry span context
+ # Only adds headers when OTEL is enabled and has valid context
+ @dify_app.after_request
+ def add_trace_headers(response):
+ try:
+ span = get_current_span()
+ ctx = span.get_span_context() if span else None
+
+ if not ctx or not ctx.is_valid:
+ return response
+
+ # Inject trace headers from OTEL context
+ if ctx.trace_id != INVALID_TRACE_ID and "X-Trace-Id" not in response.headers:
+ response.headers["X-Trace-Id"] = format(ctx.trace_id, "032x")
+ if ctx.span_id != INVALID_SPAN_ID and "X-Span-Id" not in response.headers:
+ response.headers["X-Span-Id"] = format(ctx.span_id, "016x")
+
+ except Exception:
+ # Never break the response due to tracing header injection
+ logger.warning("Failed to add trace headers to response", exc_info=True)
+ return response
+
# Capture the decorator's return value to avoid pyright reportUnusedFunction
_ = before_request
+ _ = add_trace_headers
return dify_app
@@ -58,10 +87,12 @@ def initialize_extensions(app: DifyApp):
ext_commands,
ext_compress,
ext_database,
+ ext_forward_refs,
ext_hosting_provider,
ext_import_modules,
ext_logging,
ext_login,
+ ext_logstore,
ext_mail,
ext_migrate,
ext_orjson,
@@ -70,6 +101,7 @@ def initialize_extensions(app: DifyApp):
ext_redis,
ext_request_logging,
ext_sentry,
+ ext_session_factory,
ext_set_secretkey,
ext_storage,
ext_timezone,
@@ -82,6 +114,7 @@ def initialize_extensions(app: DifyApp):
ext_warnings,
ext_import_modules,
ext_orjson,
+ ext_forward_refs,
ext_set_secretkey,
ext_compress,
ext_code_based_extension,
@@ -90,6 +123,7 @@ def initialize_extensions(app: DifyApp):
ext_migrate,
ext_redis,
ext_storage,
+ ext_logstore, # Initialize logstore after storage, before celery
ext_celery,
ext_login,
ext_mail,
@@ -100,6 +134,7 @@ def initialize_extensions(app: DifyApp):
ext_commands,
ext_otel,
ext_request_logging,
+ ext_session_factory,
]
for ext in extensions:
short_name = ext.__name__.split(".")[-1]
diff --git a/api/cnt_base.sh b/api/cnt_base.sh
new file mode 100755
index 0000000000..9e407f3584
--- /dev/null
+++ b/api/cnt_base.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+set -euxo pipefail
+
+for pattern in "Base" "TypeBase"; do
+ printf "%s " "$pattern"
+ grep "($pattern):" -r --include='*.py' --exclude-dir=".venv" --exclude-dir="tests" . | wc -l
+done
diff --git a/api/commands.py b/api/commands.py
index e15c996a34..e223df74d4 100644
--- a/api/commands.py
+++ b/api/commands.py
@@ -1,7 +1,9 @@
import base64
+import datetime
import json
import logging
import secrets
+import time
from typing import Any
import click
@@ -34,7 +36,7 @@ from libs.rsa import generate_key_pair
from models import Tenant
from models.dataset import Dataset, DatasetCollectionBinding, DatasetMetadata, DatasetMetadataBinding, DocumentSegment
from models.dataset import Document as DatasetDocument
-from models.model import Account, App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation, UploadFile
+from models.model import App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation, UploadFile
from models.oauth import DatasourceOauthParamConfig, DatasourceProvider
from models.provider import Provider, ProviderModel
from models.provider_ids import DatasourceProviderID, ToolProviderID
@@ -45,6 +47,9 @@ from services.clear_free_plan_tenant_expired_logs import ClearFreePlanTenantExpi
from services.plugin.data_migration import PluginDataMigration
from services.plugin.plugin_migration import PluginMigration
from services.plugin.plugin_service import PluginService
+from services.retention.conversation.messages_clean_policy import create_message_clean_policy
+from services.retention.conversation.messages_clean_service import MessagesCleanService
+from services.retention.workflow_run.clear_free_plan_expired_workflow_run_logs import WorkflowRunCleanup
from tasks.remove_app_and_related_data_task import delete_draft_variables_batch
logger = logging.getLogger(__name__)
@@ -62,8 +67,10 @@ def reset_password(email, new_password, password_confirm):
if str(new_password).strip() != str(password_confirm).strip():
click.echo(click.style("Passwords do not match.", fg="red"))
return
+ normalized_email = email.strip().lower()
+
with sessionmaker(db.engine, expire_on_commit=False).begin() as session:
- account = session.query(Account).where(Account.email == email).one_or_none()
+ account = AccountService.get_account_by_email_with_case_fallback(email.strip(), session=session)
if not account:
click.echo(click.style(f"Account not found for email: {email}", fg="red"))
@@ -84,7 +91,7 @@ def reset_password(email, new_password, password_confirm):
base64_password_hashed = base64.b64encode(password_hashed).decode()
account.password = base64_password_hashed
account.password_salt = base64_salt
- AccountService.reset_login_error_rate_limit(email)
+ AccountService.reset_login_error_rate_limit(normalized_email)
click.echo(click.style("Password reset successfully.", fg="green"))
@@ -100,20 +107,22 @@ def reset_email(email, new_email, email_confirm):
if str(new_email).strip() != str(email_confirm).strip():
click.echo(click.style("New emails do not match.", fg="red"))
return
+ normalized_new_email = new_email.strip().lower()
+
with sessionmaker(db.engine, expire_on_commit=False).begin() as session:
- account = session.query(Account).where(Account.email == email).one_or_none()
+ account = AccountService.get_account_by_email_with_case_fallback(email.strip(), session=session)
if not account:
click.echo(click.style(f"Account not found for email: {email}", fg="red"))
return
try:
- email_validate(new_email)
+ email_validate(normalized_new_email)
except:
click.echo(click.style(f"Invalid email: {new_email}", fg="red"))
return
- account.email = new_email
+ account.email = normalized_new_email
click.echo(click.style("Email updated successfully.", fg="green"))
@@ -235,7 +244,7 @@ def migrate_annotation_vector_database():
if annotations:
for annotation in annotations:
document = Document(
- page_content=annotation.question,
+ page_content=annotation.question_text,
metadata={"annotation_id": annotation.id, "app_id": app.id, "doc_id": annotation.id},
)
documents.append(document)
@@ -658,7 +667,7 @@ def create_tenant(email: str, language: str | None = None, name: str | None = No
return
# Create account
- email = email.strip()
+ email = email.strip().lower()
if "@" not in email:
click.echo(click.style("Invalid email address.", fg="red"))
@@ -852,6 +861,61 @@ def clear_free_plan_tenant_expired_logs(days: int, batch: int, tenant_ids: list[
click.echo(click.style("Clear free plan tenant expired logs completed.", fg="green"))
+@click.command("clean-workflow-runs", help="Clean expired workflow runs and related data for free tenants.")
+@click.option("--days", default=30, show_default=True, help="Delete workflow runs created before N days ago.")
+@click.option("--batch-size", default=200, show_default=True, help="Batch size for selecting workflow runs.")
+@click.option(
+ "--start-from",
+ type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+ default=None,
+ help="Optional lower bound (inclusive) for created_at; must be paired with --end-before.",
+)
+@click.option(
+ "--end-before",
+ type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+ default=None,
+ help="Optional upper bound (exclusive) for created_at; must be paired with --start-from.",
+)
+@click.option(
+ "--dry-run",
+ is_flag=True,
+ help="Preview cleanup results without deleting any workflow run data.",
+)
+def clean_workflow_runs(
+ days: int,
+ batch_size: int,
+ start_from: datetime.datetime | None,
+ end_before: datetime.datetime | None,
+ dry_run: bool,
+):
+ """
+ Clean workflow runs and related workflow data for free tenants.
+ """
+ if (start_from is None) ^ (end_before is None):
+ raise click.UsageError("--start-from and --end-before must be provided together.")
+
+ start_time = datetime.datetime.now(datetime.UTC)
+ click.echo(click.style(f"Starting workflow run cleanup at {start_time.isoformat()}.", fg="white"))
+
+ WorkflowRunCleanup(
+ days=days,
+ batch_size=batch_size,
+ start_from=start_from,
+ end_before=end_before,
+ dry_run=dry_run,
+ ).run()
+
+ end_time = datetime.datetime.now(datetime.UTC)
+ elapsed = end_time - start_time
+ click.echo(
+ click.style(
+ f"Workflow run cleanup completed. start={start_time.isoformat()} "
+ f"end={end_time.isoformat()} duration={elapsed}",
+ fg="green",
+ )
+ )
+
+
@click.option("-f", "--force", is_flag=True, help="Skip user confirmation and force the command to execute.")
@click.command("clear-orphaned-file-records", help="Clear orphaned file records.")
def clear_orphaned_file_records(force: bool):
@@ -1139,6 +1203,7 @@ def remove_orphaned_files_on_storage(force: bool):
click.echo(click.style(f"Found {len(all_files_in_tables)} files in tables.", fg="white"))
except Exception as e:
click.echo(click.style(f"Error fetching keys: {str(e)}", fg="red"))
+ return
all_files_on_storage = []
for storage_path in storage_paths:
@@ -1183,6 +1248,217 @@ def remove_orphaned_files_on_storage(force: bool):
click.echo(click.style(f"Removed {removed_files} orphaned files, with {error_files} errors.", fg="yellow"))
+@click.command("file-usage", help="Query file usages and show where files are referenced.")
+@click.option("--file-id", type=str, default=None, help="Filter by file UUID.")
+@click.option("--key", type=str, default=None, help="Filter by storage key.")
+@click.option("--src", type=str, default=None, help="Filter by table.column pattern (e.g., 'documents.%' or '%.icon').")
+@click.option("--limit", type=int, default=100, help="Limit number of results (default: 100).")
+@click.option("--offset", type=int, default=0, help="Offset for pagination (default: 0).")
+@click.option("--json", "output_json", is_flag=True, help="Output results in JSON format.")
+def file_usage(
+ file_id: str | None,
+ key: str | None,
+ src: str | None,
+ limit: int,
+ offset: int,
+ output_json: bool,
+):
+ """
+ Query file usages and show where files are referenced in the database.
+
+ This command reuses the same reference checking logic as clear-orphaned-file-records
+ and displays detailed information about where each file is referenced.
+ """
+ # define tables and columns to process
+ files_tables = [
+ {"table": "upload_files", "id_column": "id", "key_column": "key"},
+ {"table": "tool_files", "id_column": "id", "key_column": "file_key"},
+ ]
+ ids_tables = [
+ {"type": "uuid", "table": "message_files", "column": "upload_file_id", "pk_column": "id"},
+ {"type": "text", "table": "documents", "column": "data_source_info", "pk_column": "id"},
+ {"type": "text", "table": "document_segments", "column": "content", "pk_column": "id"},
+ {"type": "text", "table": "messages", "column": "answer", "pk_column": "id"},
+ {"type": "text", "table": "workflow_node_executions", "column": "inputs", "pk_column": "id"},
+ {"type": "text", "table": "workflow_node_executions", "column": "process_data", "pk_column": "id"},
+ {"type": "text", "table": "workflow_node_executions", "column": "outputs", "pk_column": "id"},
+ {"type": "text", "table": "conversations", "column": "introduction", "pk_column": "id"},
+ {"type": "text", "table": "conversations", "column": "system_instruction", "pk_column": "id"},
+ {"type": "text", "table": "accounts", "column": "avatar", "pk_column": "id"},
+ {"type": "text", "table": "apps", "column": "icon", "pk_column": "id"},
+ {"type": "text", "table": "sites", "column": "icon", "pk_column": "id"},
+ {"type": "json", "table": "messages", "column": "inputs", "pk_column": "id"},
+ {"type": "json", "table": "messages", "column": "message", "pk_column": "id"},
+ ]
+
+ # Stream file usages with pagination to avoid holding all results in memory
+ paginated_usages = []
+ total_count = 0
+
+ # First, build a mapping of file_id -> storage_key from the base tables
+ file_key_map = {}
+ for files_table in files_tables:
+ query = f"SELECT {files_table['id_column']}, {files_table['key_column']} FROM {files_table['table']}"
+ with db.engine.begin() as conn:
+ rs = conn.execute(sa.text(query))
+ for row in rs:
+ file_key_map[str(row[0])] = f"{files_table['table']}:{row[1]}"
+
+ # If filtering by key or file_id, verify it exists
+ if file_id and file_id not in file_key_map:
+ if output_json:
+ click.echo(json.dumps({"error": f"File ID {file_id} not found in base tables"}))
+ else:
+ click.echo(click.style(f"File ID {file_id} not found in base tables.", fg="red"))
+ return
+
+ if key:
+ valid_prefixes = {f"upload_files:{key}", f"tool_files:{key}"}
+ matching_file_ids = [fid for fid, fkey in file_key_map.items() if fkey in valid_prefixes]
+ if not matching_file_ids:
+ if output_json:
+ click.echo(json.dumps({"error": f"Key {key} not found in base tables"}))
+ else:
+ click.echo(click.style(f"Key {key} not found in base tables.", fg="red"))
+ return
+
+ guid_regexp = "[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
+
+ # For each reference table/column, find matching file IDs and record the references
+ for ids_table in ids_tables:
+ src_filter = f"{ids_table['table']}.{ids_table['column']}"
+
+ # Skip if src filter doesn't match (use fnmatch for wildcard patterns)
+ if src:
+ if "%" in src or "_" in src:
+ import fnmatch
+
+ # Convert SQL LIKE wildcards to fnmatch wildcards (% -> *, _ -> ?)
+ pattern = src.replace("%", "*").replace("_", "?")
+ if not fnmatch.fnmatch(src_filter, pattern):
+ continue
+ else:
+ if src_filter != src:
+ continue
+
+ if ids_table["type"] == "uuid":
+ # Direct UUID match
+ query = (
+ f"SELECT {ids_table['pk_column']}, {ids_table['column']} "
+ f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+ )
+ with db.engine.begin() as conn:
+ rs = conn.execute(sa.text(query))
+ for row in rs:
+ record_id = str(row[0])
+ ref_file_id = str(row[1])
+ if ref_file_id not in file_key_map:
+ continue
+ storage_key = file_key_map[ref_file_id]
+
+ # Apply filters
+ if file_id and ref_file_id != file_id:
+ continue
+ if key and not storage_key.endswith(key):
+ continue
+
+ # Only collect items within the requested page range
+ if offset <= total_count < offset + limit:
+ paginated_usages.append(
+ {
+ "src": f"{ids_table['table']}.{ids_table['column']}",
+ "record_id": record_id,
+ "file_id": ref_file_id,
+ "key": storage_key,
+ }
+ )
+ total_count += 1
+
+ elif ids_table["type"] in ("text", "json"):
+ # Extract UUIDs from text/json content
+ column_cast = f"{ids_table['column']}::text" if ids_table["type"] == "json" else ids_table["column"]
+ query = (
+ f"SELECT {ids_table['pk_column']}, {column_cast} "
+ f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+ )
+ with db.engine.begin() as conn:
+ rs = conn.execute(sa.text(query))
+ for row in rs:
+ record_id = str(row[0])
+ content = str(row[1])
+
+ # Find all UUIDs in the content
+ import re
+
+ uuid_pattern = re.compile(guid_regexp, re.IGNORECASE)
+ matches = uuid_pattern.findall(content)
+
+ for ref_file_id in matches:
+ if ref_file_id not in file_key_map:
+ continue
+ storage_key = file_key_map[ref_file_id]
+
+ # Apply filters
+ if file_id and ref_file_id != file_id:
+ continue
+ if key and not storage_key.endswith(key):
+ continue
+
+ # Only collect items within the requested page range
+ if offset <= total_count < offset + limit:
+ paginated_usages.append(
+ {
+ "src": f"{ids_table['table']}.{ids_table['column']}",
+ "record_id": record_id,
+ "file_id": ref_file_id,
+ "key": storage_key,
+ }
+ )
+ total_count += 1
+
+ # Output results
+ if output_json:
+ result = {
+ "total": total_count,
+ "offset": offset,
+ "limit": limit,
+ "usages": paginated_usages,
+ }
+ click.echo(json.dumps(result, indent=2))
+ else:
+ click.echo(
+ click.style(f"Found {total_count} file usages (showing {len(paginated_usages)} results)", fg="white")
+ )
+ click.echo("")
+
+ if not paginated_usages:
+ click.echo(click.style("No file usages found matching the specified criteria.", fg="yellow"))
+ return
+
+ # Print table header
+ click.echo(
+ click.style(
+ f"{'Src (Table.Column)':<50} {'Record ID':<40} {'File ID':<40} {'Storage Key':<60}",
+ fg="cyan",
+ )
+ )
+ click.echo(click.style("-" * 190, fg="white"))
+
+ # Print each usage
+ for usage in paginated_usages:
+ click.echo(f"{usage['src']:<50} {usage['record_id']:<40} {usage['file_id']:<40} {usage['key']:<60}")
+
+ # Show pagination info
+ if offset + limit < total_count:
+ click.echo("")
+ click.echo(
+ click.style(
+ f"Showing {offset + 1}-{offset + len(paginated_usages)} of {total_count} results", fg="white"
+ )
+ )
+ click.echo(click.style(f"Use --offset {offset + limit} to see next page", fg="white"))
+
+
@click.command("setup-system-tool-oauth-client", help="Setup system tool oauth client.")
@click.option("--provider", prompt=True, help="Provider name")
@click.option("--client-params", prompt=True, help="Client Params")
@@ -1899,3 +2175,79 @@ def migrate_oss(
except Exception as e:
db.session.rollback()
click.echo(click.style(f"Failed to update DB storage_type: {str(e)}", fg="red"))
+
+
+@click.command("clean-expired-messages", help="Clean expired messages.")
+@click.option(
+ "--start-from",
+ type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+ required=True,
+ help="Lower bound (inclusive) for created_at.",
+)
+@click.option(
+ "--end-before",
+ type=click.DateTime(formats=["%Y-%m-%d", "%Y-%m-%dT%H:%M:%S"]),
+ required=True,
+ help="Upper bound (exclusive) for created_at.",
+)
+@click.option("--batch-size", default=1000, show_default=True, help="Batch size for selecting messages.")
+@click.option(
+ "--graceful-period",
+ default=21,
+ show_default=True,
+ help="Graceful period in days after subscription expiration, will be ignored when billing is disabled.",
+)
+@click.option("--dry-run", is_flag=True, default=False, help="Show messages logs would be cleaned without deleting")
+def clean_expired_messages(
+ batch_size: int,
+ graceful_period: int,
+ start_from: datetime.datetime,
+ end_before: datetime.datetime,
+ dry_run: bool,
+):
+ """
+ Clean expired messages and related data for tenants based on clean policy.
+ """
+ click.echo(click.style("clean_messages: start clean messages.", fg="green"))
+
+ start_at = time.perf_counter()
+
+ try:
+ # Create policy based on billing configuration
+ # NOTE: graceful_period will be ignored when billing is disabled.
+ policy = create_message_clean_policy(graceful_period_days=graceful_period)
+
+ # Create and run the cleanup service
+ service = MessagesCleanService.from_time_range(
+ policy=policy,
+ start_from=start_from,
+ end_before=end_before,
+ batch_size=batch_size,
+ dry_run=dry_run,
+ )
+ stats = service.run()
+
+ end_at = time.perf_counter()
+ click.echo(
+ click.style(
+ f"clean_messages: completed successfully\n"
+ f" - Latency: {end_at - start_at:.2f}s\n"
+ f" - Batches processed: {stats['batches']}\n"
+ f" - Total messages scanned: {stats['total_messages']}\n"
+ f" - Messages filtered: {stats['filtered_messages']}\n"
+ f" - Messages deleted: {stats['total_deleted']}",
+ fg="green",
+ )
+ )
+ except Exception as e:
+ end_at = time.perf_counter()
+ logger.exception("clean_messages failed")
+ click.echo(
+ click.style(
+ f"clean_messages: failed after {end_at - start_at:.2f}s - {str(e)}",
+ fg="red",
+ )
+ )
+ raise
+
+ click.echo(click.style("messages cleanup completed.", fg="green"))
diff --git a/api/configs/extra/__init__.py b/api/configs/extra/__init__.py
index 4543b5389d..de97adfc0e 100644
--- a/api/configs/extra/__init__.py
+++ b/api/configs/extra/__init__.py
@@ -1,9 +1,11 @@
+from configs.extra.archive_config import ArchiveStorageConfig
from configs.extra.notion_config import NotionConfig
from configs.extra.sentry_config import SentryConfig
class ExtraServiceConfig(
# place the configs in alphabet order
+ ArchiveStorageConfig,
NotionConfig,
SentryConfig,
):
diff --git a/api/configs/extra/archive_config.py b/api/configs/extra/archive_config.py
new file mode 100644
index 0000000000..a85628fa61
--- /dev/null
+++ b/api/configs/extra/archive_config.py
@@ -0,0 +1,43 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class ArchiveStorageConfig(BaseSettings):
+ """
+ Configuration settings for workflow run logs archiving storage.
+ """
+
+ ARCHIVE_STORAGE_ENABLED: bool = Field(
+ description="Enable workflow run logs archiving to S3-compatible storage",
+ default=False,
+ )
+
+ ARCHIVE_STORAGE_ENDPOINT: str | None = Field(
+ description="URL of the S3-compatible storage endpoint (e.g., 'https://storage.example.com')",
+ default=None,
+ )
+
+ ARCHIVE_STORAGE_ARCHIVE_BUCKET: str | None = Field(
+ description="Name of the bucket to store archived workflow logs",
+ default=None,
+ )
+
+ ARCHIVE_STORAGE_EXPORT_BUCKET: str | None = Field(
+ description="Name of the bucket to store exported workflow runs",
+ default=None,
+ )
+
+ ARCHIVE_STORAGE_ACCESS_KEY: str | None = Field(
+ description="Access key ID for authenticating with storage",
+ default=None,
+ )
+
+ ARCHIVE_STORAGE_SECRET_KEY: str | None = Field(
+ description="Secret access key for authenticating with storage",
+ default=None,
+ )
+
+ ARCHIVE_STORAGE_REGION: str = Field(
+ description="Region for storage (use 'auto' if the provider supports it)",
+ default="auto",
+ )
diff --git a/api/configs/feature/__init__.py b/api/configs/feature/__init__.py
index 1ba3eb4ffb..3ee4e1699e 100644
--- a/api/configs/feature/__init__.py
+++ b/api/configs/feature/__init__.py
@@ -73,14 +73,14 @@ class AppExecutionConfig(BaseSettings):
description="Maximum allowed execution time for the application in seconds",
default=1200,
)
+ APP_DEFAULT_ACTIVE_REQUESTS: NonNegativeInt = Field(
+ description="Default number of concurrent active requests per app (0 for unlimited)",
+ default=0,
+ )
APP_MAX_ACTIVE_REQUESTS: NonNegativeInt = Field(
description="Maximum number of concurrent active requests per app (0 for unlimited)",
default=0,
)
- APP_DAILY_RATE_LIMIT: NonNegativeInt = Field(
- description="Maximum number of requests per app per day",
- default=5000,
- )
class CodeExecutionSandboxConfig(BaseSettings):
@@ -218,7 +218,7 @@ class PluginConfig(BaseSettings):
PLUGIN_DAEMON_TIMEOUT: PositiveFloat | None = Field(
description="Timeout in seconds for requests to the plugin daemon (set to None to disable)",
- default=300.0,
+ default=600.0,
)
INNER_API_KEY_FOR_PLUGIN: str = Field(description="Inner api key for plugin", default="inner-api-key")
@@ -360,6 +360,57 @@ class FileUploadConfig(BaseSettings):
default=10,
)
+ IMAGE_FILE_BATCH_LIMIT: PositiveInt = Field(
+ description="Maximum number of files allowed in a image batch upload operation",
+ default=10,
+ )
+
+ SINGLE_CHUNK_ATTACHMENT_LIMIT: PositiveInt = Field(
+ description="Maximum number of files allowed in a single chunk attachment",
+ default=10,
+ )
+
+ ATTACHMENT_IMAGE_FILE_SIZE_LIMIT: NonNegativeInt = Field(
+ description="Maximum allowed image file size for attachments in megabytes",
+ default=2,
+ )
+
+ ATTACHMENT_IMAGE_DOWNLOAD_TIMEOUT: NonNegativeInt = Field(
+ description="Timeout for downloading image attachments in seconds",
+ default=60,
+ )
+
+ # Annotation Import Security Configurations
+ ANNOTATION_IMPORT_FILE_SIZE_LIMIT: NonNegativeInt = Field(
+ description="Maximum allowed CSV file size for annotation import in megabytes",
+ default=2,
+ )
+
+ ANNOTATION_IMPORT_MAX_RECORDS: PositiveInt = Field(
+ description="Maximum number of annotation records allowed in a single import",
+ default=10000,
+ )
+
+ ANNOTATION_IMPORT_MIN_RECORDS: PositiveInt = Field(
+ description="Minimum number of annotation records required in a single import",
+ default=1,
+ )
+
+ ANNOTATION_IMPORT_RATE_LIMIT_PER_MINUTE: PositiveInt = Field(
+ description="Maximum number of annotation import requests per minute per tenant",
+ default=5,
+ )
+
+ ANNOTATION_IMPORT_RATE_LIMIT_PER_HOUR: PositiveInt = Field(
+ description="Maximum number of annotation import requests per hour per tenant",
+ default=20,
+ )
+
+ ANNOTATION_IMPORT_MAX_CONCURRENT: PositiveInt = Field(
+ description="Maximum number of concurrent annotation import tasks per tenant",
+ default=2,
+ )
+
inner_UPLOAD_FILE_EXTENSION_BLACKLIST: str = Field(
description=(
"Comma-separated list of file extensions that are blocked from upload. "
@@ -536,6 +587,11 @@ class LoggingConfig(BaseSettings):
default="INFO",
)
+ LOG_OUTPUT_FORMAT: Literal["text", "json"] = Field(
+ description="Log output format: 'text' for human-readable, 'json' for structured JSON logs.",
+ default="text",
+ )
+
LOG_FILE: str | None = Field(
description="File path for log output.",
default=None,
@@ -553,7 +609,10 @@ class LoggingConfig(BaseSettings):
LOG_FORMAT: str = Field(
description="Format string for log messages",
- default="%(asctime)s.%(msecs)03d %(levelname)s [%(threadName)s] [%(filename)s:%(lineno)d] - %(message)s",
+ default=(
+ "%(asctime)s.%(msecs)03d %(levelname)s [%(threadName)s] "
+ "[%(filename)s:%(lineno)d] %(trace_id)s - %(message)s"
+ ),
)
LOG_DATEFORMAT: str | None = Field(
@@ -890,6 +949,12 @@ class MailConfig(BaseSettings):
default=False,
)
+ SMTP_LOCAL_HOSTNAME: str | None = Field(
+ description="Override the local hostname used in SMTP HELO/EHLO. "
+ "Useful behind NAT or when the default hostname causes rejections.",
+ default=None,
+ )
+
EMAIL_SEND_IP_LIMIT_PER_MINUTE: PositiveInt = Field(
description="Maximum number of emails allowed to be sent from the same IP address in a minute",
default=50,
@@ -1042,6 +1107,10 @@ class CeleryScheduleTasksConfig(BaseSettings):
description="Enable clean messages task",
default=False,
)
+ ENABLE_WORKFLOW_RUN_CLEANUP_TASK: bool = Field(
+ description="Enable scheduled workflow run cleanup task",
+ default=False,
+ )
ENABLE_MAIL_CLEAN_DOCUMENT_NOTIFY_TASK: bool = Field(
description="Enable mail clean document notify task",
default=False,
@@ -1086,7 +1155,7 @@ class CeleryScheduleTasksConfig(BaseSettings):
)
TRIGGER_PROVIDER_CREDENTIAL_THRESHOLD_SECONDS: int = Field(
description="Proactive credential refresh threshold in seconds",
- default=180,
+ default=60 * 60,
)
TRIGGER_PROVIDER_SUBSCRIPTION_THRESHOLD_SECONDS: int = Field(
description="Proactive subscription refresh threshold in seconds",
@@ -1223,6 +1292,21 @@ class TenantIsolatedTaskQueueConfig(BaseSettings):
)
+class SandboxExpiredRecordsCleanConfig(BaseSettings):
+ SANDBOX_EXPIRED_RECORDS_CLEAN_GRACEFUL_PERIOD: NonNegativeInt = Field(
+ description="Graceful period in days for sandbox records clean after subscription expiration",
+ default=21,
+ )
+ SANDBOX_EXPIRED_RECORDS_CLEAN_BATCH_SIZE: PositiveInt = Field(
+ description="Maximum number of records to process in each batch",
+ default=1000,
+ )
+ SANDBOX_EXPIRED_RECORDS_RETENTION_DAYS: PositiveInt = Field(
+ description="Retention days for sandbox expired workflow_run records and message records",
+ default=30,
+ )
+
+
class FeatureConfig(
# place the configs in alphabet order
AppExecutionConfig,
@@ -1248,6 +1332,7 @@ class FeatureConfig(
PositionConfig,
RagEtlConfig,
RepositoryConfig,
+ SandboxExpiredRecordsCleanConfig,
SecurityConfig,
TenantIsolatedTaskQueueConfig,
ToolConfig,
diff --git a/api/configs/feature/hosted_service/__init__.py b/api/configs/feature/hosted_service/__init__.py
index 4ad30014c7..42ede718c4 100644
--- a/api/configs/feature/hosted_service/__init__.py
+++ b/api/configs/feature/hosted_service/__init__.py
@@ -8,6 +8,11 @@ class HostedCreditConfig(BaseSettings):
default="",
)
+ HOSTED_POOL_CREDITS: int = Field(
+ description="Pool credits for hosted service",
+ default=200,
+ )
+
def get_model_credits(self, model_name: str) -> int:
"""
Get credit value for a specific model name.
@@ -60,19 +65,46 @@ class HostedOpenAiConfig(BaseSettings):
HOSTED_OPENAI_TRIAL_MODELS: str = Field(
description="Comma-separated list of available models for trial access",
- default="gpt-3.5-turbo,"
- "gpt-3.5-turbo-1106,"
- "gpt-3.5-turbo-instruct,"
+ default="gpt-4,"
+ "gpt-4-turbo-preview,"
+ "gpt-4-turbo-2024-04-09,"
+ "gpt-4-1106-preview,"
+ "gpt-4-0125-preview,"
+ "gpt-4-turbo,"
+ "gpt-4.1,"
+ "gpt-4.1-2025-04-14,"
+ "gpt-4.1-mini,"
+ "gpt-4.1-mini-2025-04-14,"
+ "gpt-4.1-nano,"
+ "gpt-4.1-nano-2025-04-14,"
+ "gpt-3.5-turbo,"
"gpt-3.5-turbo-16k,"
"gpt-3.5-turbo-16k-0613,"
+ "gpt-3.5-turbo-1106,"
"gpt-3.5-turbo-0613,"
"gpt-3.5-turbo-0125,"
- "text-davinci-003",
- )
-
- HOSTED_OPENAI_QUOTA_LIMIT: NonNegativeInt = Field(
- description="Quota limit for hosted OpenAI service usage",
- default=200,
+ "gpt-3.5-turbo-instruct,"
+ "text-davinci-003,"
+ "chatgpt-4o-latest,"
+ "gpt-4o,"
+ "gpt-4o-2024-05-13,"
+ "gpt-4o-2024-08-06,"
+ "gpt-4o-2024-11-20,"
+ "gpt-4o-audio-preview,"
+ "gpt-4o-audio-preview-2025-06-03,"
+ "gpt-4o-mini,"
+ "gpt-4o-mini-2024-07-18,"
+ "o3-mini,"
+ "o3-mini-2025-01-31,"
+ "gpt-5-mini-2025-08-07,"
+ "gpt-5-mini,"
+ "o4-mini,"
+ "o4-mini-2025-04-16,"
+ "gpt-5-chat-latest,"
+ "gpt-5,"
+ "gpt-5-2025-08-07,"
+ "gpt-5-nano,"
+ "gpt-5-nano-2025-08-07",
)
HOSTED_OPENAI_PAID_ENABLED: bool = Field(
@@ -87,6 +119,13 @@ class HostedOpenAiConfig(BaseSettings):
"gpt-4-turbo-2024-04-09,"
"gpt-4-1106-preview,"
"gpt-4-0125-preview,"
+ "gpt-4-turbo,"
+ "gpt-4.1,"
+ "gpt-4.1-2025-04-14,"
+ "gpt-4.1-mini,"
+ "gpt-4.1-mini-2025-04-14,"
+ "gpt-4.1-nano,"
+ "gpt-4.1-nano-2025-04-14,"
"gpt-3.5-turbo,"
"gpt-3.5-turbo-16k,"
"gpt-3.5-turbo-16k-0613,"
@@ -94,7 +133,150 @@ class HostedOpenAiConfig(BaseSettings):
"gpt-3.5-turbo-0613,"
"gpt-3.5-turbo-0125,"
"gpt-3.5-turbo-instruct,"
- "text-davinci-003",
+ "text-davinci-003,"
+ "chatgpt-4o-latest,"
+ "gpt-4o,"
+ "gpt-4o-2024-05-13,"
+ "gpt-4o-2024-08-06,"
+ "gpt-4o-2024-11-20,"
+ "gpt-4o-audio-preview,"
+ "gpt-4o-audio-preview-2025-06-03,"
+ "gpt-4o-mini,"
+ "gpt-4o-mini-2024-07-18,"
+ "o3-mini,"
+ "o3-mini-2025-01-31,"
+ "gpt-5-mini-2025-08-07,"
+ "gpt-5-mini,"
+ "o4-mini,"
+ "o4-mini-2025-04-16,"
+ "gpt-5-chat-latest,"
+ "gpt-5,"
+ "gpt-5-2025-08-07,"
+ "gpt-5-nano,"
+ "gpt-5-nano-2025-08-07",
+ )
+
+
+class HostedGeminiConfig(BaseSettings):
+ """
+ Configuration for fetching Gemini service
+ """
+
+ HOSTED_GEMINI_API_KEY: str | None = Field(
+ description="API key for hosted Gemini service",
+ default=None,
+ )
+
+ HOSTED_GEMINI_API_BASE: str | None = Field(
+ description="Base URL for hosted Gemini API",
+ default=None,
+ )
+
+ HOSTED_GEMINI_API_ORGANIZATION: str | None = Field(
+ description="Organization ID for hosted Gemini service",
+ default=None,
+ )
+
+ HOSTED_GEMINI_TRIAL_ENABLED: bool = Field(
+ description="Enable trial access to hosted Gemini service",
+ default=False,
+ )
+
+ HOSTED_GEMINI_TRIAL_MODELS: str = Field(
+ description="Comma-separated list of available models for trial access",
+ default="gemini-2.5-flash,gemini-2.0-flash,gemini-2.0-flash-lite,",
+ )
+
+ HOSTED_GEMINI_PAID_ENABLED: bool = Field(
+ description="Enable paid access to hosted gemini service",
+ default=False,
+ )
+
+ HOSTED_GEMINI_PAID_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="gemini-2.5-flash,gemini-2.0-flash,gemini-2.0-flash-lite,",
+ )
+
+
+class HostedXAIConfig(BaseSettings):
+ """
+ Configuration for fetching XAI service
+ """
+
+ HOSTED_XAI_API_KEY: str | None = Field(
+ description="API key for hosted XAI service",
+ default=None,
+ )
+
+ HOSTED_XAI_API_BASE: str | None = Field(
+ description="Base URL for hosted XAI API",
+ default=None,
+ )
+
+ HOSTED_XAI_API_ORGANIZATION: str | None = Field(
+ description="Organization ID for hosted XAI service",
+ default=None,
+ )
+
+ HOSTED_XAI_TRIAL_ENABLED: bool = Field(
+ description="Enable trial access to hosted XAI service",
+ default=False,
+ )
+
+ HOSTED_XAI_TRIAL_MODELS: str = Field(
+ description="Comma-separated list of available models for trial access",
+ default="grok-3,grok-3-mini,grok-3-mini-fast",
+ )
+
+ HOSTED_XAI_PAID_ENABLED: bool = Field(
+ description="Enable paid access to hosted XAI service",
+ default=False,
+ )
+
+ HOSTED_XAI_PAID_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="grok-3,grok-3-mini,grok-3-mini-fast",
+ )
+
+
+class HostedDeepseekConfig(BaseSettings):
+ """
+ Configuration for fetching Deepseek service
+ """
+
+ HOSTED_DEEPSEEK_API_KEY: str | None = Field(
+ description="API key for hosted Deepseek service",
+ default=None,
+ )
+
+ HOSTED_DEEPSEEK_API_BASE: str | None = Field(
+ description="Base URL for hosted Deepseek API",
+ default=None,
+ )
+
+ HOSTED_DEEPSEEK_API_ORGANIZATION: str | None = Field(
+ description="Organization ID for hosted Deepseek service",
+ default=None,
+ )
+
+ HOSTED_DEEPSEEK_TRIAL_ENABLED: bool = Field(
+ description="Enable trial access to hosted Deepseek service",
+ default=False,
+ )
+
+ HOSTED_DEEPSEEK_TRIAL_MODELS: str = Field(
+ description="Comma-separated list of available models for trial access",
+ default="deepseek-chat,deepseek-reasoner",
+ )
+
+ HOSTED_DEEPSEEK_PAID_ENABLED: bool = Field(
+ description="Enable paid access to hosted Deepseek service",
+ default=False,
+ )
+
+ HOSTED_DEEPSEEK_PAID_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="deepseek-chat,deepseek-reasoner",
)
@@ -144,16 +326,66 @@ class HostedAnthropicConfig(BaseSettings):
default=False,
)
- HOSTED_ANTHROPIC_QUOTA_LIMIT: NonNegativeInt = Field(
- description="Quota limit for hosted Anthropic service usage",
- default=600000,
- )
-
HOSTED_ANTHROPIC_PAID_ENABLED: bool = Field(
description="Enable paid access to hosted Anthropic service",
default=False,
)
+ HOSTED_ANTHROPIC_TRIAL_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="claude-opus-4-20250514,"
+ "claude-sonnet-4-20250514,"
+ "claude-3-5-haiku-20241022,"
+ "claude-3-opus-20240229,"
+ "claude-3-7-sonnet-20250219,"
+ "claude-3-haiku-20240307",
+ )
+ HOSTED_ANTHROPIC_PAID_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="claude-opus-4-20250514,"
+ "claude-sonnet-4-20250514,"
+ "claude-3-5-haiku-20241022,"
+ "claude-3-opus-20240229,"
+ "claude-3-7-sonnet-20250219,"
+ "claude-3-haiku-20240307",
+ )
+
+
+class HostedTongyiConfig(BaseSettings):
+ """
+ Configuration for hosted Tongyi service
+ """
+
+ HOSTED_TONGYI_API_KEY: str | None = Field(
+ description="API key for hosted Tongyi service",
+ default=None,
+ )
+
+ HOSTED_TONGYI_USE_INTERNATIONAL_ENDPOINT: bool = Field(
+ description="Use international endpoint for hosted Tongyi service",
+ default=False,
+ )
+
+ HOSTED_TONGYI_TRIAL_ENABLED: bool = Field(
+ description="Enable trial access to hosted Tongyi service",
+ default=False,
+ )
+
+ HOSTED_TONGYI_PAID_ENABLED: bool = Field(
+ description="Enable paid access to hosted Anthropic service",
+ default=False,
+ )
+
+ HOSTED_TONGYI_TRIAL_MODELS: str = Field(
+ description="Comma-separated list of available models for trial access",
+ default="",
+ )
+
+ HOSTED_TONGYI_PAID_MODELS: str = Field(
+ description="Comma-separated list of available models for paid access",
+ default="",
+ )
+
class HostedMinmaxConfig(BaseSettings):
"""
@@ -246,9 +478,13 @@ class HostedServiceConfig(
HostedOpenAiConfig,
HostedSparkConfig,
HostedZhipuAIConfig,
+ HostedTongyiConfig,
# moderation
HostedModerationConfig,
# credit config
HostedCreditConfig,
+ HostedGeminiConfig,
+ HostedXAIConfig,
+ HostedDeepseekConfig,
):
pass
diff --git a/api/configs/middleware/__init__.py b/api/configs/middleware/__init__.py
index 816d0e442f..63f75924bf 100644
--- a/api/configs/middleware/__init__.py
+++ b/api/configs/middleware/__init__.py
@@ -26,6 +26,7 @@ from .vdb.clickzetta_config import ClickzettaConfig
from .vdb.couchbase_config import CouchbaseConfig
from .vdb.elasticsearch_config import ElasticsearchConfig
from .vdb.huawei_cloud_config import HuaweiCloudConfig
+from .vdb.iris_config import IrisVectorConfig
from .vdb.lindorm_config import LindormConfig
from .vdb.matrixone_config import MatrixoneConfig
from .vdb.milvus_config import MilvusConfig
@@ -105,6 +106,12 @@ class KeywordStoreConfig(BaseSettings):
class DatabaseConfig(BaseSettings):
+ # Database type selector
+ DB_TYPE: Literal["postgresql", "mysql", "oceanbase", "seekdb"] = Field(
+ description="Database type to use. OceanBase is MySQL-compatible.",
+ default="postgresql",
+ )
+
DB_HOST: str = Field(
description="Hostname or IP address of the database server.",
default="localhost",
@@ -140,10 +147,10 @@ class DatabaseConfig(BaseSettings):
default="",
)
- SQLALCHEMY_DATABASE_URI_SCHEME: str = Field(
- description="Database URI scheme for SQLAlchemy connection.",
- default="postgresql",
- )
+ @computed_field # type: ignore[prop-decorator]
+ @property
+ def SQLALCHEMY_DATABASE_URI_SCHEME(self) -> str:
+ return "postgresql" if self.DB_TYPE == "postgresql" else "mysql+pymysql"
@computed_field # type: ignore[prop-decorator]
@property
@@ -204,15 +211,15 @@ class DatabaseConfig(BaseSettings):
# Parse DB_EXTRAS for 'options'
db_extras_dict = dict(parse_qsl(self.DB_EXTRAS))
options = db_extras_dict.get("options", "")
- # Always include timezone
- timezone_opt = "-c timezone=UTC"
- if options:
- # Merge user options and timezone
- merged_options = f"{options} {timezone_opt}"
- else:
- merged_options = timezone_opt
-
- connect_args = {"options": merged_options}
+ connect_args = {}
+ # Use the dynamic SQLALCHEMY_DATABASE_URI_SCHEME property
+ if self.SQLALCHEMY_DATABASE_URI_SCHEME.startswith("postgresql"):
+ timezone_opt = "-c timezone=UTC"
+ if options:
+ merged_options = f"{options} {timezone_opt}"
+ else:
+ merged_options = timezone_opt
+ connect_args = {"options": merged_options}
return {
"pool_size": self.SQLALCHEMY_POOL_SIZE,
@@ -330,6 +337,7 @@ class MiddlewareConfig(
ChromaConfig,
ClickzettaConfig,
HuaweiCloudConfig,
+ IrisVectorConfig,
MilvusConfig,
AlibabaCloudMySQLConfig,
MyScaleConfig,
diff --git a/api/configs/middleware/storage/aliyun_oss_storage_config.py b/api/configs/middleware/storage/aliyun_oss_storage_config.py
index 331c486d54..6df14175ae 100644
--- a/api/configs/middleware/storage/aliyun_oss_storage_config.py
+++ b/api/configs/middleware/storage/aliyun_oss_storage_config.py
@@ -41,3 +41,8 @@ class AliyunOSSStorageConfig(BaseSettings):
description="Base path within the bucket to store objects (e.g., 'my-app-data/')",
default=None,
)
+
+ ALIYUN_CLOUDBOX_ID: str | None = Field(
+ description="Cloudbox id for aliyun cloudbox service",
+ default=None,
+ )
diff --git a/api/configs/middleware/storage/huawei_obs_storage_config.py b/api/configs/middleware/storage/huawei_obs_storage_config.py
index 5b5cd2f750..46b6f2e68d 100644
--- a/api/configs/middleware/storage/huawei_obs_storage_config.py
+++ b/api/configs/middleware/storage/huawei_obs_storage_config.py
@@ -26,3 +26,8 @@ class HuaweiCloudOBSStorageConfig(BaseSettings):
description="Endpoint URL for Huawei Cloud OBS (e.g., 'https://obs.cn-north-4.myhuaweicloud.com')",
default=None,
)
+
+ HUAWEI_OBS_PATH_STYLE: bool = Field(
+ description="Flag to indicate whether to use path-style URLs for OBS requests",
+ default=False,
+ )
diff --git a/api/configs/middleware/storage/tencent_cos_storage_config.py b/api/configs/middleware/storage/tencent_cos_storage_config.py
index e297e748e9..cdd10740f8 100644
--- a/api/configs/middleware/storage/tencent_cos_storage_config.py
+++ b/api/configs/middleware/storage/tencent_cos_storage_config.py
@@ -31,3 +31,8 @@ class TencentCloudCOSStorageConfig(BaseSettings):
description="Protocol scheme for COS requests: 'https' (recommended) or 'http'",
default=None,
)
+
+ TENCENT_COS_CUSTOM_DOMAIN: str | None = Field(
+ description="Tencent Cloud COS custom domain setting",
+ default=None,
+ )
diff --git a/api/configs/middleware/storage/volcengine_tos_storage_config.py b/api/configs/middleware/storage/volcengine_tos_storage_config.py
index be01f2dc36..2a35300401 100644
--- a/api/configs/middleware/storage/volcengine_tos_storage_config.py
+++ b/api/configs/middleware/storage/volcengine_tos_storage_config.py
@@ -4,7 +4,7 @@ from pydantic_settings import BaseSettings
class VolcengineTOSStorageConfig(BaseSettings):
"""
- Configuration settings for Volcengine Tinder Object Storage (TOS)
+ Configuration settings for Volcengine Torch Object Storage (TOS)
"""
VOLCENGINE_TOS_BUCKET_NAME: str | None = Field(
diff --git a/api/configs/middleware/vdb/iris_config.py b/api/configs/middleware/vdb/iris_config.py
new file mode 100644
index 0000000000..c532d191c3
--- /dev/null
+++ b/api/configs/middleware/vdb/iris_config.py
@@ -0,0 +1,91 @@
+"""Configuration for InterSystems IRIS vector database."""
+
+from pydantic import Field, PositiveInt, model_validator
+from pydantic_settings import BaseSettings
+
+
+class IrisVectorConfig(BaseSettings):
+ """Configuration settings for IRIS vector database connection and pooling."""
+
+ IRIS_HOST: str | None = Field(
+ description="Hostname or IP address of the IRIS server.",
+ default="localhost",
+ )
+
+ IRIS_SUPER_SERVER_PORT: PositiveInt | None = Field(
+ description="Port number for IRIS connection.",
+ default=1972,
+ )
+
+ IRIS_USER: str | None = Field(
+ description="Username for IRIS authentication.",
+ default="_SYSTEM",
+ )
+
+ IRIS_PASSWORD: str | None = Field(
+ description="Password for IRIS authentication.",
+ default="Dify@1234",
+ )
+
+ IRIS_SCHEMA: str | None = Field(
+ description="Schema name for IRIS tables.",
+ default="dify",
+ )
+
+ IRIS_DATABASE: str | None = Field(
+ description="Database namespace for IRIS connection.",
+ default="USER",
+ )
+
+ IRIS_CONNECTION_URL: str | None = Field(
+ description="Full connection URL for IRIS (overrides individual fields if provided).",
+ default=None,
+ )
+
+ IRIS_MIN_CONNECTION: PositiveInt = Field(
+ description="Minimum number of connections in the pool.",
+ default=1,
+ )
+
+ IRIS_MAX_CONNECTION: PositiveInt = Field(
+ description="Maximum number of connections in the pool.",
+ default=3,
+ )
+
+ IRIS_TEXT_INDEX: bool = Field(
+ description="Enable full-text search index using %iFind.Index.Basic.",
+ default=True,
+ )
+
+ IRIS_TEXT_INDEX_LANGUAGE: str = Field(
+ description="Language for full-text search index (e.g., 'en', 'ja', 'zh', 'de').",
+ default="en",
+ )
+
+ @model_validator(mode="before")
+ @classmethod
+ def validate_config(cls, values: dict) -> dict:
+ """Validate IRIS configuration values.
+
+ Args:
+ values: Configuration dictionary
+
+ Returns:
+ Validated configuration dictionary
+
+ Raises:
+ ValueError: If required fields are missing or pool settings are invalid
+ """
+ # Only validate required fields if IRIS is being used as the vector store
+ # This allows the config to be loaded even when IRIS is not in use
+
+ # vector_store = os.environ.get("VECTOR_STORE", "")
+ # We rely on Pydantic defaults for required fields if they are missing from env.
+ # Strict existence check is removed to allow defaults to work.
+
+ min_conn = values.get("IRIS_MIN_CONNECTION", 1)
+ max_conn = values.get("IRIS_MAX_CONNECTION", 3)
+ if min_conn > max_conn:
+ raise ValueError("IRIS_MIN_CONNECTION must be less than or equal to IRIS_MAX_CONNECTION")
+
+ return values
diff --git a/api/configs/middleware/vdb/milvus_config.py b/api/configs/middleware/vdb/milvus_config.py
index 05cee51cc9..eb9b0ac2ab 100644
--- a/api/configs/middleware/vdb/milvus_config.py
+++ b/api/configs/middleware/vdb/milvus_config.py
@@ -16,7 +16,6 @@ class MilvusConfig(BaseSettings):
description="Authentication token for Milvus, if token-based authentication is enabled",
default=None,
)
-
MILVUS_USER: str | None = Field(
description="Username for authenticating with Milvus, if username/password authentication is enabled",
default=None,
diff --git a/api/configs/middleware/vdb/weaviate_config.py b/api/configs/middleware/vdb/weaviate_config.py
index aa81c870f6..6f4fccaa7f 100644
--- a/api/configs/middleware/vdb/weaviate_config.py
+++ b/api/configs/middleware/vdb/weaviate_config.py
@@ -31,3 +31,8 @@ class WeaviateConfig(BaseSettings):
description="Number of objects to be processed in a single batch operation (default is 100)",
default=100,
)
+
+ WEAVIATE_TOKENIZATION: str | None = Field(
+ description="Tokenization for Weaviate (default is word)",
+ default="word",
+ )
diff --git a/api/constants/languages.py b/api/constants/languages.py
index 0312a558c9..8c1ce368ac 100644
--- a/api/constants/languages.py
+++ b/api/constants/languages.py
@@ -20,6 +20,7 @@ language_timezone_mapping = {
"sl-SI": "Europe/Ljubljana",
"th-TH": "Asia/Bangkok",
"id-ID": "Asia/Jakarta",
+ "ar-TN": "Africa/Tunis",
}
languages = list(language_timezone_mapping.keys())
diff --git a/api/constants/pipeline_templates.json b/api/constants/pipeline_templates.json
new file mode 100644
index 0000000000..32b42769e3
--- /dev/null
+++ b/api/constants/pipeline_templates.json
@@ -0,0 +1,7343 @@
+{
+ "pipeline_templates": {
+ "en-US": {
+ "pipeline_templates": [
+ {
+ "id": "9f5ea5a7-7796-49f3-9e9a-ae2d8e84cfa3",
+ "name": "General Mode-ECO",
+ "description": "In this template, the document content is divided into smaller paragraphs, known as general chunks, which are directly used for matching user queries and retrieval in Economical indexing mode.",
+ "icon": {
+ "icon_type": "image",
+ "icon": "52064ff0-26b6-47d0-902f-e331f94d959b",
+ "icon_background": null,
+ "icon_url": "data:image\/png;base64,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"
+ },
+ "copyright": "Copyright 2023 Dify",
+ "privacy_policy": "https:\/\/dify.ai\n",
+ "position": 1,
+ "chunk_structure": "text_model",
+ "language": "en-US"
+ },
+ {
+ "id": "9553b1e0-0c26-445b-9e18-063ad7eca0b4",
+ "name": "Parent-child-HQ",
+ "description": "This template uses an advanced chunking strategy that organizes document text into a hierarchical structure of larger \"parent\" chunks and smaller \"child\" chunks to balance retrieval precision and contextual richness.",
+ "icon": {
+ "icon_type": "image",
+ "icon": "ab8da246-37ba-4bbb-9b24-e7bda0778005",
+ "icon_background": null,
+ "icon_url": 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+ },
+ "copyright": "Copyright 2023 Dify",
+ "privacy_policy": "https:\/\/dify.ai\n",
+ "position": 2,
+ "chunk_structure": "hierarchical_model",
+ "language": "en-US"
+ },
+ {
+ "id": "9ef3e66a-11c7-4227-897c-3b0f9a42da1a",
+ "name": "Simple Q&A",
+ "description": "This template generates structured Q&A pairs by extracting selected columns from a table. These pairs are indexed by questions, enabling efficient retrieval of relevant answers based on query similarity.",
+ "icon": {
+ "icon_type": "image",
+ "icon": "ae0993dc-ff90-48ac-9e35-c31ebae5124b",
+ "icon_background": null,
+ "icon_url": 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+ },
+ "copyright": "Copyright 2023 Dify",
+ "privacy_policy": "https:\/\/dify.ai\n",
+ "position": 3,
+ "chunk_structure": "qa_model",
+ "language": "en-US"
+ },
+ {
+ "id": "982d1788-837a-40c8-b7de-d37b09a9b2bc",
+ "name": "Convert to Markdown",
+ "description": "This template is designed for converting native Office files such as DOCX, XLSX, and PPTX into Markdown to facilitate better information processing. PDF files are not recommended.",
+ "icon": {
+ "icon_type": "image",
+ "icon": "9d658c3a-b22f-487d-8223-db51e9012505",
+ "icon_background": null,
+ "icon_url": 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+ },
+ "copyright": "Copyright 2023 Dify",
+ "privacy_policy": "https:\/\/dify.ai\n",
+ "position": 5,
+ "chunk_structure": "hierarchical_model",
+ "language": "en-US"
+ },
+ {
+ "id": "98374ab6-9dcd-434d-983e-268bec156b43",
+ "name": "LLM Generated Q&A",
+ "description": "This template is designed to use LLM to extract key information from the input document and generate Q&A pairs indexed by questions, enabling efficient retrieval of relevant answers based on query similarity.",
+ "icon": {
+ "icon_type": "image",
+ "icon": "e4ea16ed-9690-4de9-ab80-5b622ecbcc04",
+ "icon_background": null,
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+ "privacy_policy": "https:\/\/dify.ai\n",
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name: General Mode-ECO\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1751337124089-source-1750836372241-target\n selected: false\n source: '1751337124089'\n sourceHandle: source\n target: '1750836372241'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: variable-aggregator\n targetType: tool\n id: 1753346901505-source-1751337124089-target\n selected: false\n source: '1753346901505'\n sourceHandle: source\n target: '1751337124089'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: variable-aggregator\n id: 1750836391776-source-1753346901505-target\n selected: false\n source: '1750836391776'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: document-extractor\n targetType: variable-aggregator\n id: 1753349228522-source-1753346901505-target\n selected: false\n source: '1753349228522'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1754023419266-source-1753346901505-target\n selected: false\n source: '1754023419266'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756442998557-source-1756442986174-target\n selected: false\n source: '1756442998557'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: variable-aggregator\n targetType: if-else\n id: 1756442986174-source-1756443014860-target\n selected: false\n source: '1756442986174'\n sourceHandle: source\n target: '1756443014860'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1750836380067-source-1756442986174-target\n selected: false\n source: '1750836380067'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: tool\n id: 1756443014860-true-1750836391776-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'true'\n target: '1750836391776'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: document-extractor\n id: 1756443014860-false-1753349228522-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'false'\n target: '1753349228522'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756896212061-source-1753346901505-target\n source: '1756896212061'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756907397615-source-1753346901505-target\n source: '1756907397615'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: text_model\n index_chunk_variable_selector:\n - '1751337124089'\n - result\n indexing_technique: economy\n keyword_number: 10\n retrieval_model:\n score_threshold: 0.5\n score_threshold_enabled: false\n search_method: keyword_search\n top_k: 3\n selected: false\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750836372241'\n position:\n x: 479.7628208876065\n y: 326\n positionAbsolute:\n x: 479.7628208876065\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - txt\n - markdown\n - mdx\n - pdf\n - html\n - xlsx\n - xls\n - vtt\n - properties\n - doc\n - docx\n - csv\n - eml\n - msg\n - pptx\n - xml\n - epub\n - ppt\n - md\n plugin_id: langgenius/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1750836380067'\n position:\n x: -1371.6520723158733\n y: 224.87938381325645\n positionAbsolute:\n x: -1371.6520723158733\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n documents:\n description: the documents extracted from the file\n items:\n type: object\n type: array\n images:\n description: The images extracted from the file\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n ja_JP: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n pt_BR: o arquivo a ser analisado (suporta pdf, ppt, pptx, doc, docx, png,\n jpg, jpeg)\n zh_Hans: 用于解析的文件(支持 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)\n label:\n en_US: file\n ja_JP: file\n pt_BR: file\n zh_Hans: file\n llm_description: the file to be parsed (support pdf, ppt, pptx, doc, docx,\n png, jpg, jpeg)\n max: null\n min: null\n name: file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n params:\n file: ''\n provider_id: langgenius/dify_extractor/dify_extractor\n provider_name: langgenius/dify_extractor/dify_extractor\n provider_type: builtin\n selected: false\n title: Dify Extractor\n tool_configurations: {}\n tool_description: Dify Extractor\n tool_label: Dify Extractor\n tool_name: dify_extractor\n tool_node_version: '2'\n tool_parameters:\n file:\n type: variable\n value:\n - '1756442986174'\n - output\n type: tool\n height: 52\n id: '1750836391776'\n position:\n x: -417.5334221022782\n y: 268.1692071834485\n positionAbsolute:\n x: -417.5334221022782\n y: 268.1692071834485\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 252\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n → use extractor to extract document content → split and clean content into\n structured chunks → store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1124\n height: 252\n id: '1751252161631'\n position:\n x: -1371.6520723158733\n y: -123.758428116601\n positionAbsolute:\n x: -1371.6520723158733\n y: -123.758428116601\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1124\n - data:\n author: TenTen\n desc: ''\n height: 388\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 285\n height: 388\n id: '1751252440357'\n position:\n x: -1723.9942193415582\n y: 224.87938381325645\n positionAbsolute:\n x: -1723.9942193415582\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 285\n - data:\n author: TenTen\n desc: ''\n height: 430\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n document extractor in Retrieval-Augmented Generation (RAG) is a tool or\n component that automatically identifies, extracts, and structures text and\n data from various types of documents—such as PDFs, images, scanned files,\n handwritten notes, and more—into a format that can be effectively used by\n language models within RAG Pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Dify\n Extractor\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" is\n a built-in document parser developed by Dify. It supports a wide range of\n common file formats and offers specialized handling for certain formats,\n such as \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":16,\"mode\":\"normal\",\"style\":\"\",\"text\":\".docx\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\n In addition to text extraction, it can extract images embedded within documents,\n store them, and return their accessible URLs.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 430\n id: '1751253091602'\n position:\n x: -417.5334221022782\n y: 532.832924599999\n positionAbsolute:\n x: -417.5334221022782\n y: 532.832924599999\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 265\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"General\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" divides\n content into chunks and retrieves the most relevant ones based on the user’s\n query for LLM processing. You can customize chunking rules—such as delimiter,\n maximum length, and overlap—to fit different document formats or scenarios.\n Preprocessing options are also available to clean up the text by removing\n excess spaces, URLs, and emails.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 265\n id: '1751253953926'\n position:\n x: 184.46657789772178\n y: 407.42301051148354\n positionAbsolute:\n x: 184.46657789772178\n y: 407.42301051148354\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 344\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods: \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. Economical mode\n uses keyword-based inverted indexing with no token consumption but lower\n accuracy; upgrading to High-Quality is possible, but downgrading requires\n creating a new knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 344\n id: '1751254117904'\n position:\n x: 479.7628208876065\n y: 472.46585541244207\n positionAbsolute:\n x: 479.7628208876065\n y: 472.46585541244207\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n result:\n description: The result of the general chunk tool.\n properties:\n general_chunks:\n items:\n description: The chunk of the text.\n type: string\n type: array\n type: object\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The text you want to chunk.\n ja_JP: The text you want to chunk.\n pt_BR: The text you want to chunk.\n zh_Hans: 你想要分块的文本。\n label:\n en_US: Input Content\n ja_JP: Input Content\n pt_BR: Input Content\n zh_Hans: 输入变量\n llm_description: The text you want to chunk.\n max: null\n min: null\n name: input_variable\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The delimiter of the chunks.\n ja_JP: The delimiter of the chunks.\n pt_BR: The delimiter of the chunks.\n zh_Hans: 块的分隔符。\n label:\n en_US: Delimiter\n ja_JP: Delimiter\n pt_BR: Delimiter\n zh_Hans: 分隔符\n llm_description: The delimiter of the chunks, the format of the delimiter\n must be a string.\n max: null\n min: null\n name: delimiter\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The maximum chunk length.\n ja_JP: The maximum chunk length.\n pt_BR: The maximum chunk length.\n zh_Hans: 最大块的长度。\n label:\n en_US: Maximum Chunk Length\n ja_JP: Maximum Chunk Length\n pt_BR: Maximum Chunk Length\n zh_Hans: 最大块的长度\n llm_description: The maximum chunk length, the format of the chunk size\n must be an integer.\n max: null\n min: null\n name: max_chunk_length\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The chunk overlap length.\n ja_JP: The chunk overlap length.\n pt_BR: The chunk overlap length.\n zh_Hans: 块的重叠长度。\n label:\n en_US: Chunk Overlap Length\n ja_JP: Chunk Overlap Length\n pt_BR: Chunk Overlap Length\n zh_Hans: 块的重叠长度\n llm_description: The chunk overlap length, the format of the chunk overlap\n length must be an integer.\n max: null\n min: null\n name: chunk_overlap_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: Replace consecutive spaces, newlines and tabs\n ja_JP: Replace consecutive spaces, newlines and tabs\n pt_BR: Replace consecutive spaces, newlines and tabs\n zh_Hans: 替换连续的空格、换行符和制表符\n label:\n en_US: Replace consecutive spaces, newlines and tabs\n ja_JP: Replace consecutive spaces, newlines and tabs\n pt_BR: Replace consecutive spaces, newlines and tabs\n zh_Hans: 替换连续的空格、换行符和制表符\n llm_description: Replace consecutive spaces, newlines and tabs, the format\n of the replace must be a boolean.\n max: null\n min: null\n name: replace_consecutive_spaces_newlines_tabs\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: Delete all URLs and email addresses\n ja_JP: Delete all URLs and email addresses\n pt_BR: Delete all URLs and email addresses\n zh_Hans: 删除所有URL和电子邮件地址\n label:\n en_US: Delete all URLs and email addresses\n ja_JP: Delete all URLs and email addresses\n pt_BR: Delete all URLs and email addresses\n zh_Hans: 删除所有URL和电子邮件地址\n llm_description: Delete all URLs and email addresses, the format of the\n delete must be a boolean.\n max: null\n min: null\n name: delete_all_urls_and_email_addresses\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n params:\n chunk_overlap_length: ''\n delete_all_urls_and_email_addresses: ''\n delimiter: ''\n input_variable: ''\n max_chunk_length: ''\n replace_consecutive_spaces_newlines_tabs: ''\n provider_id: langgenius/general_chunker/general_chunker\n provider_name: langgenius/general_chunker/general_chunker\n provider_type: builtin\n selected: false\n title: General Chunker\n tool_configurations: {}\n tool_description: A tool for general text chunking mode, the chunks retrieved\n and recalled are the same.\n tool_label: General Chunker\n tool_name: general_chunker\n tool_node_version: '2'\n tool_parameters:\n chunk_overlap_length:\n type: variable\n value:\n - rag\n - shared\n - Chunk_Overlap_Length\n delete_all_urls_and_email_addresses:\n type: variable\n value:\n - rag\n - shared\n - clean_2\n delimiter:\n type: mixed\n value: '{{#rag.shared.Dilmiter#}}'\n input_variable:\n type: mixed\n value: '{{#1753346901505.output#}}'\n max_chunk_length:\n type: variable\n value:\n - rag\n - shared\n - Maximum_Chunk_Length\n replace_consecutive_spaces_newlines_tabs:\n type: variable\n value:\n - rag\n - shared\n - clean_1\n type: tool\n height: 52\n id: '1751337124089'\n position:\n x: 184.46657789772178\n y: 326\n positionAbsolute:\n x: 184.46657789772178\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n output_type: string\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836391776'\n - text\n - - '1753349228522'\n - text\n - - '1754023419266'\n - content\n - - '1756896212061'\n - content\n height: 187\n id: '1753346901505'\n position:\n x: -117.24452412456148\n y: 326\n positionAbsolute:\n x: -117.24452412456148\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_array_file: false\n selected: false\n title: Doc Extractor\n type: document-extractor\n variable_selector:\n - '1756442986174'\n - output\n height: 92\n id: '1753349228522'\n position:\n x: -417.5334221022782\n y: 417.25474169825833\n positionAbsolute:\n x: -417.5334221022782\n y: 417.25474169825833\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Notion\n datasource_name: notion_datasource\n datasource_parameters: {}\n plugin_id: langgenius/notion_datasource\n provider_name: notion_datasource\n provider_type: online_document\n selected: false\n title: Notion\n type: datasource\n height: 52\n id: '1754023419266'\n position:\n x: -1369.6904698303242\n y: 440.01452302398053\n positionAbsolute:\n x: -1369.6904698303242\n y: 440.01452302398053\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n output_type: file\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836380067'\n - file\n - - '1756442998557'\n - file\n height: 135\n id: '1756442986174'\n position:\n x: -1067.06980963949\n y: 236.10252072775984\n positionAbsolute:\n x: -1067.06980963949\n y: 236.10252072775984\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Google Drive\n datasource_name: google_drive\n datasource_parameters: {}\n plugin_id: langgenius/google_drive\n provider_name: google_drive\n provider_type: online_drive\n selected: false\n title: Google Drive\n type: datasource\n height: 52\n id: '1756442998557'\n position:\n x: -1371.6520723158733\n y: 326\n positionAbsolute:\n x: -1371.6520723158733\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n cases:\n - case_id: 'true'\n conditions:\n - comparison_operator: is\n id: 1581dd11-7898-41f4-962f-937283ba7e01\n value: .xlsx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 92abb46d-d7e4-46e7-a5e1-8a29bb45d528\n value: .xls\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 1dde5ae7-754d-4e83-96b2-fe1f02995d8b\n value: .md\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 7e1a80e5-c32a-46a4-8f92-8912c64972aa\n value: .markdown\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 53abfe95-c7d0-4f63-ad37-17d425d25106\n value: .mdx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 436877b8-8c0a-4cc6-9565-92754db08571\n value: .html\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 5e3e375e-750b-4204-8ac3-9a1174a5ab7c\n value: .htm\n varType: file\n variable_selector:\n - 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rag\n - '1756896212061'\n - jina_subpages\n limit:\n type: variable\n value:\n - rag\n - '1756896212061'\n - jina_limit\n url:\n type: mixed\n value: '{{#rag.1756896212061.jina_url#}}'\n use_sitemap:\n type: variable\n value:\n - rag\n - '1756896212061'\n - jian_sitemap\n plugin_id: langgenius/jina_datasource\n provider_name: jinareader\n provider_type: website_crawl\n selected: false\n title: Jina Reader\n type: datasource\n height: 52\n id: '1756896212061'\n position:\n x: -1371.6520723158733\n y: 538.9988445953813\n positionAbsolute:\n x: -1371.6520723158733\n y: 538.9988445953813\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Firecrawl\n datasource_name: crawl\n datasource_parameters:\n crawl_subpages:\n type: variable\n value:\n - rag\n - '1756907397615'\n - firecrawl_subpages\n exclude_paths:\n type: mixed\n value: '{{#rag.1756907397615.exclude_paths#}}'\n include_paths:\n type: mixed\n value: '{{#rag.1756907397615.include_paths#}}'\n limit:\n type: variable\n value:\n - 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+ },
+ "language": "zh-Hans",
+ "position": 1
+ },
+ "9553b1e0-0c26-445b-9e18-063ad7eca0b4": {
+ "chunk_structure": "hierarchical_model",
+ "description": "This template uses an advanced chunking strategy that organizes document text into a hierarchical structure of larger \"parent\" chunks and smaller \"child\" chunks to balance retrieval precision and contextual richness.",
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false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756442998557-source-1756442986174-target\n selected: false\n source: '1756442998557'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: variable-aggregator\n targetType: if-else\n id: 1756442986174-source-1756443014860-target\n selected: false\n source: '1756442986174'\n sourceHandle: source\n target: '1756443014860'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1750836380067-source-1756442986174-target\n selected: false\n source: '1750836380067'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: tool\n id: 1756443014860-true-1750836391776-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'true'\n target: '1750836391776'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: document-extractor\n id: 1756443014860-false-1753349228522-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'false'\n target: '1753349228522'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756896212061-source-1753346901505-target\n source: '1756896212061'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756907397615-source-1753346901505-target\n source: '1756907397615'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: variable-aggregator\n targetType: tool\n id: 1753346901505-source-1756972161593-target\n source: '1753346901505'\n sourceHandle: source\n target: '1756972161593'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1756972161593-source-1750836372241-target\n source: '1756972161593'\n sourceHandle: source\n target: '1750836372241'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: hierarchical_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius/jina/jina\n index_chunk_variable_selector:\n - '1756972161593'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n reranking_enable: true\n reranking_mode: reranking_model\n reranking_model:\n reranking_model_name: jina-reranker-v1-base-en\n reranking_provider_name: langgenius/jina/jina\n score_threshold: 0\n score_threshold_enabled: false\n search_method: hybrid_search\n top_k: 3\n weights: null\n selected: false\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750836372241'\n position:\n x: 479.7628208876065\n y: 326\n positionAbsolute:\n x: 479.7628208876065\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - txt\n - markdown\n - mdx\n - pdf\n - html\n - xlsx\n - xls\n - vtt\n - properties\n - doc\n - docx\n - csv\n - eml\n - msg\n - pptx\n - xml\n - epub\n - ppt\n - md\n plugin_id: langgenius/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1750836380067'\n position:\n x: -1371.6520723158733\n y: 224.87938381325645\n positionAbsolute:\n x: -1371.6520723158733\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n documents:\n description: the documents extracted from the file\n items:\n type: object\n type: array\n images:\n description: The images extracted from the file\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n ja_JP: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n pt_BR: o arquivo a ser analisado (suporta pdf, ppt, pptx, doc, docx, png,\n jpg, jpeg)\n zh_Hans: 用于解析的文件(支持 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)\n label:\n en_US: file\n ja_JP: file\n pt_BR: file\n zh_Hans: file\n llm_description: the file to be parsed (support pdf, ppt, pptx, doc, docx,\n png, jpg, jpeg)\n max: null\n min: null\n name: file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n params:\n file: ''\n provider_id: langgenius/dify_extractor/dify_extractor\n provider_name: langgenius/dify_extractor/dify_extractor\n provider_type: builtin\n selected: false\n title: Dify Extractor\n tool_configurations: {}\n tool_description: Dify Extractor\n tool_label: Dify Extractor\n tool_name: dify_extractor\n tool_node_version: '2'\n tool_parameters:\n file:\n type: variable\n value:\n - '1756442986174'\n - output\n type: tool\n height: 52\n id: '1750836391776'\n position:\n x: -417.5334221022782\n y: 268.1692071834485\n positionAbsolute:\n x: -417.5334221022782\n y: 268.1692071834485\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 252\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n → use extractor to extract document content → split and clean content into\n structured chunks → store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1124\n height: 252\n id: '1751252161631'\n position:\n x: -1371.6520723158733\n y: -123.758428116601\n positionAbsolute:\n x: -1371.6520723158733\n y: -123.758428116601\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1124\n - data:\n author: TenTen\n desc: ''\n height: 388\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 285\n height: 388\n id: '1751252440357'\n position:\n x: -1723.9942193415582\n y: 224.87938381325645\n positionAbsolute:\n x: -1723.9942193415582\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 285\n - data:\n author: TenTen\n desc: ''\n height: 430\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n document extractor in Retrieval-Augmented Generation (RAG) is a tool or\n component that automatically identifies, extracts, and structures text and\n data from various types of documents—such as PDFs, images, scanned files,\n handwritten notes, and more—into a format that can be effectively used by\n language models within RAG Pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Dify\n Extractor\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" is\n a built-in document parser developed by Dify. It supports a wide range of\n common file formats and offers specialized handling for certain formats,\n such as \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":16,\"mode\":\"normal\",\"style\":\"\",\"text\":\".docx\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\n In addition to text extraction, it can extract images embedded within documents,\n store them, and return their accessible URLs.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 430\n id: '1751253091602'\n position:\n x: -417.5334221022782\n y: 547.4103414077279\n positionAbsolute:\n x: -417.5334221022782\n y: 547.4103414077279\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 638\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Parent-Child\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n addresses the dilemma of context and precision by leveraging a two-tier\n hierarchical approach that effectively balances the trade-off between accurate\n matching and comprehensive contextual information in RAG systems. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Here\n is the essential mechanism of this structured, two-level information access:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Query Matching with Child Chunks: Small, focused pieces of information,\n often as concise as a single sentence within a paragraph, are used to match\n the user''s query. These child chunks enable precise and relevant initial\n retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Contextual Enrichment with Parent Chunks: Larger, encompassing sections—such\n as a paragraph, a section, or even an entire document—that include the matched\n child chunks are then retrieved. These parent chunks provide comprehensive\n context for the Language Model (LLM). length, and overlap—to fit different\n document formats or scenarios. Preprocessing options are also available\n to clean up the text by removing excess spaces, URLs, and emails.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 638\n id: '1751253953926'\n position:\n x: 184.46657789772178\n y: 407.42301051148354\n positionAbsolute:\n x: 184.46657789772178\n y: 407.42301051148354\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 410\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods: \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. Economical mode\n uses keyword-based inverted indexing with no token consumption but lower\n accuracy; upgrading to High-Quality is possible, but downgrading requires\n creating a new knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"*\n Parent-Child Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q&A\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" only\n support the \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" indexing\n method.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 410\n id: '1751254117904'\n position:\n x: 479.7628208876065\n y: 472.46585541244207\n positionAbsolute:\n x: 479.7628208876065\n y: 472.46585541244207\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n output_type: string\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836391776'\n - text\n - - '1753349228522'\n - text\n - - '1754023419266'\n - content\n - - '1756896212061'\n - content\n - - '1756907397615'\n - content\n height: 213\n id: '1753346901505'\n position:\n x: -117.24452412456148\n y: 326\n positionAbsolute:\n x: -117.24452412456148\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_array_file: false\n selected: false\n title: Doc Extractor\n type: document-extractor\n variable_selector:\n - '1756442986174'\n - output\n height: 92\n id: '1753349228522'\n position:\n x: -417.5334221022782\n y: 417.25474169825833\n positionAbsolute:\n x: -417.5334221022782\n y: 417.25474169825833\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Notion\n datasource_name: notion_datasource\n datasource_parameters: {}\n plugin_id: langgenius/notion_datasource\n provider_name: notion_datasource\n provider_type: online_document\n selected: false\n title: Notion\n type: datasource\n height: 52\n id: '1754023419266'\n position:\n x: -1369.6904698303242\n y: 440.01452302398053\n positionAbsolute:\n x: -1369.6904698303242\n y: 440.01452302398053\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n output_type: file\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836380067'\n - file\n - - '1756442998557'\n - file\n height: 135\n id: '1756442986174'\n position:\n x: -1054.415447856335\n y: 236.10252072775984\n positionAbsolute:\n x: -1054.415447856335\n y: 236.10252072775984\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Google Drive\n datasource_name: google_drive\n datasource_parameters: {}\n plugin_id: langgenius/google_drive\n provider_name: google_drive\n provider_type: online_drive\n selected: false\n title: Google Drive\n type: datasource\n height: 52\n id: '1756442998557'\n position:\n x: -1371.6520723158733\n y: 326\n positionAbsolute:\n x: -1371.6520723158733\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n cases:\n - case_id: 'true'\n conditions:\n - comparison_operator: is\n id: 1581dd11-7898-41f4-962f-937283ba7e01\n value: .xlsx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 92abb46d-d7e4-46e7-a5e1-8a29bb45d528\n value: .xls\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 1dde5ae7-754d-4e83-96b2-fe1f02995d8b\n value: .md\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 7e1a80e5-c32a-46a4-8f92-8912c64972aa\n value: .markdown\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 53abfe95-c7d0-4f63-ad37-17d425d25106\n value: .mdx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 436877b8-8c0a-4cc6-9565-92754db08571\n value: .html\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - 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rag\n - '1756907397615'\n - firecrawl_subpages\n exclude_paths:\n type: mixed\n value: '{{#rag.1756907397615.exclude_paths#}}'\n include_paths:\n type: mixed\n value: '{{#rag.1756907397615.include_paths#}}'\n limit:\n type: variable\n value:\n - rag\n - '1756907397615'\n - max_pages\n max_depth:\n type: variable\n value:\n - rag\n - '1756907397615'\n - max_depth\n only_main_content:\n type: variable\n value:\n - rag\n - '1756907397615'\n - main_content\n url:\n type: mixed\n value: '{{#rag.1756907397615.firecrawl_url1#}}'\n plugin_id: langgenius/firecrawl_datasource\n provider_name: firecrawl\n provider_type: website_crawl\n selected: false\n title: Firecrawl\n type: datasource\n height: 52\n id: '1756907397615'\n position:\n x: -1371.6520723158733\n y: 644.3296146102903\n positionAbsolute:\n x: -1371.6520723158733\n y: 644.3296146102903\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The text you want to chunk.\n ja_JP: The text you want to chunk.\n pt_BR: Conteúdo de Entrada\n zh_Hans: 输入文本\n label:\n en_US: Input Content\n ja_JP: Input Content\n pt_BR: Conteúdo de Entrada\n zh_Hans: 输入文本\n llm_description: The text you want to chunk.\n max: null\n min: null\n name: input_text\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: paragraph\n form: llm\n human_description:\n en_US: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n ja_JP: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n pt_BR: Dividir texto em parágrafos com base no separador e no comprimento\n máximo do bloco, usando o texto dividido como bloco pai ou documento\n completo como bloco pai e diretamente recuperá-lo.\n zh_Hans: 根据分隔符和最大块长度将文本拆分为段落,使用拆分文本作为检索的父块或整个文档用作父块并直接检索。\n label:\n en_US: Parent Mode\n ja_JP: Parent Mode\n pt_BR: Modo Pai\n zh_Hans: 父块模式\n llm_description: Split text into paragraphs based on separator and maximum\n chunk length, using split text as parent block or entire document as parent\n block and directly retrieve.\n max: null\n min: null\n name: parent_mode\n options:\n - 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'\n form: llm\n human_description:\n en_US: Separator used for subchunking\n ja_JP: Separator used for subchunking\n pt_BR: Separador usado para subdivisão\n zh_Hans: 用于子分块的分隔符\n label:\n en_US: Child Delimiter\n ja_JP: Child Delimiter\n pt_BR: Separador de Subdivisão\n zh_Hans: 子分块分隔符\n llm_description: The separator used to split subchunks\n max: null\n min: null\n name: subchunk_separator\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 512\n form: llm\n human_description:\n en_US: Maximum length for subchunking\n ja_JP: Maximum length for subchunking\n pt_BR: Comprimento máximo para subdivisão\n zh_Hans: 用于子分块的最大长度\n label:\n en_US: Maximum Child Chunk Length\n ja_JP: Maximum Child Chunk Length\n pt_BR: Comprimento Máximo de Subdivisão\n zh_Hans: 子分块最大长度\n llm_description: Maximum length allowed per subchunk\n max: null\n min: null\n name: subchunk_max_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - 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"
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+ "icon_type": "image",
+ "icon_url": 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"
+ },
+ "language": "zh-Hans",
+ "position": 2
+ },
+ "9ef3e66a-11c7-4227-897c-3b0f9a42da1a": {
+ "chunk_structure": "qa_model",
+ "description": "This template generates structured Q&A pairs by extracting selected columns from a table. These pairs are indexed by questions, enabling efficient retrieval of relevant answers based on query similarity.",
+ "export_data": "dependencies:\n- current_identifier: null\n type: marketplace\n value:\n marketplace_plugin_unique_identifier: langgenius/qa_chunk:0.0.8@1fed9644646bdd48792cdf5a1d559a3df336bd3a8edb0807227499fb56dce3af\n version: null\n- current_identifier: null\n type: marketplace\n value:\n marketplace_plugin_unique_identifier: langgenius/jina:0.0.8@d3a6766fbb80890d73fea7ea04803f3e1702c6e6bd621aafb492b86222a193dd\n version: null\nkind: rag_pipeline\nrag_pipeline:\n description: ''\n icon: 769900fc-8a31-4584-94f6-f227357c00c8\n icon_background: null\n icon_type: image\n icon_url: data:image/png;base64,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name: Simple Q&A\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: datasource\n targetType: tool\n id: 1750836380067-source-1753253430271-target\n source: '1750836380067'\n sourceHandle: source\n target: '1753253430271'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1753253430271-source-1750836372241-target\n source: '1753253430271'\n sourceHandle: source\n target: '1750836372241'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: qa_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius/jina/jina\n index_chunk_variable_selector:\n - '1753253430271'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n reranking_enable: false\n reranking_mode: reranking_model\n reranking_model:\n reranking_model_name: null\n reranking_provider_name: null\n score_threshold: 0\n score_threshold_enabled: false\n search_method: semantic_search\n top_k: 3\n weights: null\n selected: true\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750836372241'\n position:\n x: 160\n y: 326\n positionAbsolute:\n x: 160\n y: 326\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - csv\n plugin_id: langgenius/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1750836380067'\n position:\n x: -714.4192784522008\n y: 326\n positionAbsolute:\n x: -714.4192784522008\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 249\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n → use extractor to extract document content → split and clean content into\n structured chunks → store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\"},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1115\n height: 249\n id: '1751252161631'\n position:\n x: -714.4192784522008\n y: -19.94142868660783\n positionAbsolute:\n x: -714.4192784522008\n y: -19.94142868660783\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1115\n - data:\n author: TenTen\n desc: ''\n height: 281\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. 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+ "properties": {
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+ "ja_JP": "The file you want to extract QA from.",
+ "pt_BR": "The file you want to extract QA from.",
+ "zh_Hans": "你想要提取 QA 的文件。"
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+ "ja_JP": "Input File",
+ "pt_BR": "Input File",
+ "zh_Hans": "输入文件"
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+ "ja_JP": "Column number for question",
+ "pt_BR": "Column number for question",
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+ "ja_JP": "Column number for answer.",
+ "pt_BR": "Column number for answer.",
+ "zh_Hans": "答案所在的列。"
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+ "ja_JP": "Column number for answer",
+ "pt_BR": "Column number for answer",
+ "zh_Hans": "答案所在的列"
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+ "tool_description": "A tool for QA chunking mode.",
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+ "name": "Simple Q&A",
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+ "language": "zh-Hans",
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+ },
+ "982d1788-837a-40c8-b7de-d37b09a9b2bc": {
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+ "description": "This template is designed for converting native Office files such as DOCX, XLSX, and PPTX into Markdown to facilitate better information processing. PDF files are not recommended.",
+ "export_data": "dependencies:\n- current_identifier: null\n type: marketplace\n value:\n marketplace_plugin_unique_identifier: langgenius/jina:0.0.8@d3a6766fbb80890d73fea7ea04803f3e1702c6e6bd621aafb492b86222a193dd\n- current_identifier: null\n type: marketplace\n value:\n marketplace_plugin_unique_identifier: langgenius/parentchild_chunker:0.0.7@ee9c253e7942436b4de0318200af97d98d094262f3c1a56edbe29dcb01fbc158\n- current_identifier: null\n type: marketplace\n value:\n marketplace_plugin_unique_identifier: yevanchen/markitdown:0.0.4@776b3e2e930e2ffd28a75bb20fecbe7a020849cf754f86e604acacf1258877f6\nkind: rag_pipeline\nrag_pipeline:\n description: ''\n icon: 9d658c3a-b22f-487d-8223-db51e9012505\n icon_background: null\n icon_type: image\n icon_url: data:image/png;base64,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name: Convert to Markdown\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1751336942081-source-1750400198569-target\n selected: false\n source: '1751336942081'\n sourceHandle: source\n target: '1750400198569'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: tool\n id: 1750400203722-source-1751359716720-target\n selected: false\n source: '1750400203722'\n sourceHandle: source\n target: '1751359716720'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: tool\n id: 1751359716720-source-1751336942081-target\n source: '1751359716720'\n sourceHandle: source\n target: '1751336942081'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: hierarchical_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius/jina/jina\n index_chunk_variable_selector:\n - '1751336942081'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n hybridSearchMode: weighted_score\n score_threshold: 0.5\n score_threshold_enabled: false\n search_method: hybrid_search\n top_k: 3\n vector_setting:\n embedding_model_name: jina-embeddings-v2-base-en\n embedding_provider_name: langgenius/jina/jina\n selected: true\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750400198569'\n position:\n x: 357.7591396590142\n y: 282\n positionAbsolute:\n x: 357.7591396590142\n y: 282\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - html\n - xlsx\n - xls\n - doc\n - docx\n - csv\n - pptx\n - xml\n - ppt\n - txt\n plugin_id: langgenius/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1750400203722'\n position:\n x: -580.684520226929\n y: 282\n positionAbsolute:\n x: -580.684520226929\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 316\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 374\n height: 316\n id: '1751264451381'\n position:\n x: -1034.2054006208518\n y: 282\n positionAbsolute:\n x: -1034.2054006208518\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 374\n - data:\n author: TenTen\n desc: ''\n height: 260\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n → use extractor to extract document content → split and clean content into\n structured chunks → store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\"},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1182\n height: 260\n id: '1751266376760'\n position:\n x: -580.684520226929\n y: -21.891401375096322\n positionAbsolute:\n x: -580.684520226929\n y: -21.891401375096322\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1182\n - data:\n author: TenTen\n desc: ''\n height: 417\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n document extractor in Retrieval-Augmented Generation (RAG) is a tool or\n component that automatically identifies, extracts, and structures text and\n data from various types of documents—such as PDFs, images, scanned files,\n handwritten notes, and more—into a format that can be effectively used by\n language models within RAG Pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Markitdown\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n serves as an excellent alternative to traditional document extraction nodes,\n offering robust file conversion capabilities within the Dify ecosystem.\n It leverages MarkItDown''s plugin-based architecture to provide seamless\n conversion of multiple file formats to Markdown.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 241\n height: 417\n id: '1751266402561'\n position:\n x: -266.96080929383595\n y: 372.64040589639495\n positionAbsolute:\n x: -266.96080929383595\n y: 372.64040589639495\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 241\n - 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These child chunks enable precise and relevant initial\n retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Contextual Enrichment with Parent Chunks: Larger, encompassing sections—such\n as a paragraph, a section, or even an entire document—that include the matched\n child chunks are then retrieved. These parent chunks provide comprehensive\n context for the Language Model (LLM).\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 554\n id: '1751266447821'\n position:\n x: 37.74090119950054\n y: 372.64040589639495\n positionAbsolute:\n x: 37.74090119950054\n y: 372.64040589639495\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 411\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods: \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. 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+ "pt_BR": "Conteúdo de Entrada",
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+ "ja_JP": "Split text into paragraphs based on separator and maximum chunk length, using split text as parent block or entire document as parent block and directly retrieve.",
+ "pt_BR": "Dividir texto em parágrafos com base no separador e no comprimento máximo do bloco, usando o texto dividido como bloco pai ou documento completo como bloco pai e diretamente recuperá-lo.",
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+ "ja_JP": "Parent Mode",
+ "pt_BR": "Modo Pai",
+ "zh_Hans": "父块模式"
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+ "llm_description": "Split text into paragraphs based on separator and maximum chunk length, using split text as parent block or entire document as parent block and directly retrieve.",
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+ "name": "parent_mode",
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+ "pt_BR": "Parágrafo",
+ "zh_Hans": "段落"
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+ "value": "paragraph"
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+ {
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+ "ja_JP": "Full Document",
+ "pt_BR": "Documento Completo",
+ "zh_Hans": "全文"
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+ "value": "full_doc"
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+ "ja_JP": "Separator used for chunking",
+ "pt_BR": "Separador usado para divisão",
+ "zh_Hans": "用于分块的分隔符"
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+ "label": {
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+ "ja_JP": "Parent Delimiter",
+ "pt_BR": "Separador de Pai",
+ "zh_Hans": "父块分隔符"
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+ "llm_description": "The separator used to split chunks",
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+ "ja_JP": "Maximum length for chunking",
+ "pt_BR": "Comprimento máximo para divisão",
+ "zh_Hans": "用于分块的最大长度"
+ },
+ "label": {
+ "en_US": "Maximum Parent Chunk Length",
+ "ja_JP": "Maximum Parent Chunk Length",
+ "pt_BR": "Comprimento Máximo do Bloco Pai",
+ "zh_Hans": "最大父块长度"
+ },
+ "llm_description": "Maximum length allowed per chunk",
+ "max": null,
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+ "default": ". ",
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+ "en_US": "Separator used for subchunking",
+ "ja_JP": "Separator used for subchunking",
+ "pt_BR": "Separador usado para subdivisão",
+ "zh_Hans": "用于子分块的分隔符"
+ },
+ "label": {
+ "en_US": "Child Delimiter",
+ "ja_JP": "Child Delimiter",
+ "pt_BR": "Separador de Subdivisão",
+ "zh_Hans": "子分块分隔符"
+ },
+ "llm_description": "The separator used to split subchunks",
+ "max": null,
+ "min": null,
+ "name": "subchunk_separator",
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+ "precision": null,
+ "required": false,
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+ "type": "string"
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+ {
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+ "default": 512,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Maximum length for subchunking",
+ "ja_JP": "Maximum length for subchunking",
+ "pt_BR": "Comprimento máximo para subdivisão",
+ "zh_Hans": "用于子分块的最大长度"
+ },
+ "label": {
+ "en_US": "Maximum Child Chunk Length",
+ "ja_JP": "Maximum Child Chunk Length",
+ "pt_BR": "Comprimento Máximo de Subdivisão",
+ "zh_Hans": "子分块最大长度"
+ },
+ "llm_description": "Maximum length allowed per subchunk",
+ "max": null,
+ "min": null,
+ "name": "subchunk_max_length",
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+ "precision": null,
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+ "type": "number"
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+ {
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+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove consecutive spaces, newlines and tabs",
+ "ja_JP": "Whether to remove consecutive spaces, newlines and tabs",
+ "pt_BR": "Se deve remover espaços extras no texto",
+ "zh_Hans": "是否移除文本中的连续空格、换行符和制表符"
+ },
+ "label": {
+ "en_US": "Replace consecutive spaces, newlines and tabs",
+ "ja_JP": "Replace consecutive spaces, newlines and tabs",
+ "pt_BR": "Substituir espaços consecutivos, novas linhas e guias",
+ "zh_Hans": "替换连续空格、换行符和制表符"
+ },
+ "llm_description": "Whether to remove consecutive spaces, newlines and tabs",
+ "max": null,
+ "min": null,
+ "name": "remove_extra_spaces",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
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+ "type": "boolean"
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+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove URLs and emails in the text",
+ "ja_JP": "Whether to remove URLs and emails in the text",
+ "pt_BR": "Se deve remover URLs e e-mails no texto",
+ "zh_Hans": "是否移除文本中的URL和电子邮件地址"
+ },
+ "label": {
+ "en_US": "Delete all URLs and email addresses",
+ "ja_JP": "Delete all URLs and email addresses",
+ "pt_BR": "Remover todas as URLs e e-mails",
+ "zh_Hans": "删除所有URL和电子邮件地址"
+ },
+ "llm_description": "Whether to remove URLs and emails in the text",
+ "max": null,
+ "min": null,
+ "name": "remove_urls_emails",
+ "options": [],
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+ "precision": null,
+ "required": false,
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+ "type": "boolean"
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+ ],
+ "params": {
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+ "max_length": "",
+ "parent_mode": "",
+ "remove_extra_spaces": "",
+ "remove_urls_emails": "",
+ "separator": "",
+ "subchunk_max_length": "",
+ "subchunk_separator": ""
+ },
+ "provider_id": "langgenius/parentchild_chunker/parentchild_chunker",
+ "provider_name": "langgenius/parentchild_chunker/parentchild_chunker",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Parent-child Chunker",
+ "tool_configurations": {},
+ "tool_description": "Process documents into parent-child chunk structures",
+ "tool_label": "Parent-child Chunker",
+ "tool_name": "parentchild_chunker",
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+ },
+ "language": "zh-Hans",
+ "position": 4
+ },
+ "98374ab6-9dcd-434d-983e-268bec156b43": {
+ "chunk_structure": "qa_model",
+ "description": "This template is designed to use LLM to extract key information from the input document and generate Q&A pairs indexed by questions, enabling efficient retrieval of relevant answers based on query similarity.",
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name: LLM Generated Q&A\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInLoop: false\n sourceType: tool\n targetType: variable-aggregator\n id: 1750836391776-source-1753346901505-target\n selected: false\n source: '1750836391776'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: document-extractor\n targetType: variable-aggregator\n id: 1753349228522-source-1753346901505-target\n selected: false\n source: '1753349228522'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1754023419266-source-1753346901505-target\n selected: false\n source: '1754023419266'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756442998557-source-1756442986174-target\n selected: false\n source: '1756442998557'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: variable-aggregator\n targetType: if-else\n id: 1756442986174-source-1756443014860-target\n selected: false\n source: '1756442986174'\n sourceHandle: source\n target: '1756443014860'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1750836380067-source-1756442986174-target\n selected: false\n source: '1750836380067'\n sourceHandle: source\n target: '1756442986174'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: tool\n id: 1756443014860-true-1750836391776-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'true'\n target: '1750836391776'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: if-else\n targetType: document-extractor\n id: 1756443014860-false-1753349228522-target\n selected: false\n source: '1756443014860'\n sourceHandle: 'false'\n target: '1753349228522'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756896212061-source-1753346901505-target\n source: '1756896212061'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: variable-aggregator\n id: 1756907397615-source-1753346901505-target\n source: '1756907397615'\n sourceHandle: source\n target: '1753346901505'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: variable-aggregator\n targetType: llm\n id: 1753346901505-source-1756912504019-target\n source: '1753346901505'\n sourceHandle: source\n target: '1756912504019'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: llm\n targetType: tool\n id: 1756912504019-source-1756912537172-target\n source: '1756912504019'\n sourceHandle: source\n target: '1756912537172'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: tool\n id: 1756912537172-source-1756912274158-target\n source: '1756912537172'\n sourceHandle: source\n target: '1756912274158'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1756912274158-source-1750836372241-target\n source: '1756912274158'\n sourceHandle: source\n target: '1750836372241'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: qa_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius/jina/jina\n index_chunk_variable_selector:\n - '1756912274158'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n hybridSearchMode: weighted_score\n reranking_enable: false\n score_threshold: 0.5\n score_threshold_enabled: false\n search_method: semantic_search\n top_k: 3\n vector_setting:\n embedding_model_name: jina-embeddings-v2-base-en\n embedding_provider_name: langgenius/jina/jina\n selected: false\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750836372241'\n position:\n x: 1150.8369138826617\n y: 326\n positionAbsolute:\n x: 1150.8369138826617\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - txt\n - markdown\n - mdx\n - pdf\n - html\n - xlsx\n - xls\n - vtt\n - properties\n - doc\n - docx\n - csv\n - eml\n - msg\n - pptx\n - xml\n - epub\n - ppt\n - md\n plugin_id: langgenius/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1750836380067'\n position:\n x: -1371.6520723158733\n y: 224.87938381325645\n positionAbsolute:\n x: -1371.6520723158733\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n documents:\n description: the documents extracted from the file\n items:\n type: object\n type: array\n images:\n description: The images extracted from the file\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n ja_JP: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n pt_BR: o arquivo a ser analisado (suporta pdf, ppt, pptx, doc, docx, png,\n jpg, jpeg)\n zh_Hans: 用于解析的文件(支持 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)\n label:\n en_US: file\n ja_JP: file\n pt_BR: file\n zh_Hans: file\n llm_description: the file to be parsed (support pdf, ppt, pptx, doc, docx,\n png, jpg, jpeg)\n max: null\n min: null\n name: file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n params:\n file: ''\n provider_id: langgenius/dify_extractor/dify_extractor\n provider_name: langgenius/dify_extractor/dify_extractor\n provider_type: builtin\n selected: false\n title: Dify Extractor\n tool_configurations: {}\n tool_description: Dify Extractor\n tool_label: Dify Extractor\n tool_name: dify_extractor\n tool_node_version: '2'\n tool_parameters:\n file:\n type: variable\n value:\n - '1756442986174'\n - output\n type: tool\n height: 52\n id: '1750836391776'\n position:\n x: -417.5334221022782\n y: 268.1692071834485\n positionAbsolute:\n x: -417.5334221022782\n y: 268.1692071834485\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 252\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n → use extractor to extract document content → split and clean content into\n structured chunks → store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https://docs.dify.ai/en/guides/knowledge-base/knowledge-pipeline/knowledge-pipeline-orchestration\"},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1124\n height: 252\n id: '1751252161631'\n position:\n x: -1371.6520723158733\n y: -123.758428116601\n positionAbsolute:\n x: -1371.6520723158733\n y: -123.758428116601\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1124\n - data:\n author: TenTen\n desc: ''\n height: 388\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 285\n height: 388\n id: '1751252440357'\n position:\n x: -1723.9942193415582\n y: 224.87938381325645\n positionAbsolute:\n x: -1723.9942193415582\n y: 224.87938381325645\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 285\n - data:\n author: TenTen\n desc: ''\n height: 430\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n document extractor in Retrieval-Augmented Generation (RAG) is a tool or\n component that automatically identifies, extracts, and structures text and\n data from various types of documents—such as PDFs, images, scanned files,\n handwritten notes, and more—into a format that can be effectively used by\n language models within RAG Pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Dify\n Extractor\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" is\n a built-in document parser developed by Dify. It supports a wide range of\n common file formats and offers specialized handling for certain formats,\n such as \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":16,\"mode\":\"normal\",\"style\":\"\",\"text\":\".docx\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\n In addition to text extraction, it can extract images embedded within documents,\n store them, and return their accessible URLs.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 430\n id: '1751253091602'\n position:\n x: -417.5334221022782\n y: 546.5283142529594\n positionAbsolute:\n x: -417.5334221022782\n y: 546.5283142529594\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 336\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q&A\n Processor\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" extracts\n specified columns from tables to generate structured Q&A pairs. Users can\n independently designate which columns to use for questions and which for\n answers.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"These\n pairs are indexed by the question field, so user queries are matched directly\n against the questions to retrieve the corresponding answers. This \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q-to-Q\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" matching\n strategy improves clarity and precision, especially in scenarios involving\n high-frequency or highly similar user questions.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 336\n id: '1751253953926'\n position:\n x: 794.2003154321724\n y: 417.25474169825833\n positionAbsolute:\n x: 794.2003154321724\n y: 417.25474169825833\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 410\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods: \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. Economical mode\n uses keyword-based inverted indexing with no token consumption but lower\n accuracy; upgrading to High-Quality is possible, but downgrading requires\n creating a new knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"*\n Parent-Child Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" and \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q&A\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" only\n support the \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\" indexing\n method.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 410\n id: '1751254117904'\n position:\n x: 1150.8369138826617\n y: 475.88970282568215\n positionAbsolute:\n x: 1150.8369138826617\n y: 475.88970282568215\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n output_type: string\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836391776'\n - text\n - - '1753349228522'\n - text\n - - '1754023419266'\n - content\n - - '1756896212061'\n - content\n height: 187\n id: '1753346901505'\n position:\n x: -117.24452412456148\n y: 326\n positionAbsolute:\n x: -117.24452412456148\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_array_file: false\n selected: false\n title: Doc Extractor\n type: document-extractor\n variable_selector:\n - '1756442986174'\n - output\n height: 92\n id: '1753349228522'\n position:\n x: -417.5334221022782\n y: 417.25474169825833\n positionAbsolute:\n x: -417.5334221022782\n y: 417.25474169825833\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Notion\n datasource_name: notion_datasource\n datasource_parameters: {}\n plugin_id: langgenius/notion_datasource\n provider_name: notion_datasource\n provider_type: online_document\n selected: false\n title: Notion\n type: datasource\n height: 52\n id: '1754023419266'\n position:\n x: -1369.6904698303242\n y: 440.01452302398053\n positionAbsolute:\n x: -1369.6904698303242\n y: 440.01452302398053\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n output_type: file\n selected: false\n title: Variable Aggregator\n type: variable-aggregator\n variables:\n - - '1750836380067'\n - file\n - - '1756442998557'\n - file\n height: 135\n id: '1756442986174'\n position:\n x: -1067.06980963949\n y: 236.10252072775984\n positionAbsolute:\n x: -1067.06980963949\n y: 236.10252072775984\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Google Drive\n datasource_name: google_drive\n datasource_parameters: {}\n plugin_id: langgenius/google_drive\n provider_name: google_drive\n provider_type: online_drive\n selected: false\n title: Google Drive\n type: datasource\n height: 52\n id: '1756442998557'\n position:\n x: -1371.6520723158733\n y: 326\n positionAbsolute:\n x: -1371.6520723158733\n y: 326\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n cases:\n - case_id: 'true'\n conditions:\n - comparison_operator: is\n id: 1581dd11-7898-41f4-962f-937283ba7e01\n value: .xlsx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 92abb46d-d7e4-46e7-a5e1-8a29bb45d528\n value: .xls\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 1dde5ae7-754d-4e83-96b2-fe1f02995d8b\n value: .md\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 7e1a80e5-c32a-46a4-8f92-8912c64972aa\n value: .markdown\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 53abfe95-c7d0-4f63-ad37-17d425d25106\n value: .mdx\n varType: string\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 436877b8-8c0a-4cc6-9565-92754db08571\n value: .html\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 5e3e375e-750b-4204-8ac3-9a1174a5ab7c\n value: .htm\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 1a84a784-a797-4f96-98a0-33a9b48ceb2b\n value: .docx\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 62d11445-876a-493f-85d3-8fc020146bdd\n value: .csv\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n - comparison_operator: is\n id: 02c4bce8-7668-4ccd-b750-4281f314b231\n value: .txt\n varType: file\n variable_selector:\n - '1756442986174'\n - output\n - extension\n id: 'true'\n logical_operator: or\n selected: false\n title: IF/ELSE\n type: if-else\n height: 358\n id: '1756443014860'\n position:\n x: -733.5977815139424\n y: 236.10252072775984\n positionAbsolute:\n x: -733.5977815139424\n y: 236.10252072775984\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Jina Reader\n datasource_name: jina_reader\n datasource_parameters:\n crawl_sub_pages:\n type: variable\n value:\n - rag\n - '1756896212061'\n - jina_subpages\n limit:\n type: variable\n value:\n - rag\n - '1756896212061'\n - jina_limit\n url:\n type: mixed\n value: '{{#rag.1756896212061.jina_url#}}'\n use_sitemap:\n type: variable\n value:\n - rag\n - '1756896212061'\n - jian_sitemap\n plugin_id: langgenius/jina_datasource\n provider_name: jinareader\n provider_type: website_crawl\n selected: false\n title: Jina Reader\n type: datasource\n height: 52\n id: '1756896212061'\n position:\n x: -1371.6520723158733\n y: 538.9988445953813\n positionAbsolute:\n x: -1371.6520723158733\n y: 538.9988445953813\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: Firecrawl\n datasource_name: crawl\n datasource_parameters:\n crawl_subpages:\n type: variable\n value:\n - rag\n - '1756907397615'\n - firecrawl_subpages\n exclude_paths:\n type: mixed\n value: '{{#rag.1756907397615.exclude_paths#}}'\n include_paths:\n type: mixed\n value: '{{#rag.1756907397615.include_paths#}}'\n limit:\n type: variable\n value:\n - rag\n - '1756907397615'\n - max_pages\n max_depth:\n type: variable\n value:\n - rag\n - '1756907397615'\n - max_depth\n only_main_content:\n type: variable\n value:\n - rag\n - '1756907397615'\n - main_content\n url:\n type: mixed\n value: '{{#rag.1756907397615.firecrawl_url1#}}'\n plugin_id: langgenius/firecrawl_datasource\n provider_name: firecrawl\n provider_type: website_crawl\n selected: false\n title: Firecrawl\n type: datasource\n height: 52\n id: '1756907397615'\n position:\n x: -1371.6520723158733\n y: 644.3296146102903\n positionAbsolute:\n x: -1371.6520723158733\n y: 644.3296146102903\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The file you want to extract QA from.\n ja_JP: The file you want to extract QA from.\n pt_BR: The file you want to extract QA from.\n zh_Hans: 你想要提取 QA 的文件。\n label:\n en_US: Input File\n ja_JP: Input File\n pt_BR: Input File\n zh_Hans: 输入文件\n llm_description: The file you want to extract QA from.\n max: null\n min: null\n name: input_file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n - auto_generate: null\n default: 0\n form: llm\n human_description:\n en_US: Column number for question.\n ja_JP: Column number for question.\n pt_BR: Column number for question.\n zh_Hans: 问题所在的列。\n label:\n en_US: Column number for question\n ja_JP: Column number for question\n pt_BR: Column number for question\n zh_Hans: 问题所在的列\n llm_description: The column number for question, the format of the column\n number must be an integer.\n max: null\n min: null\n name: question_column\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: 1\n form: llm\n human_description:\n en_US: Column number for answer.\n ja_JP: Column number for answer.\n pt_BR: Column number for answer.\n zh_Hans: 答案所在的列。\n label:\n en_US: Column number for answer\n ja_JP: Column number for answer\n pt_BR: Column number for answer\n zh_Hans: 答案所在的列\n llm_description: The column number for answer, the format of the column\n number must be an integer.\n max: null\n min: null\n name: answer_column\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: number\n params:\n answer_column: ''\n input_file: ''\n question_column: ''\n provider_id: langgenius/qa_chunk/qa_chunk\n provider_name: langgenius/qa_chunk/qa_chunk\n provider_type: builtin\n selected: false\n title: Q&A Processor\n tool_configurations: {}\n tool_description: A tool for QA chunking mode.\n tool_label: QA Chunk\n tool_name: qa_chunk\n tool_node_version: '2'\n tool_parameters:\n answer_column:\n type: constant\n value: 2\n input_file:\n type: variable\n value:\n - 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Present\n the output as a Markdown table, where the first column is serial number,\n the second column is Question, and the third column is Question. 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+ "x": -1371.6520723158733,
+ "y": 644.3296146102903
+ },
+ "positionAbsolute": {
+ "x": -1371.6520723158733,
+ "y": 644.3296146102903
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "is_team_authorization": true,
+ "paramSchemas": [
+ {
+ "auto_generate": null,
+ "default": null,
+ "form": "llm",
+ "human_description": {
+ "en_US": "The file you want to extract QA from.",
+ "ja_JP": "The file you want to extract QA from.",
+ "pt_BR": "The file you want to extract QA from.",
+ "zh_Hans": "你想要提取 QA 的文件。"
+ },
+ "label": {
+ "en_US": "Input File",
+ "ja_JP": "Input File",
+ "pt_BR": "Input File",
+ "zh_Hans": "输入文件"
+ },
+ "llm_description": "The file you want to extract QA from.",
+ "max": null,
+ "min": null,
+ "name": "input_file",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "file"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Column number for question.",
+ "ja_JP": "Column number for question.",
+ "pt_BR": "Column number for question.",
+ "zh_Hans": "问题所在的列。"
+ },
+ "label": {
+ "en_US": "Column number for question",
+ "ja_JP": "Column number for question",
+ "pt_BR": "Column number for question",
+ "zh_Hans": "问题所在的列"
+ },
+ "llm_description": "The column number for question, the format of the column number must be an integer.",
+ "max": null,
+ "min": null,
+ "name": "question_column",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ },
+ {
+ "auto_generate": null,
+ "default": 1,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Column number for answer.",
+ "ja_JP": "Column number for answer.",
+ "pt_BR": "Column number for answer.",
+ "zh_Hans": "答案所在的列。"
+ },
+ "label": {
+ "en_US": "Column number for answer",
+ "ja_JP": "Column number for answer",
+ "pt_BR": "Column number for answer",
+ "zh_Hans": "答案所在的列"
+ },
+ "llm_description": "The column number for answer, the format of the column number must be an integer.",
+ "max": null,
+ "min": null,
+ "name": "answer_column",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ }
+ ],
+ "params": {
+ "answer_column": "",
+ "input_file": "",
+ "question_column": ""
+ },
+ "provider_id": "langgenius/qa_chunk/qa_chunk",
+ "provider_name": "langgenius/qa_chunk/qa_chunk",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Q&A Processor",
+ "tool_configurations": {},
+ "tool_description": "A tool for QA chunking mode.",
+ "tool_label": "QA Chunk",
+ "tool_name": "qa_chunk",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "answer_column": {
+ "type": "constant",
+ "value": 2
+ },
+ "input_file": {
+ "type": "variable",
+ "value": [
+ "1756912537172",
+ "files"
+ ]
+ },
+ "question_column": {
+ "type": "constant",
+ "value": 1
+ }
+ },
+ "type": "tool"
+ },
+ "height": 52,
+ "id": "1756912274158",
+ "position": {
+ "x": 794.2003154321724,
+ "y": 326
+ },
+ "positionAbsolute": {
+ "x": 794.2003154321724,
+ "y": 326
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "context": {
+ "enabled": false,
+ "variable_selector": []
+ },
+ "model": {
+ "completion_params": {
+ "temperature": 0.7
+ },
+ "mode": "chat",
+ "name": "claude-3-5-sonnet-20240620",
+ "provider": "langgenius/anthropic/anthropic"
+ },
+ "prompt_template": [
+ {
+ "id": "7f8105aa-a37d-4f5a-b581-babeeb31e833",
+ "role": "system",
+ "text": "\nGenerate a list of Q&A pairs based on {{#1753346901505.output#}}. Present the output as a Markdown table, where the first column is serial number, the second column is Question, and the third column is Question. Ensure that the table format can be easily converted into a CSV file.\nExample Output Format:\n| Index | Question | Answer |\n|-------|-----------|--------|\n| 1 | What is the main purpose of the document? | The document explains the company's new product launch strategy.  |\n| 2 || When will the product be launched? | The product will be launched in Q3 of this year. |\n\nInstructions:\nRead and understand the input text.\nExtract key information and generate meaningful questions and answers.\nPreserve any ![image] URLs from the input text in the answers.\nKeep questions concise and specific.\nEnsure answers are accurate, self-contained, and clear.\nOutput only the Markdown table without any extra explanation."
+ }
+ ],
+ "selected": false,
+ "title": "LLM",
+ "type": "llm",
+ "vision": {
+ "enabled": false
+ }
+ },
+ "height": 88,
+ "id": "1756912504019",
+ "position": {
+ "x": 184.46657789772178,
+ "y": 326
+ },
+ "positionAbsolute": {
+ "x": 184.46657789772178,
+ "y": 326
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "is_team_authorization": true,
+ "paramSchemas": [
+ {
+ "auto_generate": null,
+ "default": null,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Markdown text",
+ "ja_JP": "Markdown text",
+ "pt_BR": "Markdown text",
+ "zh_Hans": "Markdown格式文本,必须为Markdown表格格式"
+ },
+ "label": {
+ "en_US": "Markdown text",
+ "ja_JP": "Markdown text",
+ "pt_BR": "Markdown text",
+ "zh_Hans": "Markdown格式文本"
+ },
+ "llm_description": "",
+ "max": null,
+ "min": null,
+ "name": "md_text",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": null,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Filename of the output file",
+ "ja_JP": "Filename of the output file",
+ "pt_BR": "Filename of the output file",
+ "zh_Hans": "输出文件名"
+ },
+ "label": {
+ "en_US": "Filename of the output file",
+ "ja_JP": "Filename of the output file",
+ "pt_BR": "Filename of the output file",
+ "zh_Hans": "输出文件名"
+ },
+ "llm_description": "",
+ "max": null,
+ "min": null,
+ "name": "output_filename",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ }
+ ],
+ "params": {
+ "md_text": "",
+ "output_filename": ""
+ },
+ "provider_id": "bowenliang123/md_exporter/md_exporter",
+ "provider_name": "bowenliang123/md_exporter/md_exporter",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Markdown to CSV file",
+ "tool_configurations": {},
+ "tool_description": "Generate CSV file from Markdown text",
+ "tool_label": "Markdown to CSV file",
+ "tool_name": "md_to_csv",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "md_text": {
+ "type": "mixed",
+ "value": "{{#1756912504019.text#}}"
+ },
+ "output_filename": {
+ "type": "mixed",
+ "value": "LLM Generated Q&A"
+ }
+ },
+ "type": "tool"
+ },
+ "height": 52,
+ "id": "1756912537172",
+ "position": {
+ "x": 484.75465419110174,
+ "y": 326
+ },
+ "positionAbsolute": {
+ "x": 484.75465419110174,
+ "y": 326
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "author": "TenTen",
+ "desc": "",
+ "height": 174,
+ "selected": false,
+ "showAuthor": true,
+ "text": "{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The LLM-generated Q&A pairs are designed to extract key information from the input text and present it in a structured, easy-to-use format. Each pair consists of a concise question that captures an important point or detail, and a clear, self-contained answer that provides the relevant information without requiring additional context. The output is formatted as a Markdown table with three columns—Index, Question, and Answer—so that it can be easily converted into a CSV file for further processing. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}",
+ "theme": "blue",
+ "title": "",
+ "type": "",
+ "width": 528
+ },
+ "height": 174,
+ "id": "1756912556940",
+ "position": {
+ "x": 184.46657789772178,
+ "y": 462.64405262857747
+ },
+ "positionAbsolute": {
+ "x": 184.46657789772178,
+ "y": 462.64405262857747
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom-note",
+ "width": 528
+ }
+ ],
+ "viewport": {
+ "x": 1149.1394490177502,
+ "y": 317.2338302699771,
+ "zoom": 0.4911032886685182
+ }
+ },
+ "icon_info": {
+ "icon": "e4ea16ed-9690-4de9-ab80-5b622ecbcc04",
+ "icon_background": null,
+ "icon_type": "image",
+ "icon_url": 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"
+ },
+ "id": "98374ab6-9dcd-434d-983e-268bec156b43",
+ "name": "LLM Generated Q&A",
+ "icon": {
+ "icon": "e4ea16ed-9690-4de9-ab80-5b622ecbcc04",
+ "icon_background": null,
+ "icon_type": "image",
+ "icon_url": 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+ },
+ "language": "zh-Hans",
+ "position": 5
+ },
+ {
+ "chunk_structure": "hierarchical_model",
+ "description": "This knowledge pipeline uses LLMs to extract content from images and tables in documents and automatically generate descriptive annotations for contextual enrichment.",
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name: Contextual Enrichment Using LLM\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1751336942081-source-1750400198569-target\n selected: false\n source: '1751336942081'\n sourceHandle: source\n target: '1750400198569'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: llm\n targetType: tool\n id: 1758002850987-source-1751336942081-target\n source: '1758002850987'\n sourceHandle: source\n target: '1751336942081'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInIteration: false\n isInLoop: false\n sourceType: datasource\n targetType: tool\n id: 1756915693835-source-1758027159239-target\n source: '1756915693835'\n sourceHandle: source\n target: '1758027159239'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: llm\n id: 1758027159239-source-1758002850987-target\n source: '1758027159239'\n sourceHandle: source\n target: '1758002850987'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: hierarchical_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius\/jina\/jina\n index_chunk_variable_selector:\n - '1751336942081'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n reranking_enable: true\n reranking_mode: reranking_model\n reranking_model:\n reranking_model_name: jina-reranker-v1-base-en\n reranking_provider_name: langgenius\/jina\/jina\n score_threshold: 0\n score_threshold_enabled: false\n search_method: hybrid_search\n top_k: 3\n weights: null\n selected: false\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750400198569'\n position:\n x: 474.7618603027596\n y: 282\n positionAbsolute:\n x: 474.7618603027596\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 458\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 5 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Text Input, Online Drive, Online Doc, and Web Crawler. Different\n types of Data Sources have different input and output types. The output\n of File Upload and Online Drive are files, while the output of Online Doc\n and WebCrawler are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 458\n id: '1751264451381'\n position:\n x: -893.2836123260277\n y: 378.2537898330178\n positionAbsolute:\n x: -893.2836123260277\n y: 378.2537898330178\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 260\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n \u2192 use extractor to extract document content \u2192 split and clean content into\n structured chunks \u2192 store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https:\/\/docs.dify.ai\/en\/guides\/knowledge-base\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https:\/\/docs.dify.ai\/en\/guides\/knowledge-base\"},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\".\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1182\n height: 260\n id: '1751266376760'\n position:\n x: -704.0614991386192\n y: -73.30453110517956\n positionAbsolute:\n x: -704.0614991386192\n y: -73.30453110517956\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1182\n - data:\n author: TenTen\n desc: ''\n height: 304\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"MinerU\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n is an advanced open-source document extractor designed specifically to convert\n complex, unstructured documents\u2014such as PDFs, Word files, and PPTs\u2014into\n high-quality, machine-readable formats like Markdown and JSON. MinerU addresses\n challenges in document parsing such as layout detection, formula recognition,\n and multi-language support, which are critical for generating high-quality\n training corpora for LLMs.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 304\n id: '1751266402561'\n position:\n x: -555.2228329530462\n y: 592.0458661166498\n positionAbsolute:\n x: -555.2228329530462\n y: 592.0458661166498\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 554\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Parent-Child\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n addresses the dilemma of context and precision by leveraging a two-tier\n hierarchical approach that effectively balances the trade-off between accurate\n matching and comprehensive contextual information in RAG systems. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Here\n is the essential mechanism of this structured, two-level information access:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Query Matching with Child Chunks: Small, focused pieces of information,\n often as concise as a single sentence within a paragraph, are used to match\n the user''s query. These child chunks enable precise and relevant initial\n retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Contextual Enrichment with Parent Chunks: Larger, encompassing sections\u2014such\n as a paragraph, a section, or even an entire document\u2014that include the matched\n child chunks are then retrieved. These parent chunks provide comprehensive\n context for the Language Model (LLM).\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 554\n id: '1751266447821'\n position:\n x: 153.2996965006646\n y: 378.2537898330178\n positionAbsolute:\n x: 153.2996965006646\n y: 378.2537898330178\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 411\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods:\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0and\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. Economical mode\n uses keyword-based inverted indexing with no token consumption but lower\n accuracy; upgrading to High-Quality is possible, but downgrading requires\n creating a new knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"*\n Parent-Child Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0and\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q&A\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0only\n support the\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0indexing\n method.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 411\n id: '1751266580099'\n position:\n x: 482.3389174180554\n y: 437.9839361130071\n positionAbsolute:\n x: 482.3389174180554\n y: 437.9839361130071\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n result:\n description: Parent child chunks result\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: ''\n ja_JP: ''\n pt_BR: ''\n zh_Hans: ''\n label:\n en_US: Input Content\n ja_JP: Input Content\n pt_BR: Conte\u00fado de Entrada\n zh_Hans: \u8f93\u5165\u6587\u672c\n llm_description: The text you want to chunk.\n max: null\n min: null\n name: input_text\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: paragraph\n form: llm\n human_description:\n en_US: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n ja_JP: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n pt_BR: Dividir texto em par\u00e1grafos com base no separador e no comprimento\n m\u00e1ximo do bloco, usando o texto dividido como bloco pai ou documento\n completo como bloco pai e diretamente recuper\u00e1-lo.\n zh_Hans: \u6839\u636e\u5206\u9694\u7b26\u548c\u6700\u5927\u5757\u957f\u5ea6\u5c06\u6587\u672c\u62c6\u5206\u4e3a\u6bb5\u843d\uff0c\u4f7f\u7528\u62c6\u5206\u6587\u672c\u4f5c\u4e3a\u68c0\u7d22\u7684\u7236\u5757\u6216\u6574\u4e2a\u6587\u6863\u7528\u4f5c\u7236\u5757\u5e76\u76f4\u63a5\u68c0\u7d22\u3002\n label:\n en_US: Parent Mode\n ja_JP: Parent Mode\n pt_BR: Modo Pai\n zh_Hans: \u7236\u5757\u6a21\u5f0f\n llm_description: Split text into paragraphs based on separator and maximum\n chunk length, using split text as parent block or entire document as parent\n block and directly retrieve.\n max: null\n min: null\n name: parent_mode\n options:\n - label:\n en_US: Paragraph\n ja_JP: Paragraph\n pt_BR: Par\u00e1grafo\n zh_Hans: \u6bb5\u843d\n value: paragraph\n - label:\n en_US: Full Document\n ja_JP: Full Document\n pt_BR: Documento Completo\n zh_Hans: \u5168\u6587\n value: full_doc\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: '\n\n\n '\n form: llm\n human_description:\n en_US: Separator used for chunking\n ja_JP: Separator used for chunking\n pt_BR: Separador usado para divis\u00e3o\n zh_Hans: \u7528\u4e8e\u5206\u5757\u7684\u5206\u9694\u7b26\n label:\n en_US: Parent Delimiter\n ja_JP: Parent Delimiter\n pt_BR: Separador de Pai\n zh_Hans: \u7236\u5757\u5206\u9694\u7b26\n llm_description: The separator used to split chunks\n max: null\n min: null\n name: separator\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 1024\n form: llm\n human_description:\n en_US: Maximum length for chunking\n ja_JP: Maximum length for chunking\n pt_BR: Comprimento m\u00e1ximo para divis\u00e3o\n zh_Hans: \u7528\u4e8e\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6\n label:\n en_US: Maximum Parent Chunk Length\n ja_JP: Maximum Parent Chunk Length\n pt_BR: Comprimento M\u00e1ximo do Bloco Pai\n zh_Hans: \u6700\u5927\u7236\u5757\u957f\u5ea6\n llm_description: Maximum length allowed per chunk\n max: null\n min: null\n name: max_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: '. '\n form: llm\n human_description:\n en_US: Separator used for subchunking\n ja_JP: Separator used for subchunking\n pt_BR: Separador usado para subdivis\u00e3o\n zh_Hans: \u7528\u4e8e\u5b50\u5206\u5757\u7684\u5206\u9694\u7b26\n label:\n en_US: Child Delimiter\n ja_JP: Child Delimiter\n pt_BR: Separador de Subdivis\u00e3o\n zh_Hans: \u5b50\u5206\u5757\u5206\u9694\u7b26\n llm_description: The separator used to split subchunks\n max: null\n min: null\n name: subchunk_separator\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 512\n form: llm\n human_description:\n en_US: Maximum length for subchunking\n ja_JP: Maximum length for subchunking\n pt_BR: Comprimento m\u00e1ximo para subdivis\u00e3o\n zh_Hans: \u7528\u4e8e\u5b50\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6\n label:\n en_US: Maximum Child Chunk Length\n ja_JP: Maximum Child Chunk Length\n pt_BR: Comprimento M\u00e1ximo de Subdivis\u00e3o\n zh_Hans: \u5b50\u5206\u5757\u6700\u5927\u957f\u5ea6\n llm_description: Maximum length allowed per subchunk\n max: null\n min: null\n name: subchunk_max_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: 0\n form: llm\n human_description:\n en_US: Whether to remove consecutive spaces, newlines and tabs\n ja_JP: Whether to remove consecutive spaces, newlines and tabs\n pt_BR: Se deve remover espa\u00e7os extras no texto\n zh_Hans: \u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26\n label:\n en_US: Replace consecutive spaces, newlines and tabs\n ja_JP: Replace consecutive spaces, newlines and tabs\n pt_BR: Substituir espa\u00e7os consecutivos, novas linhas e guias\n zh_Hans: \u66ff\u6362\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26\n llm_description: Whether to remove consecutive spaces, newlines and tabs\n max: null\n min: null\n name: remove_extra_spaces\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: 0\n form: llm\n human_description:\n en_US: Whether to remove URLs and emails in the text\n ja_JP: Whether to remove URLs and emails in the text\n pt_BR: Se deve remover URLs e e-mails no texto\n zh_Hans: \u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\n label:\n en_US: Delete all URLs and email addresses\n ja_JP: Delete all URLs and email addresses\n pt_BR: Remover todas as URLs e e-mails\n zh_Hans: \u5220\u9664\u6240\u6709URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\n llm_description: Whether to remove URLs and emails in the text\n max: null\n min: null\n name: remove_urls_emails\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n params:\n input_text: ''\n max_length: ''\n parent_mode: ''\n remove_extra_spaces: ''\n remove_urls_emails: ''\n separator: ''\n subchunk_max_length: ''\n subchunk_separator: ''\n provider_id: langgenius\/parentchild_chunker\/parentchild_chunker\n provider_name: langgenius\/parentchild_chunker\/parentchild_chunker\n provider_type: builtin\n selected: false\n title: Parent-child Chunker\n tool_configurations: {}\n tool_description: Process documents into parent-child chunk structures\n tool_label: Parent-child Chunker\n tool_name: parentchild_chunker\n tool_node_version: '2'\n tool_parameters:\n input_text:\n type: mixed\n value: '{{#1758002850987.text#}}'\n max_length:\n type: variable\n value:\n - rag\n - shared\n - Maximum_Parent_Length\n parent_mode:\n type: variable\n value:\n - rag\n - shared\n - Parent_Mode\n remove_extra_spaces:\n type: variable\n value:\n - rag\n - shared\n - clean_1\n remove_urls_emails:\n type: variable\n value:\n - rag\n - shared\n - clean_2\n separator:\n type: mixed\n value: '{{#rag.shared.Parent_Delimiter#}}'\n subchunk_max_length:\n type: variable\n value:\n - rag\n - shared\n - Maximum_Child_Length\n subchunk_separator:\n type: mixed\n value: '{{#rag.shared.Child_Delimiter#}}'\n type: tool\n height: 52\n id: '1751336942081'\n position:\n x: 144.55897745117755\n y: 282\n positionAbsolute:\n x: 144.55897745117755\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 446\n selected: true\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"In\n this step, the LLM is responsible for enriching and reorganizing content,\n along with images and tables. The goal is to maintain the integrity of image\n URLs and tables while providing contextual descriptions and summaries to\n enhance understanding. The content should be structured into well-organized\n paragraphs, using double newlines to separate them. The LLM should enrich\n the document by adding relevant descriptions for images and extracting key\n insights from tables, ensuring the content remains easy to retrieve within\n a Retrieval-Augmented Generation (RAG) system. The final output should preserve\n the original structure, making it more accessible for knowledge retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 446\n id: '1753967810859'\n position:\n x: -176.67459682201036\n y: 405.2790698865377\n positionAbsolute:\n x: -176.67459682201036\n y: 405.2790698865377\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - pdf\n - doc\n - docx\n - pptx\n - ppt\n - jpg\n - png\n - jpeg\n plugin_id: langgenius\/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File\n type: datasource\n height: 52\n id: '1756915693835'\n position:\n x: -893.2836123260277\n y: 282\n positionAbsolute:\n x: -893.2836123260277\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n context:\n enabled: false\n variable_selector: []\n model:\n completion_params:\n temperature: 0.7\n mode: chat\n name: claude-3-5-sonnet-20240620\n provider: langgenius\/anthropic\/anthropic\n prompt_template:\n - id: beb97761-d30d-4549-9b67-de1b8292e43d\n role: system\n text: \"You are an AI document assistant. \\nYour tasks are:\\nEnrich the content\\\n \\ contextually:\\nAdd meaningful descriptions for each image.\\nSummarize\\\n \\ key information from each table.\\nOutput the enriched content\u00a0with clear\\\n \\ annotations showing the\u00a0corresponding image and table positions, so\\\n \\ the text can later be aligned back into the original document. Preserve\\\n \\ any ![image] URLs from the input text.\\nYou will receive two inputs:\\n\\\n The file and text\u00a0(may contain images url and tables).\\nThe final output\\\n \\ should be a\u00a0single, enriched version of the original document with ![image]\\\n \\ url preserved.\\nGenerate output directly without saying words like:\\\n \\ Here's the enriched version of the original text with the image description\\\n \\ inserted.\"\n - id: f92ef0cd-03a7-48a7-80e8-bcdc965fb399\n role: user\n text: The file is {{#1756915693835.file#}} and the text are\u00a0{{#1758027159239.text#}}.\n selected: false\n title: LLM\n type: llm\n vision:\n configs:\n detail: high\n variable_selector:\n - '1756915693835'\n - file\n enabled: true\n height: 88\n id: '1758002850987'\n position:\n x: -176.67459682201036\n y: 282\n positionAbsolute:\n x: -176.67459682201036\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: The file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n ja_JP: \u89e3\u6790\u3059\u308b\u30d5\u30a1\u30a4\u30eb(pdf\u3001ppt\u3001pptx\u3001doc\u3001docx\u3001png\u3001jpg\u3001jpeg\u3092\u30b5\u30dd\u30fc\u30c8)\n pt_BR: The file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n zh_Hans: \u7528\u4e8e\u89e3\u6790\u7684\u6587\u4ef6(\u652f\u6301 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)\n label:\n en_US: file\n ja_JP: file\n pt_BR: file\n zh_Hans: file\n llm_description: The file to be parsed (support pdf, ppt, pptx, doc, docx,\n png, jpg, jpeg)\n max: null\n min: null\n name: file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n - auto_generate: null\n default: auto\n form: form\n human_description:\n en_US: (For local deployment v1 and v2) Parsing method, can be auto, ocr,\n or txt. Default is auto. If results are not satisfactory, try ocr\n ja_JP: \uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v1\u3068v2\u7528\uff09\u89e3\u6790\u65b9\u6cd5\u306f\u3001auto\u3001ocr\u3001\u307e\u305f\u306ftxt\u306e\u3044\u305a\u308c\u304b\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fauto\u3067\u3059\u3002\u7d50\u679c\u304c\u6e80\u8db3\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001ocr\u3092\u8a66\u3057\u3066\u304f\u3060\u3055\u3044\n pt_BR: (For local deployment v1 and v2) Parsing method, can be auto, ocr,\n or txt. Default is auto. If results are not satisfactory, try ocr\n zh_Hans: \uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v1\u548cv2\u7248\u672c\uff09\u89e3\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u662fauto, ocr, \u6216 txt\u3002\u9ed8\u8ba4\u662fauto\u3002\u5982\u679c\u7ed3\u679c\u4e0d\u7406\u60f3\uff0c\u8bf7\u5c1d\u8bd5ocr\n label:\n en_US: parse method\n ja_JP: \u89e3\u6790\u65b9\u6cd5\n pt_BR: parse method\n zh_Hans: \u89e3\u6790\u65b9\u6cd5\n llm_description: (For local deployment v1 and v2) Parsing method, can be\n auto, ocr, or txt. Default is auto. If results are not satisfactory, try\n ocr\n max: null\n min: null\n name: parse_method\n options:\n - icon: ''\n label:\n en_US: auto\n ja_JP: auto\n pt_BR: auto\n zh_Hans: auto\n value: auto\n - icon: ''\n label:\n en_US: ocr\n ja_JP: ocr\n pt_BR: ocr\n zh_Hans: ocr\n value: ocr\n - icon: ''\n label:\n en_US: txt\n ja_JP: txt\n pt_BR: txt\n zh_Hans: txt\n value: txt\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: 1\n form: form\n human_description:\n en_US: (For official API and local deployment v2) Whether to enable formula\n recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\u3068\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API and local deployment v2) Whether to enable formula\n recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u662f\u5426\u5f00\u542f\u516c\u5f0f\u8bc6\u522b\n label:\n en_US: Enable formula recognition\n ja_JP: \u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable formula recognition\n zh_Hans: \u5f00\u542f\u516c\u5f0f\u8bc6\u522b\n llm_description: (For official API and local deployment v2) Whether to enable\n formula recognition\n max: null\n min: null\n name: enable_formula\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: 1\n form: form\n human_description:\n en_US: (For official API and local deployment v2) Whether to enable table\n recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\u3068\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API and local deployment v2) Whether to enable table\n recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u662f\u5426\u5f00\u542f\u8868\u683c\u8bc6\u522b\n label:\n en_US: Enable table recognition\n ja_JP: \u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable table recognition\n zh_Hans: \u5f00\u542f\u8868\u683c\u8bc6\u522b\n llm_description: (For official API and local deployment v2) Whether to enable\n table recognition\n max: null\n min: null\n name: enable_table\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: auto\n form: form\n human_description:\n en_US: '(For official API and local deployment v2) Specify document language,\n default ch, can be set to auto(local deployment need to specify the\n language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5'\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\u3068\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fch\u3067\u3001auto\u306b\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002auto\u306e\u5834\u5408\uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8\u3067\u306f\u8a00\u8a9e\u3092\u6307\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fch\u3067\u3059\uff09\u3001\u30e2\u30c7\u30eb\u306f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u81ea\u52d5\u7684\u306b\u8b58\u5225\u3057\u307e\u3059\u3002\u4ed6\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u5024\u30ea\u30b9\u30c8\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5\n pt_BR: '(For official API and local deployment v2) Specify document language,\n default ch, can be set to auto(local deployment need to specify the\n language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5'\n zh_Hans: \uff08\u4ec5\u9650\u5b98\u65b9api\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u6307\u5b9a\u6587\u6863\u8bed\u8a00\uff0c\u9ed8\u8ba4 ch\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3aauto\uff0c\u5f53\u4e3aauto\u65f6\u6a21\u578b\u4f1a\u81ea\u52a8\u8bc6\u522b\u6587\u6863\u8bed\u8a00\uff08\u672c\u5730\u90e8\u7f72\u9700\u8981\u6307\u5b9a\u660e\u786e\u7684\u8bed\u8a00\uff0c\u9ed8\u8ba4ch\uff09\uff0c\u5176\u4ed6\u53ef\u9009\u503c\u5217\u8868\u8be6\u89c1\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5\n label:\n en_US: Document language\n ja_JP: \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\n pt_BR: Document language\n zh_Hans: \u6587\u6863\u8bed\u8a00\n llm_description: '(For official API and local deployment v2) Specify document\n language, default ch, can be set to auto(local deployment need to specify\n the language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5'\n max: null\n min: null\n name: language\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 0\n form: form\n human_description:\n en_US: (For official API) Whether to enable OCR recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API) Whether to enable OCR recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542fOCR\u8bc6\u522b\n label:\n en_US: Enable OCR recognition\n ja_JP: OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable OCR recognition\n zh_Hans: \u5f00\u542fOCR\u8bc6\u522b\n llm_description: (For official API) Whether to enable OCR recognition\n max: null\n min: null\n name: enable_ocr\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: '[]'\n form: form\n human_description:\n en_US: '(For official API) Example: [\"docx\",\"html\"], markdown, json are\n the default export formats, no need to set, this parameter only supports\n one or more of docx, html, latex'\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u4f8b\uff1a[\"docx\",\"html\"]\u3001markdown\u3001json\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\u3067\u3042\u308a\u3001\u8a2d\u5b9a\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001docx\u3001html\u3001latex\u306e3\u3064\u306e\u5f62\u5f0f\u306e\u3044\u305a\u308c\u304b\u307e\u305f\u306f\u8907\u6570\u306e\u307f\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u307e\u3059\n pt_BR: '(For official API) Example: [\"docx\",\"html\"], markdown, json are\n the default export formats, no need to set, this parameter only supports\n one or more of docx, html, latex'\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u793a\u4f8b\uff1a[\"docx\",\"html\"],markdown\u3001json\u4e3a\u9ed8\u8ba4\u5bfc\u51fa\u683c\u5f0f\uff0c\u65e0\u987b\u8bbe\u7f6e\uff0c\u8be5\u53c2\u6570\u4ec5\u652f\u6301docx\u3001html\u3001latex\u4e09\u79cd\u683c\u5f0f\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\n label:\n en_US: Extra export formats\n ja_JP: \u8ffd\u52a0\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\n pt_BR: Extra export formats\n zh_Hans: \u989d\u5916\u5bfc\u51fa\u683c\u5f0f\n llm_description: '(For official API) Example: [\"docx\",\"html\"], markdown,\n json are the default export formats, no need to set, this parameter only\n supports one or more of docx, html, latex'\n max: null\n min: null\n name: extra_formats\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: pipeline\n form: form\n human_description:\n en_US: '(For local deployment v2) Example: pipeline, vlm-transformers,\n vlm-sglang-engine, vlm-sglang-client, default is pipeline'\n ja_JP: \uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u4f8b\uff1apipeline\u3001vlm-transformers\u3001vlm-sglang-engine\u3001vlm-sglang-client\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306fpipeline\n pt_BR: '(For local deployment v2) Example: pipeline, vlm-transformers,\n vlm-sglang-engine, vlm-sglang-client, default is pipeline'\n zh_Hans: \uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u793a\u4f8b\uff1apipeline\u3001vlm-transformers\u3001vlm-sglang-engine\u3001vlm-sglang-client\uff0c\u9ed8\u8ba4\u503c\u4e3apipeline\n label:\n en_US: Backend type\n ja_JP: \u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u30bf\u30a4\u30d7\n pt_BR: Backend type\n zh_Hans: \u89e3\u6790\u540e\u7aef\n llm_description: '(For local deployment v2) Example: pipeline, vlm-transformers,\n vlm-sglang-engine, vlm-sglang-client, default is pipeline'\n max: null\n min: null\n name: backend\n options:\n - icon: ''\n label:\n en_US: pipeline\n ja_JP: pipeline\n pt_BR: pipeline\n zh_Hans: pipeline\n value: pipeline\n - icon: ''\n label:\n en_US: vlm-transformers\n ja_JP: vlm-transformers\n pt_BR: vlm-transformers\n zh_Hans: vlm-transformers\n value: vlm-transformers\n - icon: ''\n label:\n en_US: vlm-sglang-engine\n ja_JP: vlm-sglang-engine\n pt_BR: vlm-sglang-engine\n zh_Hans: vlm-sglang-engine\n value: vlm-sglang-engine\n - icon: ''\n label:\n en_US: vlm-sglang-client\n ja_JP: vlm-sglang-client\n pt_BR: vlm-sglang-client\n zh_Hans: vlm-sglang-client\n value: vlm-sglang-client\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: ''\n form: form\n human_description:\n en_US: '(For local deployment v2 when backend is vlm-sglang-client) Example:\n http:\/\/127.0.0.1:8000, default is empty'\n ja_JP: \uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528 \u89e3\u6790\u5f8c\u7aef\u304cvlm-sglang-client\u306e\u5834\u5408\uff09\u4f8b\uff1ahttp:\/\/127.0.0.1:8000\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u7a7a\n pt_BR: '(For local deployment v2 when backend is vlm-sglang-client) Example:\n http:\/\/127.0.0.1:8000, default is empty'\n zh_Hans: \uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v2\u7248\u672c \u89e3\u6790\u540e\u7aef\u4e3avlm-sglang-client\u65f6\uff09\u793a\u4f8b\uff1ahttp:\/\/127.0.0.1:8000\uff0c\u9ed8\u8ba4\u503c\u4e3a\u7a7a\n label:\n en_US: sglang-server url\n ja_JP: sglang-server\u30a2\u30c9\u30ec\u30b9\n pt_BR: sglang-server url\n zh_Hans: sglang-server\u5730\u5740\n llm_description: '(For local deployment v2 when backend is vlm-sglang-client)\n Example: http:\/\/127.0.0.1:8000, default is empty'\n max: null\n min: null\n name: sglang_server_url\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n params:\n backend: ''\n enable_formula: ''\n enable_ocr: ''\n enable_table: ''\n extra_formats: ''\n file: ''\n language: ''\n parse_method: ''\n sglang_server_url: ''\n provider_id: langgenius\/mineru\/mineru\n provider_name: langgenius\/mineru\/mineru\n provider_type: builtin\n selected: false\n title: Parse File\n tool_configurations:\n backend:\n type: constant\n value: pipeline\n enable_formula:\n type: constant\n value: 1\n enable_ocr:\n type: constant\n value: true\n enable_table:\n type: constant\n value: 1\n extra_formats:\n type: mixed\n value: '[]'\n language:\n type: mixed\n value: auto\n parse_method:\n type: constant\n value: auto\n sglang_server_url:\n type: mixed\n value: ''\n tool_description: a tool for parsing text, tables, and images, supporting\n multiple formats such as pdf, pptx, docx, etc. supporting multiple languages\n such as English, Chinese, etc.\n tool_label: Parse File\n tool_name: parse-file\n tool_node_version: '2'\n tool_parameters:\n file:\n type: variable\n value:\n - '1756915693835'\n - file\n type: tool\n height: 270\n id: '1758027159239'\n position:\n x: -544.9739996945534\n y: 282\n positionAbsolute:\n x: -544.9739996945534\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n viewport:\n x: 679.9701291615181\n y: -191.49392257836791\n zoom: 0.8239704766223018\n rag_pipeline_variables:\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: paragraph\n label: Parent Mode\n max_length: 48\n options:\n - paragraph\n - full_doc\n placeholder: null\n required: true\n tooltips: 'Parent Mode provides two options: paragraph mode splits text into paragraphs\n as parent chunks for retrieval, while full_doc mode uses the entire document\n as a single parent chunk (text beyond 10,000 tokens will be truncated).'\n type: select\n unit: null\n variable: Parent_Mode\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: \\n\\n\n label: Parent Delimiter\n max_length: 48\n options: []\n placeholder: null\n required: false\n tooltips: A delimiter is the character used to separate text. \\n\\n is recommended\n for splitting the original document into large parent chunks. You can also use\n special delimiters defined by yourself.\n type: text-input\n unit: null\n variable: Parent_Delimiter\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: 1024\n label: Maximum Parent Length\n max_length: 48\n options: []\n placeholder: null\n required: false\n tooltips: null\n type: number\n unit: tokens\n variable: Maximum_Parent_Length\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: \\n\n label: Child Delimiter\n max_length: 48\n options: []\n placeholder: null\n required: true\n tooltips: A delimiter is the character used to separate text. \\n is recommended\n for splitting parent chunks into small child chunks. 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+ "pt_BR": "Comprimento M\u00e1ximo do Bloco Pai",
+ "zh_Hans": "\u6700\u5927\u7236\u5757\u957f\u5ea6"
+ },
+ "llm_description": "Maximum length allowed per chunk",
+ "max": null,
+ "min": null,
+ "name": "max_length",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ },
+ {
+ "auto_generate": null,
+ "default": ". ",
+ "form": "llm",
+ "human_description": {
+ "en_US": "Separator used for subchunking",
+ "ja_JP": "Separator used for subchunking",
+ "pt_BR": "Separador usado para subdivis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5b50\u5206\u5757\u7684\u5206\u9694\u7b26"
+ },
+ "label": {
+ "en_US": "Child Delimiter",
+ "ja_JP": "Child Delimiter",
+ "pt_BR": "Separador de Subdivis\u00e3o",
+ "zh_Hans": "\u5b50\u5206\u5757\u5206\u9694\u7b26"
+ },
+ "llm_description": "The separator used to split subchunks",
+ "max": null,
+ "min": null,
+ "name": "subchunk_separator",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": 512,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Maximum length for subchunking",
+ "ja_JP": "Maximum length for subchunking",
+ "pt_BR": "Comprimento m\u00e1ximo para subdivis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5b50\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6"
+ },
+ "label": {
+ "en_US": "Maximum Child Chunk Length",
+ "ja_JP": "Maximum Child Chunk Length",
+ "pt_BR": "Comprimento M\u00e1ximo de Subdivis\u00e3o",
+ "zh_Hans": "\u5b50\u5206\u5757\u6700\u5927\u957f\u5ea6"
+ },
+ "llm_description": "Maximum length allowed per subchunk",
+ "max": null,
+ "min": null,
+ "name": "subchunk_max_length",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove consecutive spaces, newlines and tabs",
+ "ja_JP": "Whether to remove consecutive spaces, newlines and tabs",
+ "pt_BR": "Se deve remover espa\u00e7os extras no texto",
+ "zh_Hans": "\u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26"
+ },
+ "label": {
+ "en_US": "Replace consecutive spaces, newlines and tabs",
+ "ja_JP": "Replace consecutive spaces, newlines and tabs",
+ "pt_BR": "Substituir espa\u00e7os consecutivos, novas linhas e guias",
+ "zh_Hans": "\u66ff\u6362\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26"
+ },
+ "llm_description": "Whether to remove consecutive spaces, newlines and tabs",
+ "max": null,
+ "min": null,
+ "name": "remove_extra_spaces",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove URLs and emails in the text",
+ "ja_JP": "Whether to remove URLs and emails in the text",
+ "pt_BR": "Se deve remover URLs e e-mails no texto",
+ "zh_Hans": "\u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740"
+ },
+ "label": {
+ "en_US": "Delete all URLs and email addresses",
+ "ja_JP": "Delete all URLs and email addresses",
+ "pt_BR": "Remover todas as URLs e e-mails",
+ "zh_Hans": "\u5220\u9664\u6240\u6709URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740"
+ },
+ "llm_description": "Whether to remove URLs and emails in the text",
+ "max": null,
+ "min": null,
+ "name": "remove_urls_emails",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ }
+ ],
+ "params": {
+ "input_text": "",
+ "max_length": "",
+ "parent_mode": "",
+ "remove_extra_spaces": "",
+ "remove_urls_emails": "",
+ "separator": "",
+ "subchunk_max_length": "",
+ "subchunk_separator": ""
+ },
+ "provider_id": "langgenius\/parentchild_chunker\/parentchild_chunker",
+ "provider_name": "langgenius\/parentchild_chunker\/parentchild_chunker",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Parent-child Chunker",
+ "tool_configurations": {},
+ "tool_description": "Process documents into parent-child chunk structures",
+ "tool_label": "Parent-child Chunker",
+ "tool_name": "parentchild_chunker",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "input_text": {
+ "type": "mixed",
+ "value": "{{#1758002850987.text#}}"
+ },
+ "max_length": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "Maximum_Parent_Length"
+ ]
+ },
+ "parent_mode": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "Parent_Mode"
+ ]
+ },
+ "remove_extra_spaces": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "clean_1"
+ ]
+ },
+ "remove_urls_emails": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "clean_2"
+ ]
+ },
+ "separator": {
+ "type": "mixed",
+ "value": "{{#rag.shared.Parent_Delimiter#}}"
+ },
+ "subchunk_max_length": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "Maximum_Child_Length"
+ ]
+ },
+ "subchunk_separator": {
+ "type": "mixed",
+ "value": "{{#rag.shared.Child_Delimiter#}}"
+ }
+ },
+ "type": "tool"
+ },
+ "height": 52,
+ "id": "1751336942081",
+ "position": {
+ "x": 144.55897745117755,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": 144.55897745117755,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "author": "TenTen",
+ "desc": "",
+ "height": 446,
+ "selected": true,
+ "showAuthor": true,
+ "text": "{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"In this step, the LLM is responsible for enriching and reorganizing content, along with images and tables. The goal is to maintain the integrity of image URLs and tables while providing contextual descriptions and summaries to enhance understanding. The content should be structured into well-organized paragraphs, using double newlines to separate them. The LLM should enrich the document by adding relevant descriptions for images and extracting key insights from tables, ensuring the content remains easy to retrieve within a Retrieval-Augmented Generation (RAG) system. The final output should preserve the original structure, making it more accessible for knowledge retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}",
+ "theme": "blue",
+ "title": "",
+ "type": "",
+ "width": 240
+ },
+ "height": 446,
+ "id": "1753967810859",
+ "position": {
+ "x": -176.67459682201036,
+ "y": 405.2790698865377
+ },
+ "positionAbsolute": {
+ "x": -176.67459682201036,
+ "y": 405.2790698865377
+ },
+ "selected": true,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom-note",
+ "width": 240
+ },
+ {
+ "data": {
+ "datasource_configurations": {},
+ "datasource_label": "File",
+ "datasource_name": "upload-file",
+ "datasource_parameters": {},
+ "fileExtensions": [
+ "pdf",
+ "doc",
+ "docx",
+ "pptx",
+ "ppt",
+ "jpg",
+ "png",
+ "jpeg"
+ ],
+ "plugin_id": "langgenius\/file",
+ "provider_name": "file",
+ "provider_type": "local_file",
+ "selected": false,
+ "title": "File",
+ "type": "datasource"
+ },
+ "height": 52,
+ "id": "1756915693835",
+ "position": {
+ "x": -893.2836123260277,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": -893.2836123260277,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "context": {
+ "enabled": false,
+ "variable_selector": []
+ },
+ "model": {
+ "completion_params": {
+ "temperature": 0.7
+ },
+ "mode": "chat",
+ "name": "claude-3-5-sonnet-20240620",
+ "provider": "langgenius\/anthropic\/anthropic"
+ },
+ "prompt_template": [
+ {
+ "id": "beb97761-d30d-4549-9b67-de1b8292e43d",
+ "role": "system",
+ "text": "You are an AI document assistant. \nYour tasks are:\nEnrich the content contextually:\nAdd meaningful descriptions for each image.\nSummarize key information from each table.\nOutput the enriched content\u00a0with clear annotations showing the\u00a0corresponding image and table positions, so the text can later be aligned back into the original document. Preserve any ![image] URLs from the input text.\nYou will receive two inputs:\nThe file and text\u00a0(may contain images url and tables).\nThe final output should be a\u00a0single, enriched version of the original document with ![image] url preserved.\nGenerate output directly without saying words like: Here's the enriched version of the original text with the image description inserted."
+ },
+ {
+ "id": "f92ef0cd-03a7-48a7-80e8-bcdc965fb399",
+ "role": "user",
+ "text": "The file is {{#1756915693835.file#}} and the text are\u00a0{{#1758027159239.text#}}."
+ }
+ ],
+ "selected": false,
+ "title": "LLM",
+ "type": "llm",
+ "vision": {
+ "configs": {
+ "detail": "high",
+ "variable_selector": [
+ "1756915693835",
+ "file"
+ ]
+ },
+ "enabled": true
+ }
+ },
+ "height": 88,
+ "id": "1758002850987",
+ "position": {
+ "x": -176.67459682201036,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": -176.67459682201036,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "is_team_authorization": true,
+ "paramSchemas": [
+ {
+ "auto_generate": null,
+ "default": null,
+ "form": "llm",
+ "human_description": {
+ "en_US": "The file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg, jpeg)",
+ "ja_JP": "\u89e3\u6790\u3059\u308b\u30d5\u30a1\u30a4\u30eb(pdf\u3001ppt\u3001pptx\u3001doc\u3001docx\u3001png\u3001jpg\u3001jpeg\u3092\u30b5\u30dd\u30fc\u30c8)",
+ "pt_BR": "The file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg, jpeg)",
+ "zh_Hans": "\u7528\u4e8e\u89e3\u6790\u7684\u6587\u4ef6(\u652f\u6301 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)"
+ },
+ "label": {
+ "en_US": "file",
+ "ja_JP": "file",
+ "pt_BR": "file",
+ "zh_Hans": "file"
+ },
+ "llm_description": "The file to be parsed (support pdf, ppt, pptx, doc, docx, png, jpg, jpeg)",
+ "max": null,
+ "min": null,
+ "name": "file",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "file"
+ },
+ {
+ "auto_generate": null,
+ "default": "auto",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For local deployment v1 and v2) Parsing method, can be auto, ocr, or txt. Default is auto. If results are not satisfactory, try ocr",
+ "ja_JP": "\uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v1\u3068v2\u7528\uff09\u89e3\u6790\u65b9\u6cd5\u306f\u3001auto\u3001ocr\u3001\u307e\u305f\u306ftxt\u306e\u3044\u305a\u308c\u304b\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fauto\u3067\u3059\u3002\u7d50\u679c\u304c\u6e80\u8db3\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001ocr\u3092\u8a66\u3057\u3066\u304f\u3060\u3055\u3044",
+ "pt_BR": "(For local deployment v1 and v2) Parsing method, can be auto, ocr, or txt. Default is auto. If results are not satisfactory, try ocr",
+ "zh_Hans": "\uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v1\u548cv2\u7248\u672c\uff09\u89e3\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u662fauto, ocr, \u6216 txt\u3002\u9ed8\u8ba4\u662fauto\u3002\u5982\u679c\u7ed3\u679c\u4e0d\u7406\u60f3\uff0c\u8bf7\u5c1d\u8bd5ocr"
+ },
+ "label": {
+ "en_US": "parse method",
+ "ja_JP": "\u89e3\u6790\u65b9\u6cd5",
+ "pt_BR": "parse method",
+ "zh_Hans": "\u89e3\u6790\u65b9\u6cd5"
+ },
+ "llm_description": "(For local deployment v1 and v2) Parsing method, can be auto, ocr, or txt. Default is auto. If results are not satisfactory, try ocr",
+ "max": null,
+ "min": null,
+ "name": "parse_method",
+ "options": [
+ {
+ "icon": "",
+ "label": {
+ "en_US": "auto",
+ "ja_JP": "auto",
+ "pt_BR": "auto",
+ "zh_Hans": "auto"
+ },
+ "value": "auto"
+ },
+ {
+ "icon": "",
+ "label": {
+ "en_US": "ocr",
+ "ja_JP": "ocr",
+ "pt_BR": "ocr",
+ "zh_Hans": "ocr"
+ },
+ "value": "ocr"
+ },
+ {
+ "icon": "",
+ "label": {
+ "en_US": "txt",
+ "ja_JP": "txt",
+ "pt_BR": "txt",
+ "zh_Hans": "txt"
+ },
+ "value": "txt"
+ }
+ ],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "select"
+ },
+ {
+ "auto_generate": null,
+ "default": 1,
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API and local deployment v2) Whether to enable formula recognition",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\u3068\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b",
+ "pt_BR": "(For official API and local deployment v2) Whether to enable formula recognition",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u662f\u5426\u5f00\u542f\u516c\u5f0f\u8bc6\u522b"
+ },
+ "label": {
+ "en_US": "Enable formula recognition",
+ "ja_JP": "\u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b",
+ "pt_BR": "Enable formula recognition",
+ "zh_Hans": "\u5f00\u542f\u516c\u5f0f\u8bc6\u522b"
+ },
+ "llm_description": "(For official API and local deployment v2) Whether to enable formula recognition",
+ "max": null,
+ "min": null,
+ "name": "enable_formula",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": 1,
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API and local deployment v2) Whether to enable table recognition",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\u3068\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b",
+ "pt_BR": "(For official API and local deployment v2) Whether to enable table recognition",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u662f\u5426\u5f00\u542f\u8868\u683c\u8bc6\u522b"
+ },
+ "label": {
+ "en_US": "Enable table recognition",
+ "ja_JP": "\u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b",
+ "pt_BR": "Enable table recognition",
+ "zh_Hans": "\u5f00\u542f\u8868\u683c\u8bc6\u522b"
+ },
+ "llm_description": "(For official API and local deployment v2) Whether to enable table recognition",
+ "max": null,
+ "min": null,
+ "name": "enable_table",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": "auto",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API and local deployment v2) Specify document language, default ch, can be set to auto(local deployment need to specify the language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5",
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+ "pt_BR": "(For official API and local deployment v2) Specify document language, default ch, can be set to auto(local deployment need to specify the language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5",
+ "zh_Hans": "\uff08\u4ec5\u9650\u5b98\u65b9api\u548c\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u6307\u5b9a\u6587\u6863\u8bed\u8a00\uff0c\u9ed8\u8ba4 ch\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3aauto\uff0c\u5f53\u4e3aauto\u65f6\u6a21\u578b\u4f1a\u81ea\u52a8\u8bc6\u522b\u6587\u6863\u8bed\u8a00\uff08\u672c\u5730\u90e8\u7f72\u9700\u8981\u6307\u5b9a\u660e\u786e\u7684\u8bed\u8a00\uff0c\u9ed8\u8ba4ch\uff09\uff0c\u5176\u4ed6\u53ef\u9009\u503c\u5217\u8868\u8be6\u89c1\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5"
+ },
+ "label": {
+ "en_US": "Document language",
+ "ja_JP": "\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e",
+ "pt_BR": "Document language",
+ "zh_Hans": "\u6587\u6863\u8bed\u8a00"
+ },
+ "llm_description": "(For official API and local deployment v2) Specify document language, default ch, can be set to auto(local deployment need to specify the language, default ch), other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/version3.x\/pipeline_usage\/OCR.html#5",
+ "max": null,
+ "min": null,
+ "name": "language",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Whether to enable OCR recognition",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b",
+ "pt_BR": "(For official API) Whether to enable OCR recognition",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542fOCR\u8bc6\u522b"
+ },
+ "label": {
+ "en_US": "Enable OCR recognition",
+ "ja_JP": "OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b",
+ "pt_BR": "Enable OCR recognition",
+ "zh_Hans": "\u5f00\u542fOCR\u8bc6\u522b"
+ },
+ "llm_description": "(For official API) Whether to enable OCR recognition",
+ "max": null,
+ "min": null,
+ "name": "enable_ocr",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": "[]",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09\u4f8b\uff1a[\"docx\",\"html\"]\u3001markdown\u3001json\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\u3067\u3042\u308a\u3001\u8a2d\u5b9a\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001docx\u3001html\u3001latex\u306e3\u3064\u306e\u5f62\u5f0f\u306e\u3044\u305a\u308c\u304b\u307e\u305f\u306f\u8907\u6570\u306e\u307f\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u307e\u3059",
+ "pt_BR": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u793a\u4f8b\uff1a[\"docx\",\"html\"],markdown\u3001json\u4e3a\u9ed8\u8ba4\u5bfc\u51fa\u683c\u5f0f\uff0c\u65e0\u987b\u8bbe\u7f6e\uff0c\u8be5\u53c2\u6570\u4ec5\u652f\u6301docx\u3001html\u3001latex\u4e09\u79cd\u683c\u5f0f\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a"
+ },
+ "label": {
+ "en_US": "Extra export formats",
+ "ja_JP": "\u8ffd\u52a0\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f",
+ "pt_BR": "Extra export formats",
+ "zh_Hans": "\u989d\u5916\u5bfc\u51fa\u683c\u5f0f"
+ },
+ "llm_description": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "max": null,
+ "min": null,
+ "name": "extra_formats",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": "pipeline",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For local deployment v2) Example: pipeline, vlm-transformers, vlm-sglang-engine, vlm-sglang-client, default is pipeline",
+ "ja_JP": "\uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528\uff09\u4f8b\uff1apipeline\u3001vlm-transformers\u3001vlm-sglang-engine\u3001vlm-sglang-client\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306fpipeline",
+ "pt_BR": "(For local deployment v2) Example: pipeline, vlm-transformers, vlm-sglang-engine, vlm-sglang-client, default is pipeline",
+ "zh_Hans": "\uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v2\u7248\u672c\uff09\u793a\u4f8b\uff1apipeline\u3001vlm-transformers\u3001vlm-sglang-engine\u3001vlm-sglang-client\uff0c\u9ed8\u8ba4\u503c\u4e3apipeline"
+ },
+ "label": {
+ "en_US": "Backend type",
+ "ja_JP": "\u30d0\u30c3\u30af\u30a8\u30f3\u30c9\u30bf\u30a4\u30d7",
+ "pt_BR": "Backend type",
+ "zh_Hans": "\u89e3\u6790\u540e\u7aef"
+ },
+ "llm_description": "(For local deployment v2) Example: pipeline, vlm-transformers, vlm-sglang-engine, vlm-sglang-client, default is pipeline",
+ "max": null,
+ "min": null,
+ "name": "backend",
+ "options": [
+ {
+ "icon": "",
+ "label": {
+ "en_US": "pipeline",
+ "ja_JP": "pipeline",
+ "pt_BR": "pipeline",
+ "zh_Hans": "pipeline"
+ },
+ "value": "pipeline"
+ },
+ {
+ "icon": "",
+ "label": {
+ "en_US": "vlm-transformers",
+ "ja_JP": "vlm-transformers",
+ "pt_BR": "vlm-transformers",
+ "zh_Hans": "vlm-transformers"
+ },
+ "value": "vlm-transformers"
+ },
+ {
+ "icon": "",
+ "label": {
+ "en_US": "vlm-sglang-engine",
+ "ja_JP": "vlm-sglang-engine",
+ "pt_BR": "vlm-sglang-engine",
+ "zh_Hans": "vlm-sglang-engine"
+ },
+ "value": "vlm-sglang-engine"
+ },
+ {
+ "icon": "",
+ "label": {
+ "en_US": "vlm-sglang-client",
+ "ja_JP": "vlm-sglang-client",
+ "pt_BR": "vlm-sglang-client",
+ "zh_Hans": "vlm-sglang-client"
+ },
+ "value": "vlm-sglang-client"
+ }
+ ],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "select"
+ },
+ {
+ "auto_generate": null,
+ "default": "",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For local deployment v2 when backend is vlm-sglang-client) Example: http:\/\/127.0.0.1:8000, default is empty",
+ "ja_JP": "\uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8v2\u7528 \u89e3\u6790\u5f8c\u7aef\u304cvlm-sglang-client\u306e\u5834\u5408\uff09\u4f8b\uff1ahttp:\/\/127.0.0.1:8000\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306f\u7a7a",
+ "pt_BR": "(For local deployment v2 when backend is vlm-sglang-client) Example: http:\/\/127.0.0.1:8000, default is empty",
+ "zh_Hans": "\uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72v2\u7248\u672c \u89e3\u6790\u540e\u7aef\u4e3avlm-sglang-client\u65f6\uff09\u793a\u4f8b\uff1ahttp:\/\/127.0.0.1:8000\uff0c\u9ed8\u8ba4\u503c\u4e3a\u7a7a"
+ },
+ "label": {
+ "en_US": "sglang-server url",
+ "ja_JP": "sglang-server\u30a2\u30c9\u30ec\u30b9",
+ "pt_BR": "sglang-server url",
+ "zh_Hans": "sglang-server\u5730\u5740"
+ },
+ "llm_description": "(For local deployment v2 when backend is vlm-sglang-client) Example: http:\/\/127.0.0.1:8000, default is empty",
+ "max": null,
+ "min": null,
+ "name": "sglang_server_url",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ }
+ ],
+ "params": {
+ "backend": "",
+ "enable_formula": "",
+ "enable_ocr": "",
+ "enable_table": "",
+ "extra_formats": "",
+ "file": "",
+ "language": "",
+ "parse_method": "",
+ "sglang_server_url": ""
+ },
+ "provider_id": "langgenius\/mineru\/mineru",
+ "provider_name": "langgenius\/mineru\/mineru",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Parse File",
+ "tool_configurations": {
+ "backend": {
+ "type": "constant",
+ "value": "pipeline"
+ },
+ "enable_formula": {
+ "type": "constant",
+ "value": 1
+ },
+ "enable_ocr": {
+ "type": "constant",
+ "value": true
+ },
+ "enable_table": {
+ "type": "constant",
+ "value": 1
+ },
+ "extra_formats": {
+ "type": "mixed",
+ "value": "[]"
+ },
+ "language": {
+ "type": "mixed",
+ "value": "auto"
+ },
+ "parse_method": {
+ "type": "constant",
+ "value": "auto"
+ },
+ "sglang_server_url": {
+ "type": "mixed",
+ "value": ""
+ }
+ },
+ "tool_description": "a tool for parsing text, tables, and images, supporting multiple formats such as pdf, pptx, docx, etc. supporting multiple languages such as English, Chinese, etc.",
+ "tool_label": "Parse File",
+ "tool_name": "parse-file",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "file": {
+ "type": "variable",
+ "value": [
+ "1756915693835",
+ "file"
+ ]
+ }
+ },
+ "type": "tool"
+ },
+ "height": 270,
+ "id": "1758027159239",
+ "position": {
+ "x": -544.9739996945534,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": -544.9739996945534,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ }
+ ],
+ "viewport": {
+ "x": 679.9701291615181,
+ "y": -191.49392257836791,
+ "zoom": 0.8239704766223018
+ }
+ },
+ "icon_info": {
+ "icon": "e642577f-da15-4c03-81b9-c9dec9189a3c",
+ "icon_background": null,
+ "icon_type": "image",
+ "icon_url": 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+ },
+ "id": "103825d3-7018-43ae-bcf0-f3c001f3eb69",
+ "name": "Contextual Enrichment Using LLM"
+},
+{
+ "chunk_structure": "hierarchical_model",
+ "description": "This Knowledge Pipeline extracts images and tables from complex PDF documents for downstream processing.",
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name: Complex PDF with Images & Tables\nversion: 0.1.0\nworkflow:\n conversation_variables: []\n environment_variables: []\n features: {}\n graph:\n edges:\n - data:\n isInLoop: false\n sourceType: datasource\n targetType: tool\n id: 1750400203722-source-1751281136356-target\n selected: false\n source: '1750400203722'\n sourceHandle: source\n target: '1751281136356'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: knowledge-index\n id: 1751338398711-source-1750400198569-target\n selected: false\n source: '1751338398711'\n sourceHandle: source\n target: '1750400198569'\n targetHandle: target\n type: custom\n zIndex: 0\n - data:\n isInLoop: false\n sourceType: tool\n targetType: tool\n id: 1751281136356-source-1751338398711-target\n selected: false\n source: '1751281136356'\n sourceHandle: source\n target: '1751338398711'\n targetHandle: target\n type: custom\n zIndex: 0\n nodes:\n - data:\n chunk_structure: hierarchical_model\n embedding_model: jina-embeddings-v2-base-en\n embedding_model_provider: langgenius\/jina\/jina\n index_chunk_variable_selector:\n - '1751338398711'\n - result\n indexing_technique: high_quality\n keyword_number: 10\n retrieval_model:\n reranking_enable: true\n reranking_mode: reranking_model\n reranking_model:\n reranking_model_name: jina-reranker-v1-base-en\n reranking_provider_name: langgenius\/jina\/jina\n score_threshold: 0\n score_threshold_enabled: false\n search_method: hybrid_search\n top_k: 3\n weights: null\n selected: true\n title: Knowledge Base\n type: knowledge-index\n height: 114\n id: '1750400198569'\n position:\n x: 355.92518399555183\n y: 282\n positionAbsolute:\n x: 355.92518399555183\n y: 282\n selected: true\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n datasource_configurations: {}\n datasource_label: File\n datasource_name: upload-file\n datasource_parameters: {}\n fileExtensions:\n - txt\n - markdown\n - mdx\n - pdf\n - html\n - xlsx\n - xls\n - vtt\n - properties\n - doc\n - docx\n - csv\n - eml\n - msg\n - pptx\n - xml\n - epub\n - ppt\n - md\n plugin_id: langgenius\/file\n provider_name: file\n provider_type: local_file\n selected: false\n title: File Upload\n type: datasource\n height: 52\n id: '1750400203722'\n position:\n x: -579\n y: 282\n positionAbsolute:\n x: -579\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n author: TenTen\n desc: ''\n height: 337\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Currently\n we support 4 types of \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Data\n Sources\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\":\n File Upload, Online Drive, Online Doc, and Web Crawler. Different types\n of Data Sources have different input and output types. The output of File\n Upload and Online Drive are files, while the output of Online Doc and WebCrawler\n are pages. You can find more Data Sources on our Marketplace.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n Knowledge Pipeline can have multiple data sources. Each data source can\n be selected more than once with different settings. Each added data source\n is a tab on the add file interface. However, each time the user can only\n select one data source to import the file and trigger its subsequent processing.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 358\n height: 337\n id: '1751264451381'\n position:\n x: -990.8091030156684\n y: 282\n positionAbsolute:\n x: -990.8091030156684\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 358\n - data:\n author: TenTen\n desc: ''\n height: 260\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Knowledge\n Pipeline\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n starts with Data Source as the starting node and ends with the knowledge\n base node. The general steps are: import documents from the data source\n \u2192 use extractor to extract document content \u2192 split and clean content into\n structured chunks \u2192 store in the knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n user input variables required by the Knowledge Pipeline node must be predefined\n and managed via the Input Field section located in the top-right corner\n of the orchestration canvas. It determines what input fields the end users\n will see and need to fill in when importing files to the knowledge base\n through this pipeline.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Unique\n Inputs: Input fields defined here are only available to the selected data\n source and its downstream nodes.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Global\n Inputs: These input fields are shared across all subsequent nodes after\n the data source and are typically set during the Process Documents step.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"For\n more information, see \",\"type\":\"text\",\"version\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https:\/\/docs.dify.ai\/en\/guides\/knowledge-base\/knowledge-pipeline\/knowledge-pipeline-orchestration.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"link\",\"version\":1,\"rel\":\"noreferrer\",\"target\":null,\"title\":null,\"url\":\"https:\/\/docs.dify.ai\/en\/guides\/knowledge-base\/knowledge-pipeline\/knowledge-pipeline-orchestration\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 1182\n height: 260\n id: '1751266376760'\n position:\n x: -579\n y: -22.64803881585007\n positionAbsolute:\n x: -579\n y: -22.64803881585007\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 1182\n - data:\n author: TenTen\n desc: ''\n height: 541\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"A\n document extractor for large language models (LLMs) like MinerU is a tool\n that preprocesses and converts diverse document types into structured, clean,\n and machine-readable data. This structured data can then be used to train\n or augment LLMs and retrieval-augmented generation (RAG) systems by providing\n them with accurate, well-organized content from varied sources. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"MinerU\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n is an advanced open-source document extractor designed specifically to convert\n complex, unstructured documents\u2014such as PDFs, Word files, and PPTs\u2014into\n high-quality, machine-readable formats like Markdown and JSON. MinerU addresses\n challenges in document parsing such as layout detection, formula recognition,\n and multi-language support, which are critical for generating high-quality\n training corpora for LLMs.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 541\n id: '1751266402561'\n position:\n x: -263.7680017647218\n y: 558.328085421591\n positionAbsolute:\n x: -263.7680017647218\n y: 558.328085421591\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 554\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Parent-Child\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\n addresses the dilemma of context and precision by leveraging a two-tier\n hierarchical approach that effectively balances the trade-off between accurate\n matching and comprehensive contextual information in RAG systems. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Here\n is the essential mechanism of this structured, two-level information access:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Query Matching with Child Chunks: Small, focused pieces of information,\n often as concise as a single sentence within a paragraph, are used to match\n the user''s query. These child chunks enable precise and relevant initial\n retrieval.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"-\n Contextual Enrichment with Parent Chunks: Larger, encompassing sections\u2014such\n as a paragraph, a section, or even an entire document\u2014that include the matched\n child chunks are then retrieved. These parent chunks provide comprehensive\n context for the Language Model (LLM).\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 554\n id: '1751266447821'\n position:\n x: 42.95253988413964\n y: 366.1915342509804\n positionAbsolute:\n x: 42.95253988413964\n y: 366.1915342509804\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n author: TenTen\n desc: ''\n height: 411\n selected: false\n showAuthor: true\n text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n knowledge base provides two indexing methods:\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0and\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Economical\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\",\n each with different retrieval strategies. High-Quality mode uses embeddings\n for vectorization and supports vector, full-text, and hybrid retrieval,\n offering more accurate results but higher resource usage. Economical mode\n uses keyword-based inverted indexing with no token consumption but lower\n accuracy; upgrading to High-Quality is possible, but downgrading requires\n creating a new knowledge base.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0,\"textStyle\":\"\"},{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"*\n Parent-Child Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0and\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Q&A\n Mode\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0only\n support the\u00a0\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"High-Quality\",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"\u00a0indexing\n method.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"start\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1,\"textStyle\":\"\"}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1,\"textFormat\":1}}'\n theme: blue\n title: ''\n type: ''\n width: 240\n height: 411\n id: '1751266580099'\n position:\n x: 355.92518399555183\n y: 434.6494699299023\n positionAbsolute:\n x: 355.92518399555183\n y: 434.6494699299023\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom-note\n width: 240\n - data:\n credential_id: fd1cbc33-1481-47ee-9af2-954b53d350e0\n is_team_authorization: false\n output_schema:\n properties:\n full_zip_url:\n description: The zip URL of the complete parsed result\n type: string\n images:\n description: The images extracted from the file\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n ja_JP: \u89e3\u6790\u3059\u308b\u30d5\u30a1\u30a4\u30eb(pdf\u3001ppt\u3001pptx\u3001doc\u3001docx\u3001png\u3001jpg\u3001jpeg\u3092\u30b5\u30dd\u30fc\u30c8)\n pt_BR: the file to be parsed(support pdf, ppt, pptx, doc, docx, png, jpg,\n jpeg)\n zh_Hans: \u7528\u4e8e\u89e3\u6790\u7684\u6587\u4ef6(\u652f\u6301 pdf, ppt, pptx, doc, docx, png, jpg, jpeg)\n label:\n en_US: file\n ja_JP: file\n pt_BR: file\n zh_Hans: file\n llm_description: the file to be parsed (support pdf, ppt, pptx, doc, docx,\n png, jpg, jpeg)\n max: null\n min: null\n name: file\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: file\n - auto_generate: null\n default: auto\n form: form\n human_description:\n en_US: (For local deployment service)Parsing method, can be auto, ocr,\n or txt. Default is auto. If results are not satisfactory, try ocr\n ja_JP: \uff08\u30ed\u30fc\u30ab\u30eb\u30c7\u30d7\u30ed\u30a4\u30e1\u30f3\u30c8\u30b5\u30fc\u30d3\u30b9\u7528\uff09\u89e3\u6790\u65b9\u6cd5\u306f\u3001auto\u3001ocr\u3001\u307e\u305f\u306ftxt\u306e\u3044\u305a\u308c\u304b\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fauto\u3067\u3059\u3002\u7d50\u679c\u304c\u6e80\u8db3\u3067\u304d\u306a\u3044\u5834\u5408\u306f\u3001ocr\u3092\u8a66\u3057\u3066\u304f\u3060\u3055\u3044\n pt_BR: (For local deployment service)Parsing method, can be auto, ocr,\n or txt. Default is auto. If results are not satisfactory, try ocr\n zh_Hans: \uff08\u7528\u4e8e\u672c\u5730\u90e8\u7f72\u670d\u52a1\uff09\u89e3\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u662fauto, ocr, \u6216 txt\u3002\u9ed8\u8ba4\u662fauto\u3002\u5982\u679c\u7ed3\u679c\u4e0d\u7406\u60f3\uff0c\u8bf7\u5c1d\u8bd5ocr\n label:\n en_US: parse method\n ja_JP: \u89e3\u6790\u65b9\u6cd5\n pt_BR: parse method\n zh_Hans: \u89e3\u6790\u65b9\u6cd5\n llm_description: Parsing method, can be auto, ocr, or txt. Default is auto.\n If results are not satisfactory, try ocr\n max: null\n min: null\n name: parse_method\n options:\n - label:\n en_US: auto\n ja_JP: auto\n pt_BR: auto\n zh_Hans: auto\n value: auto\n - label:\n en_US: ocr\n ja_JP: ocr\n pt_BR: ocr\n zh_Hans: ocr\n value: ocr\n - label:\n en_US: txt\n ja_JP: txt\n pt_BR: txt\n zh_Hans: txt\n value: txt\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: 1\n form: form\n human_description:\n en_US: (For official API) Whether to enable formula recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API) Whether to enable formula recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542f\u516c\u5f0f\u8bc6\u522b\n label:\n en_US: Enable formula recognition\n ja_JP: \u6570\u5f0f\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable formula recognition\n zh_Hans: \u5f00\u542f\u516c\u5f0f\u8bc6\u522b\n llm_description: (For official API) Whether to enable formula recognition\n max: null\n min: null\n name: enable_formula\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: 1\n form: form\n human_description:\n en_US: (For official API) Whether to enable table recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API) Whether to enable table recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542f\u8868\u683c\u8bc6\u522b\n label:\n en_US: Enable table recognition\n ja_JP: \u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable table recognition\n zh_Hans: \u5f00\u542f\u8868\u683c\u8bc6\u522b\n llm_description: (For official API) Whether to enable table recognition\n max: null\n min: null\n name: enable_table\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: doclayout_yolo\n form: form\n human_description:\n en_US: '(For official API) Optional values: doclayout_yolo, layoutlmv3,\n default value is doclayout_yolo. doclayout_yolo is a self-developed\n model with better effect'\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u30aa\u30d7\u30b7\u30e7\u30f3\u5024\uff1adoclayout_yolo\u3001layoutlmv3\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f doclayout_yolo\u3002doclayout_yolo\n \u306f\u81ea\u5df1\u958b\u767a\u30e2\u30c7\u30eb\u3067\u3001\u52b9\u679c\u304c\u3088\u308a\u826f\u3044\n pt_BR: '(For official API) Optional values: doclayout_yolo, layoutlmv3,\n default value is doclayout_yolo. doclayout_yolo is a self-developed\n model with better effect'\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u53ef\u9009\u503c\uff1adoclayout_yolo\u3001layoutlmv3\uff0c\u9ed8\u8ba4\u503c\u4e3a doclayout_yolo\u3002doclayout_yolo\n \u4e3a\u81ea\u7814\u6a21\u578b\uff0c\u6548\u679c\u66f4\u597d\n label:\n en_US: Layout model\n ja_JP: \u30ec\u30a4\u30a2\u30a6\u30c8\u691c\u51fa\u30e2\u30c7\u30eb\n pt_BR: Layout model\n zh_Hans: \u5e03\u5c40\u68c0\u6d4b\u6a21\u578b\n llm_description: '(For official API) Optional values: doclayout_yolo, layoutlmv3,\n default value is doclayout_yolo. doclayout_yolo is a self-developed model\n withbetter effect'\n max: null\n min: null\n name: layout_model\n options:\n - label:\n en_US: doclayout_yolo\n ja_JP: doclayout_yolo\n pt_BR: doclayout_yolo\n zh_Hans: doclayout_yolo\n value: doclayout_yolo\n - label:\n en_US: layoutlmv3\n ja_JP: layoutlmv3\n pt_BR: layoutlmv3\n zh_Hans: layoutlmv3\n value: layoutlmv3\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: auto\n form: form\n human_description:\n en_US: '(For official API) Specify document language, default ch, can\n be set to auto, when auto, the model will automatically identify document\n language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5'\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fch\u3067\u3001auto\u306b\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002auto\u306e\u5834\u5408\u3001\u30e2\u30c7\u30eb\u306f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u81ea\u52d5\u7684\u306b\u8b58\u5225\u3057\u307e\u3059\u3002\u4ed6\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u5024\u30ea\u30b9\u30c8\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5\n pt_BR: '(For official API) Specify document language, default ch, can\n be set to auto, when auto, the model will automatically identify document\n language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5'\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u6307\u5b9a\u6587\u6863\u8bed\u8a00\uff0c\u9ed8\u8ba4 ch\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3aauto\uff0c\u5f53\u4e3aauto\u65f6\u6a21\u578b\u4f1a\u81ea\u52a8\u8bc6\u522b\u6587\u6863\u8bed\u8a00\uff0c\u5176\u4ed6\u53ef\u9009\u503c\u5217\u8868\u8be6\u89c1\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5\n label:\n en_US: Document language\n ja_JP: \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\n pt_BR: Document language\n zh_Hans: \u6587\u6863\u8bed\u8a00\n llm_description: '(For official API) Specify document language, default\n ch, can be set to auto, when auto, the model will automatically identify\n document language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5'\n max: null\n min: null\n name: language\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 0\n form: form\n human_description:\n en_US: (For official API) Whether to enable OCR recognition\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b\n pt_BR: (For official API) Whether to enable OCR recognition\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542fOCR\u8bc6\u522b\n label:\n en_US: Enable OCR recognition\n ja_JP: OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\n pt_BR: Enable OCR recognition\n zh_Hans: \u5f00\u542fOCR\u8bc6\u522b\n llm_description: (For official API) Whether to enable OCR recognition\n max: null\n min: null\n name: enable_ocr\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: '[]'\n form: form\n human_description:\n en_US: '(For official API) Example: [\"docx\",\"html\"], markdown, json are\n the default export formats, no need to set, this parameter only supports\n one or more of docx, html, latex'\n ja_JP: \uff08\u516c\u5f0fAPI\u7528\uff09\u4f8b\uff1a[\"docx\",\"html\"]\u3001markdown\u3001json\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\u3067\u3042\u308a\u3001\u8a2d\u5b9a\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001docx\u3001html\u3001latex\u306e3\u3064\u306e\u5f62\u5f0f\u306e\u3044\u305a\u308c\u304b\u307e\u305f\u306f\u8907\u6570\u306e\u307f\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u307e\u3059\n pt_BR: '(For official API) Example: [\"docx\",\"html\"], markdown, json are\n the default export formats, no need to set, this parameter only supports\n one or more of docx, html, latex'\n zh_Hans: \uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u793a\u4f8b\uff1a[\"docx\",\"html\"],markdown\u3001json\u4e3a\u9ed8\u8ba4\u5bfc\u51fa\u683c\u5f0f\uff0c\u65e0\u987b\u8bbe\u7f6e\uff0c\u8be5\u53c2\u6570\u4ec5\u652f\u6301docx\u3001html\u3001latex\u4e09\u79cd\u683c\u5f0f\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a\n label:\n en_US: Extra export formats\n ja_JP: \u8ffd\u52a0\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\n pt_BR: Extra export formats\n zh_Hans: \u989d\u5916\u5bfc\u51fa\u683c\u5f0f\n llm_description: '(For official API) Example: [\"docx\",\"html\"], markdown,\n json are the default export formats, no need to set, this parameter only\n supports one or more of docx, html, latex'\n max: null\n min: null\n name: extra_formats\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n params:\n enable_formula: ''\n enable_ocr: ''\n enable_table: ''\n extra_formats: ''\n file: ''\n language: ''\n layout_model: ''\n parse_method: ''\n provider_id: langgenius\/mineru\/mineru\n provider_name: langgenius\/mineru\/mineru\n provider_type: builtin\n selected: false\n title: MinerU\n tool_configurations:\n enable_formula:\n type: constant\n value: 1\n enable_ocr:\n type: constant\n value: 0\n enable_table:\n type: constant\n value: 1\n extra_formats:\n type: constant\n value: '[]'\n language:\n type: constant\n value: auto\n layout_model:\n type: constant\n value: doclayout_yolo\n parse_method:\n type: constant\n value: auto\n tool_description: a tool for parsing text, tables, and images, supporting\n multiple formats such as pdf, pptx, docx, etc. supporting multiple languages\n such as English, Chinese, etc.\n tool_label: Parse File\n tool_name: parse-file\n tool_node_version: '2'\n tool_parameters:\n file:\n type: variable\n value:\n - '1750400203722'\n - file\n type: tool\n height: 244\n id: '1751281136356'\n position:\n x: -263.7680017647218\n y: 282\n positionAbsolute:\n x: -263.7680017647218\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n - data:\n is_team_authorization: true\n output_schema:\n properties:\n result:\n description: Parent child chunks result\n items:\n type: object\n type: array\n type: object\n paramSchemas:\n - auto_generate: null\n default: null\n form: llm\n human_description:\n en_US: ''\n ja_JP: ''\n pt_BR: ''\n zh_Hans: ''\n label:\n en_US: Input Content\n ja_JP: Input Content\n pt_BR: Conte\u00fado de Entrada\n zh_Hans: \u8f93\u5165\u6587\u672c\n llm_description: The text you want to chunk.\n max: null\n min: null\n name: input_text\n options: []\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: paragraph\n form: llm\n human_description:\n en_US: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n ja_JP: Split text into paragraphs based on separator and maximum chunk\n length, using split text as parent block or entire document as parent\n block and directly retrieve.\n pt_BR: Dividir texto em par\u00e1grafos com base no separador e no comprimento\n m\u00e1ximo do bloco, usando o texto dividido como bloco pai ou documento\n completo como bloco pai e diretamente recuper\u00e1-lo.\n zh_Hans: \u6839\u636e\u5206\u9694\u7b26\u548c\u6700\u5927\u5757\u957f\u5ea6\u5c06\u6587\u672c\u62c6\u5206\u4e3a\u6bb5\u843d\uff0c\u4f7f\u7528\u62c6\u5206\u6587\u672c\u4f5c\u4e3a\u68c0\u7d22\u7684\u7236\u5757\u6216\u6574\u4e2a\u6587\u6863\u7528\u4f5c\u7236\u5757\u5e76\u76f4\u63a5\u68c0\u7d22\u3002\n label:\n en_US: Parent Mode\n ja_JP: Parent Mode\n pt_BR: Modo Pai\n zh_Hans: \u7236\u5757\u6a21\u5f0f\n llm_description: Split text into paragraphs based on separator and maximum\n chunk length, using split text as parent block or entire document as parent\n block and directly retrieve.\n max: null\n min: null\n name: parent_mode\n options:\n - label:\n en_US: Paragraph\n ja_JP: Paragraph\n pt_BR: Par\u00e1grafo\n zh_Hans: \u6bb5\u843d\n value: paragraph\n - label:\n en_US: Full Document\n ja_JP: Full Document\n pt_BR: Documento Completo\n zh_Hans: \u5168\u6587\n value: full_doc\n placeholder: null\n precision: null\n required: true\n scope: null\n template: null\n type: select\n - auto_generate: null\n default: '\n\n\n '\n form: llm\n human_description:\n en_US: Separator used for chunking\n ja_JP: Separator used for chunking\n pt_BR: Separador usado para divis\u00e3o\n zh_Hans: \u7528\u4e8e\u5206\u5757\u7684\u5206\u9694\u7b26\n label:\n en_US: Parent Delimiter\n ja_JP: Parent Delimiter\n pt_BR: Separador de Pai\n zh_Hans: \u7236\u5757\u5206\u9694\u7b26\n llm_description: The separator used to split chunks\n max: null\n min: null\n name: separator\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 1024\n form: llm\n human_description:\n en_US: Maximum length for chunking\n ja_JP: Maximum length for chunking\n pt_BR: Comprimento m\u00e1ximo para divis\u00e3o\n zh_Hans: \u7528\u4e8e\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6\n label:\n en_US: Maximum Parent Chunk Length\n ja_JP: Maximum Parent Chunk Length\n pt_BR: Comprimento M\u00e1ximo do Bloco Pai\n zh_Hans: \u6700\u5927\u7236\u5757\u957f\u5ea6\n llm_description: Maximum length allowed per chunk\n max: null\n min: null\n name: max_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: '. '\n form: llm\n human_description:\n en_US: Separator used for subchunking\n ja_JP: Separator used for subchunking\n pt_BR: Separador usado para subdivis\u00e3o\n zh_Hans: \u7528\u4e8e\u5b50\u5206\u5757\u7684\u5206\u9694\u7b26\n label:\n en_US: Child Delimiter\n ja_JP: Child Delimiter\n pt_BR: Separador de Subdivis\u00e3o\n zh_Hans: \u5b50\u5206\u5757\u5206\u9694\u7b26\n llm_description: The separator used to split subchunks\n max: null\n min: null\n name: subchunk_separator\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: string\n - auto_generate: null\n default: 512\n form: llm\n human_description:\n en_US: Maximum length for subchunking\n ja_JP: Maximum length for subchunking\n pt_BR: Comprimento m\u00e1ximo para subdivis\u00e3o\n zh_Hans: \u7528\u4e8e\u5b50\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6\n label:\n en_US: Maximum Child Chunk Length\n ja_JP: Maximum Child Chunk Length\n pt_BR: Comprimento M\u00e1ximo de Subdivis\u00e3o\n zh_Hans: \u5b50\u5206\u5757\u6700\u5927\u957f\u5ea6\n llm_description: Maximum length allowed per subchunk\n max: null\n min: null\n name: subchunk_max_length\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: number\n - auto_generate: null\n default: 0\n form: llm\n human_description:\n en_US: Whether to remove consecutive spaces, newlines and tabs\n ja_JP: Whether to remove consecutive spaces, newlines and tabs\n pt_BR: Se deve remover espa\u00e7os extras no texto\n zh_Hans: \u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26\n label:\n en_US: Replace consecutive spaces, newlines and tabs\n ja_JP: Replace consecutive spaces, newlines and tabs\n pt_BR: Substituir espa\u00e7os consecutivos, novas linhas e guias\n zh_Hans: \u66ff\u6362\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26\n llm_description: Whether to remove consecutive spaces, newlines and tabs\n max: null\n min: null\n name: remove_extra_spaces\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n - auto_generate: null\n default: 0\n form: llm\n human_description:\n en_US: Whether to remove URLs and emails in the text\n ja_JP: Whether to remove URLs and emails in the text\n pt_BR: Se deve remover URLs e e-mails no texto\n zh_Hans: \u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\n label:\n en_US: Delete all URLs and email addresses\n ja_JP: Delete all URLs and email addresses\n pt_BR: Remover todas as URLs e e-mails\n zh_Hans: \u5220\u9664\u6240\u6709URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740\n llm_description: Whether to remove URLs and emails in the text\n max: null\n min: null\n name: remove_urls_emails\n options: []\n placeholder: null\n precision: null\n required: false\n scope: null\n template: null\n type: boolean\n params:\n input_text: ''\n max_length: ''\n parent_mode: ''\n remove_extra_spaces: ''\n remove_urls_emails: ''\n separator: ''\n subchunk_max_length: ''\n subchunk_separator: ''\n provider_id: langgenius\/parentchild_chunker\/parentchild_chunker\n provider_name: langgenius\/parentchild_chunker\/parentchild_chunker\n provider_type: builtin\n selected: false\n title: Parent-child Chunker\n tool_configurations: {}\n tool_description: Process documents into parent-child chunk structures\n tool_label: Parent-child Chunker\n tool_name: parentchild_chunker\n tool_node_version: '2'\n tool_parameters:\n input_text:\n type: mixed\n value: '{{#1751281136356.text#}}'\n max_length:\n type: variable\n value:\n - rag\n - shared\n - Maximum_Parent_Length\n parent_mode:\n type: variable\n value:\n - rag\n - shared\n - Parent_Mode\n remove_extra_spaces:\n type: variable\n value:\n - rag\n - shared\n - clean_1\n remove_urls_emails:\n type: variable\n value:\n - rag\n - shared\n - clean_2\n separator:\n type: mixed\n value: '{{#rag.shared.Parent_Delimiter#}}'\n subchunk_max_length:\n type: variable\n value:\n - rag\n - shared\n - Maximum_Child_Length\n subchunk_separator:\n type: mixed\n value: '{{#rag.shared.Child_Delimiter#}}'\n type: tool\n height: 52\n id: '1751338398711'\n position:\n x: 42.95253988413964\n y: 282\n positionAbsolute:\n x: 42.95253988413964\n y: 282\n selected: false\n sourcePosition: right\n targetPosition: left\n type: custom\n width: 242\n viewport:\n x: 628.3302331655243\n y: 120.08894361588159\n zoom: 0.7027501395646496\n rag_pipeline_variables:\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: paragraph\n label: Parent Mode\n max_length: 48\n options:\n - paragraph\n - full_doc\n placeholder: null\n required: true\n tooltips: 'Parent Mode provides two options: paragraph mode splits text into paragraphs\n as parent chunks for retrieval, while full_doc mode uses the entire document\n as a single parent chunk (text beyond 10,000 tokens will be truncated).'\n type: select\n unit: null\n variable: Parent_Mode\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: \\n\\n\n label: Parent Delimiter\n max_length: 48\n options: []\n placeholder: null\n required: false\n tooltips: A delimiter is the character used to separate text. \\n\\n is recommended\n for splitting the original document into large parent chunks. You can also use\n special delimiters defined by yourself.\n type: text-input\n unit: null\n variable: Parent_Delimiter\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: 1024\n label: Maximum Parent Length\n max_length: 48\n options: []\n placeholder: null\n required: false\n tooltips: null\n type: number\n unit: tokens\n variable: Maximum_Parent_Length\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: \\n\n label: Child Delimiter\n max_length: 48\n options: []\n placeholder: null\n required: true\n tooltips: A delimiter is the character used to separate text. \\n is recommended\n for splitting parent chunks into small child chunks. You can also use special\n delimiters defined by yourself.\n type: text-input\n unit: null\n variable: Child_Delimiter\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: 256\n label: Maximum Child Length\n max_length: 48\n options: []\n placeholder: null\n required: true\n tooltips: null\n type: number\n unit: tokens\n variable: Maximum_Child_Length\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: true\n label: Replace consecutive spaces, newlines and tabs.\n max_length: 48\n options: []\n placeholder: null\n required: true\n tooltips: null\n type: checkbox\n unit: null\n variable: clean_1\n - allow_file_extension: null\n allow_file_upload_methods: null\n allowed_file_types: null\n belong_to_node_id: shared\n default_value: null\n label: Delete all URLs and email addresses.\n max_length: 48\n options: []\n placeholder: null\n required: false\n tooltips: null\n type: checkbox\n unit: null\n variable: clean_2\n",
+ "graph": {
+ "edges": [
+ {
+ "data": {
+ "isInLoop": false,
+ "sourceType": "datasource",
+ "targetType": "tool"
+ },
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+ "en_US": "(For official API) Whether to enable table recognition",
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+ "pt_BR": "(For official API) Whether to enable table recognition",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542f\u8868\u683c\u8bc6\u522b"
+ },
+ "label": {
+ "en_US": "Enable table recognition",
+ "ja_JP": "\u8868\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b",
+ "pt_BR": "Enable table recognition",
+ "zh_Hans": "\u5f00\u542f\u8868\u683c\u8bc6\u522b"
+ },
+ "llm_description": "(For official API) Whether to enable table recognition",
+ "max": null,
+ "min": null,
+ "name": "enable_table",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": "doclayout_yolo",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Optional values: doclayout_yolo, layoutlmv3, default value is doclayout_yolo. doclayout_yolo is a self-developed model with better effect",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09\u30aa\u30d7\u30b7\u30e7\u30f3\u5024\uff1adoclayout_yolo\u3001layoutlmv3\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f doclayout_yolo\u3002doclayout_yolo \u306f\u81ea\u5df1\u958b\u767a\u30e2\u30c7\u30eb\u3067\u3001\u52b9\u679c\u304c\u3088\u308a\u826f\u3044",
+ "pt_BR": "(For official API) Optional values: doclayout_yolo, layoutlmv3, default value is doclayout_yolo. doclayout_yolo is a self-developed model with better effect",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u53ef\u9009\u503c\uff1adoclayout_yolo\u3001layoutlmv3\uff0c\u9ed8\u8ba4\u503c\u4e3a doclayout_yolo\u3002doclayout_yolo \u4e3a\u81ea\u7814\u6a21\u578b\uff0c\u6548\u679c\u66f4\u597d"
+ },
+ "label": {
+ "en_US": "Layout model",
+ "ja_JP": "\u30ec\u30a4\u30a2\u30a6\u30c8\u691c\u51fa\u30e2\u30c7\u30eb",
+ "pt_BR": "Layout model",
+ "zh_Hans": "\u5e03\u5c40\u68c0\u6d4b\u6a21\u578b"
+ },
+ "llm_description": "(For official API) Optional values: doclayout_yolo, layoutlmv3, default value is doclayout_yolo. doclayout_yolo is a self-developed model withbetter effect",
+ "max": null,
+ "min": null,
+ "name": "layout_model",
+ "options": [
+ {
+ "label": {
+ "en_US": "doclayout_yolo",
+ "ja_JP": "doclayout_yolo",
+ "pt_BR": "doclayout_yolo",
+ "zh_Hans": "doclayout_yolo"
+ },
+ "value": "doclayout_yolo"
+ },
+ {
+ "label": {
+ "en_US": "layoutlmv3",
+ "ja_JP": "layoutlmv3",
+ "pt_BR": "layoutlmv3",
+ "zh_Hans": "layoutlmv3"
+ },
+ "value": "layoutlmv3"
+ }
+ ],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "select"
+ },
+ {
+ "auto_generate": null,
+ "default": "auto",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Specify document language, default ch, can be set to auto, when auto, the model will automatically identify document language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306fch\u3067\u3001auto\u306b\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002auto\u306e\u5834\u5408\u3001\u30e2\u30c7\u30eb\u306f\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e\u3092\u81ea\u52d5\u7684\u306b\u8b58\u5225\u3057\u307e\u3059\u3002\u4ed6\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u5024\u30ea\u30b9\u30c8\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5",
+ "pt_BR": "(For official API) Specify document language, default ch, can be set to auto, when auto, the model will automatically identify document language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u6307\u5b9a\u6587\u6863\u8bed\u8a00\uff0c\u9ed8\u8ba4 ch\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u4e3aauto\uff0c\u5f53\u4e3aauto\u65f6\u6a21\u578b\u4f1a\u81ea\u52a8\u8bc6\u522b\u6587\u6863\u8bed\u8a00\uff0c\u5176\u4ed6\u53ef\u9009\u503c\u5217\u8868\u8be6\u89c1\uff1ahttps:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5"
+ },
+ "label": {
+ "en_US": "Document language",
+ "ja_JP": "\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u8a00\u8a9e",
+ "pt_BR": "Document language",
+ "zh_Hans": "\u6587\u6863\u8bed\u8a00"
+ },
+ "llm_description": "(For official API) Specify document language, default ch, can be set to auto, when auto, the model will automatically identify document language, other optional value list see: https:\/\/paddlepaddle.github.io\/PaddleOCR\/latest\/ppocr\/blog\/multi_languages.html#5",
+ "max": null,
+ "min": null,
+ "name": "language",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Whether to enable OCR recognition",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b\u304b\u3069\u3046\u304b",
+ "pt_BR": "(For official API) Whether to enable OCR recognition",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u662f\u5426\u5f00\u542fOCR\u8bc6\u522b"
+ },
+ "label": {
+ "en_US": "Enable OCR recognition",
+ "ja_JP": "OCR\u8a8d\u8b58\u3092\u6709\u52b9\u306b\u3059\u308b",
+ "pt_BR": "Enable OCR recognition",
+ "zh_Hans": "\u5f00\u542fOCR\u8bc6\u522b"
+ },
+ "llm_description": "(For official API) Whether to enable OCR recognition",
+ "max": null,
+ "min": null,
+ "name": "enable_ocr",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": "[]",
+ "form": "form",
+ "human_description": {
+ "en_US": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "ja_JP": "\uff08\u516c\u5f0fAPI\u7528\uff09\u4f8b\uff1a[\"docx\",\"html\"]\u3001markdown\u3001json\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f\u3067\u3042\u308a\u3001\u8a2d\u5b9a\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001docx\u3001html\u3001latex\u306e3\u3064\u306e\u5f62\u5f0f\u306e\u3044\u305a\u308c\u304b\u307e\u305f\u306f\u8907\u6570\u306e\u307f\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u307e\u3059",
+ "pt_BR": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "zh_Hans": "\uff08\u7528\u4e8e\u5b98\u65b9API\uff09\u793a\u4f8b\uff1a[\"docx\",\"html\"],markdown\u3001json\u4e3a\u9ed8\u8ba4\u5bfc\u51fa\u683c\u5f0f\uff0c\u65e0\u987b\u8bbe\u7f6e\uff0c\u8be5\u53c2\u6570\u4ec5\u652f\u6301docx\u3001html\u3001latex\u4e09\u79cd\u683c\u5f0f\u4e2d\u7684\u4e00\u4e2a\u6216\u591a\u4e2a"
+ },
+ "label": {
+ "en_US": "Extra export formats",
+ "ja_JP": "\u8ffd\u52a0\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u5f62\u5f0f",
+ "pt_BR": "Extra export formats",
+ "zh_Hans": "\u989d\u5916\u5bfc\u51fa\u683c\u5f0f"
+ },
+ "llm_description": "(For official API) Example: [\"docx\",\"html\"], markdown, json are the default export formats, no need to set, this parameter only supports one or more of docx, html, latex",
+ "max": null,
+ "min": null,
+ "name": "extra_formats",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ }
+ ],
+ "params": {
+ "enable_formula": "",
+ "enable_ocr": "",
+ "enable_table": "",
+ "extra_formats": "",
+ "file": "",
+ "language": "",
+ "layout_model": "",
+ "parse_method": ""
+ },
+ "provider_id": "langgenius\/mineru\/mineru",
+ "provider_name": "langgenius\/mineru\/mineru",
+ "provider_type": "builtin",
+ "selected": false,
+ "title": "MinerU",
+ "tool_configurations": {
+ "enable_formula": {
+ "type": "constant",
+ "value": 1
+ },
+ "enable_ocr": {
+ "type": "constant",
+ "value": 0
+ },
+ "enable_table": {
+ "type": "constant",
+ "value": 1
+ },
+ "extra_formats": {
+ "type": "constant",
+ "value": "[]"
+ },
+ "language": {
+ "type": "constant",
+ "value": "auto"
+ },
+ "layout_model": {
+ "type": "constant",
+ "value": "doclayout_yolo"
+ },
+ "parse_method": {
+ "type": "constant",
+ "value": "auto"
+ }
+ },
+ "tool_description": "a tool for parsing text, tables, and images, supporting multiple formats such as pdf, pptx, docx, etc. supporting multiple languages such as English, Chinese, etc.",
+ "tool_label": "Parse File",
+ "tool_name": "parse-file",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "file": {
+ "type": "variable",
+ "value": [
+ "1750400203722",
+ "file"
+ ]
+ }
+ },
+ "type": "tool"
+ },
+ "height": 244,
+ "id": "1751281136356",
+ "position": {
+ "x": -263.7680017647218,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": -263.7680017647218,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ },
+ {
+ "data": {
+ "is_team_authorization": true,
+ "output_schema": {
+ "properties": {
+ "result": {
+ "description": "Parent child chunks result",
+ "items": {
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+ },
+ "paramSchemas": [
+ {
+ "auto_generate": null,
+ "default": null,
+ "form": "llm",
+ "human_description": {
+ "en_US": "",
+ "ja_JP": "",
+ "pt_BR": "",
+ "zh_Hans": ""
+ },
+ "label": {
+ "en_US": "Input Content",
+ "ja_JP": "Input Content",
+ "pt_BR": "Conte\u00fado de Entrada",
+ "zh_Hans": "\u8f93\u5165\u6587\u672c"
+ },
+ "llm_description": "The text you want to chunk.",
+ "max": null,
+ "min": null,
+ "name": "input_text",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": "paragraph",
+ "form": "llm",
+ "human_description": {
+ "en_US": "Split text into paragraphs based on separator and maximum chunk length, using split text as parent block or entire document as parent block and directly retrieve.",
+ "ja_JP": "Split text into paragraphs based on separator and maximum chunk length, using split text as parent block or entire document as parent block and directly retrieve.",
+ "pt_BR": "Dividir texto em par\u00e1grafos com base no separador e no comprimento m\u00e1ximo do bloco, usando o texto dividido como bloco pai ou documento completo como bloco pai e diretamente recuper\u00e1-lo.",
+ "zh_Hans": "\u6839\u636e\u5206\u9694\u7b26\u548c\u6700\u5927\u5757\u957f\u5ea6\u5c06\u6587\u672c\u62c6\u5206\u4e3a\u6bb5\u843d\uff0c\u4f7f\u7528\u62c6\u5206\u6587\u672c\u4f5c\u4e3a\u68c0\u7d22\u7684\u7236\u5757\u6216\u6574\u4e2a\u6587\u6863\u7528\u4f5c\u7236\u5757\u5e76\u76f4\u63a5\u68c0\u7d22\u3002"
+ },
+ "label": {
+ "en_US": "Parent Mode",
+ "ja_JP": "Parent Mode",
+ "pt_BR": "Modo Pai",
+ "zh_Hans": "\u7236\u5757\u6a21\u5f0f"
+ },
+ "llm_description": "Split text into paragraphs based on separator and maximum chunk length, using split text as parent block or entire document as parent block and directly retrieve.",
+ "max": null,
+ "min": null,
+ "name": "parent_mode",
+ "options": [
+ {
+ "label": {
+ "en_US": "Paragraph",
+ "ja_JP": "Paragraph",
+ "pt_BR": "Par\u00e1grafo",
+ "zh_Hans": "\u6bb5\u843d"
+ },
+ "value": "paragraph"
+ },
+ {
+ "label": {
+ "en_US": "Full Document",
+ "ja_JP": "Full Document",
+ "pt_BR": "Documento Completo",
+ "zh_Hans": "\u5168\u6587"
+ },
+ "value": "full_doc"
+ }
+ ],
+ "placeholder": null,
+ "precision": null,
+ "required": true,
+ "scope": null,
+ "template": null,
+ "type": "select"
+ },
+ {
+ "auto_generate": null,
+ "default": "\n\n",
+ "form": "llm",
+ "human_description": {
+ "en_US": "Separator used for chunking",
+ "ja_JP": "Separator used for chunking",
+ "pt_BR": "Separador usado para divis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5206\u5757\u7684\u5206\u9694\u7b26"
+ },
+ "label": {
+ "en_US": "Parent Delimiter",
+ "ja_JP": "Parent Delimiter",
+ "pt_BR": "Separador de Pai",
+ "zh_Hans": "\u7236\u5757\u5206\u9694\u7b26"
+ },
+ "llm_description": "The separator used to split chunks",
+ "max": null,
+ "min": null,
+ "name": "separator",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": 1024,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Maximum length for chunking",
+ "ja_JP": "Maximum length for chunking",
+ "pt_BR": "Comprimento m\u00e1ximo para divis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6"
+ },
+ "label": {
+ "en_US": "Maximum Parent Chunk Length",
+ "ja_JP": "Maximum Parent Chunk Length",
+ "pt_BR": "Comprimento M\u00e1ximo do Bloco Pai",
+ "zh_Hans": "\u6700\u5927\u7236\u5757\u957f\u5ea6"
+ },
+ "llm_description": "Maximum length allowed per chunk",
+ "max": null,
+ "min": null,
+ "name": "max_length",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ },
+ {
+ "auto_generate": null,
+ "default": ". ",
+ "form": "llm",
+ "human_description": {
+ "en_US": "Separator used for subchunking",
+ "ja_JP": "Separator used for subchunking",
+ "pt_BR": "Separador usado para subdivis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5b50\u5206\u5757\u7684\u5206\u9694\u7b26"
+ },
+ "label": {
+ "en_US": "Child Delimiter",
+ "ja_JP": "Child Delimiter",
+ "pt_BR": "Separador de Subdivis\u00e3o",
+ "zh_Hans": "\u5b50\u5206\u5757\u5206\u9694\u7b26"
+ },
+ "llm_description": "The separator used to split subchunks",
+ "max": null,
+ "min": null,
+ "name": "subchunk_separator",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "string"
+ },
+ {
+ "auto_generate": null,
+ "default": 512,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Maximum length for subchunking",
+ "ja_JP": "Maximum length for subchunking",
+ "pt_BR": "Comprimento m\u00e1ximo para subdivis\u00e3o",
+ "zh_Hans": "\u7528\u4e8e\u5b50\u5206\u5757\u7684\u6700\u5927\u957f\u5ea6"
+ },
+ "label": {
+ "en_US": "Maximum Child Chunk Length",
+ "ja_JP": "Maximum Child Chunk Length",
+ "pt_BR": "Comprimento M\u00e1ximo de Subdivis\u00e3o",
+ "zh_Hans": "\u5b50\u5206\u5757\u6700\u5927\u957f\u5ea6"
+ },
+ "llm_description": "Maximum length allowed per subchunk",
+ "max": null,
+ "min": null,
+ "name": "subchunk_max_length",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "number"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove consecutive spaces, newlines and tabs",
+ "ja_JP": "Whether to remove consecutive spaces, newlines and tabs",
+ "pt_BR": "Se deve remover espa\u00e7os extras no texto",
+ "zh_Hans": "\u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26"
+ },
+ "label": {
+ "en_US": "Replace consecutive spaces, newlines and tabs",
+ "ja_JP": "Replace consecutive spaces, newlines and tabs",
+ "pt_BR": "Substituir espa\u00e7os consecutivos, novas linhas e guias",
+ "zh_Hans": "\u66ff\u6362\u8fde\u7eed\u7a7a\u683c\u3001\u6362\u884c\u7b26\u548c\u5236\u8868\u7b26"
+ },
+ "llm_description": "Whether to remove consecutive spaces, newlines and tabs",
+ "max": null,
+ "min": null,
+ "name": "remove_extra_spaces",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
+ "scope": null,
+ "template": null,
+ "type": "boolean"
+ },
+ {
+ "auto_generate": null,
+ "default": 0,
+ "form": "llm",
+ "human_description": {
+ "en_US": "Whether to remove URLs and emails in the text",
+ "ja_JP": "Whether to remove URLs and emails in the text",
+ "pt_BR": "Se deve remover URLs e e-mails no texto",
+ "zh_Hans": "\u662f\u5426\u79fb\u9664\u6587\u672c\u4e2d\u7684URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740"
+ },
+ "label": {
+ "en_US": "Delete all URLs and email addresses",
+ "ja_JP": "Delete all URLs and email addresses",
+ "pt_BR": "Remover todas as URLs e e-mails",
+ "zh_Hans": "\u5220\u9664\u6240\u6709URL\u548c\u7535\u5b50\u90ae\u4ef6\u5730\u5740"
+ },
+ "llm_description": "Whether to remove URLs and emails in the text",
+ "max": null,
+ "min": null,
+ "name": "remove_urls_emails",
+ "options": [],
+ "placeholder": null,
+ "precision": null,
+ "required": false,
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+ }
+ ],
+ "params": {
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+ "remove_extra_spaces": "",
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+ "provider_type": "builtin",
+ "selected": false,
+ "title": "Parent-child Chunker",
+ "tool_configurations": {},
+ "tool_description": "Process documents into parent-child chunk structures",
+ "tool_label": "Parent-child Chunker",
+ "tool_name": "parentchild_chunker",
+ "tool_node_version": "2",
+ "tool_parameters": {
+ "input_text": {
+ "type": "mixed",
+ "value": "{{#1751281136356.text#}}"
+ },
+ "max_length": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "Maximum_Parent_Length"
+ ]
+ },
+ "parent_mode": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "Parent_Mode"
+ ]
+ },
+ "remove_extra_spaces": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "clean_1"
+ ]
+ },
+ "remove_urls_emails": {
+ "type": "variable",
+ "value": [
+ "rag",
+ "shared",
+ "clean_2"
+ ]
+ },
+ "separator": {
+ "type": "mixed",
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+ }
+ },
+ "type": "tool"
+ },
+ "height": 52,
+ "id": "1751338398711",
+ "position": {
+ "x": 42.95253988413964,
+ "y": 282
+ },
+ "positionAbsolute": {
+ "x": 42.95253988413964,
+ "y": 282
+ },
+ "selected": false,
+ "sourcePosition": "right",
+ "targetPosition": "left",
+ "type": "custom",
+ "width": 242
+ }
+ ],
+ "viewport": {
+ "x": 628.3302331655243,
+ "y": 120.08894361588159,
+ "zoom": 0.7027501395646496
+ }
+ },
+ "icon_info": {
+ "icon": "87426868-91d6-4774-a535-5fd4595a77b3",
+ "icon_background": null,
+ "icon_type": "image",
+ "icon_url": 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"
+ },
+ "id": "629cb5b8-490a-48bc-808b-ffc13085cb4f",
+ "name": "Complex PDF with Images & Tables"
+}
+ }
+}
\ No newline at end of file
diff --git a/api/controllers/common/fields.py b/api/controllers/common/fields.py
index df9de825de..c16a23fac8 100644
--- a/api/controllers/common/fields.py
+++ b/api/controllers/common/fields.py
@@ -1,62 +1,59 @@
-from flask_restx import Api, Namespace, fields
+from __future__ import annotations
-from libs.helper import AppIconUrlField
+from typing import Any, TypeAlias
-parameters__system_parameters = {
- "image_file_size_limit": fields.Integer,
- "video_file_size_limit": fields.Integer,
- "audio_file_size_limit": fields.Integer,
- "file_size_limit": fields.Integer,
- "workflow_file_upload_limit": fields.Integer,
-}
+from pydantic import BaseModel, ConfigDict, computed_field
+
+from core.file import helpers as file_helpers
+from models.model import IconType
+
+JSONValue: TypeAlias = str | int | float | bool | None | dict[str, Any] | list[Any]
+JSONObject: TypeAlias = dict[str, Any]
-def build_system_parameters_model(api_or_ns: Api | Namespace):
- """Build the system parameters model for the API or Namespace."""
- return api_or_ns.model("SystemParameters", parameters__system_parameters)
+class SystemParameters(BaseModel):
+ image_file_size_limit: int
+ video_file_size_limit: int
+ audio_file_size_limit: int
+ file_size_limit: int
+ workflow_file_upload_limit: int
-parameters_fields = {
- "opening_statement": fields.String,
- "suggested_questions": fields.Raw,
- "suggested_questions_after_answer": fields.Raw,
- "speech_to_text": fields.Raw,
- "text_to_speech": fields.Raw,
- "retriever_resource": fields.Raw,
- "annotation_reply": fields.Raw,
- "more_like_this": fields.Raw,
- "user_input_form": fields.Raw,
- "sensitive_word_avoidance": fields.Raw,
- "file_upload": fields.Raw,
- "system_parameters": fields.Nested(parameters__system_parameters),
-}
+class Parameters(BaseModel):
+ opening_statement: str | None = None
+ suggested_questions: list[str]
+ suggested_questions_after_answer: JSONObject
+ speech_to_text: JSONObject
+ text_to_speech: JSONObject
+ retriever_resource: JSONObject
+ annotation_reply: JSONObject
+ more_like_this: JSONObject
+ user_input_form: list[JSONObject]
+ sensitive_word_avoidance: JSONObject
+ file_upload: JSONObject
+ system_parameters: SystemParameters
-def build_parameters_model(api_or_ns: Api | Namespace):
- """Build the parameters model for the API or Namespace."""
- copied_fields = parameters_fields.copy()
- copied_fields["system_parameters"] = fields.Nested(build_system_parameters_model(api_or_ns))
- return api_or_ns.model("Parameters", copied_fields)
+class Site(BaseModel):
+ model_config = ConfigDict(from_attributes=True)
+ title: str
+ chat_color_theme: str | None = None
+ chat_color_theme_inverted: bool
+ icon_type: str | None = None
+ icon: str | None = None
+ icon_background: str | None = None
+ description: str | None = None
+ copyright: str | None = None
+ privacy_policy: str | None = None
+ custom_disclaimer: str | None = None
+ default_language: str
+ show_workflow_steps: bool
+ use_icon_as_answer_icon: bool
-site_fields = {
- "title": fields.String,
- "chat_color_theme": fields.String,
- "chat_color_theme_inverted": fields.Boolean,
- "icon_type": fields.String,
- "icon": fields.String,
- "icon_background": fields.String,
- "icon_url": AppIconUrlField,
- "description": fields.String,
- "copyright": fields.String,
- "privacy_policy": fields.String,
- "custom_disclaimer": fields.String,
- "default_language": fields.String,
- "show_workflow_steps": fields.Boolean,
- "use_icon_as_answer_icon": fields.Boolean,
-}
-
-
-def build_site_model(api_or_ns: Api | Namespace):
- """Build the site model for the API or Namespace."""
- return api_or_ns.model("Site", site_fields)
+ @computed_field(return_type=str | None) # type: ignore
+ @property
+ def icon_url(self) -> str | None:
+ if self.icon and self.icon_type == IconType.IMAGE:
+ return file_helpers.get_signed_file_url(self.icon)
+ return None
diff --git a/api/controllers/common/file_response.py b/api/controllers/common/file_response.py
new file mode 100644
index 0000000000..ca8ea3d52e
--- /dev/null
+++ b/api/controllers/common/file_response.py
@@ -0,0 +1,57 @@
+import os
+from email.message import Message
+from urllib.parse import quote
+
+from flask import Response
+
+HTML_MIME_TYPES = frozenset({"text/html", "application/xhtml+xml"})
+HTML_EXTENSIONS = frozenset({"html", "htm"})
+
+
+def _normalize_mime_type(mime_type: str | None) -> str:
+ if not mime_type:
+ return ""
+ message = Message()
+ message["Content-Type"] = mime_type
+ return message.get_content_type().strip().lower()
+
+
+def _is_html_extension(extension: str | None) -> bool:
+ if not extension:
+ return False
+ return extension.lstrip(".").lower() in HTML_EXTENSIONS
+
+
+def is_html_content(mime_type: str | None, filename: str | None, extension: str | None = None) -> bool:
+ normalized_mime_type = _normalize_mime_type(mime_type)
+ if normalized_mime_type in HTML_MIME_TYPES:
+ return True
+
+ if _is_html_extension(extension):
+ return True
+
+ if filename:
+ return _is_html_extension(os.path.splitext(filename)[1])
+
+ return False
+
+
+def enforce_download_for_html(
+ response: Response,
+ *,
+ mime_type: str | None,
+ filename: str | None,
+ extension: str | None = None,
+) -> bool:
+ if not is_html_content(mime_type, filename, extension):
+ return False
+
+ if filename:
+ encoded_filename = quote(filename)
+ response.headers["Content-Disposition"] = f"attachment; filename*=UTF-8''{encoded_filename}"
+ else:
+ response.headers["Content-Disposition"] = "attachment"
+
+ response.headers["Content-Type"] = "application/octet-stream"
+ response.headers["X-Content-Type-Options"] = "nosniff"
+ return True
diff --git a/api/controllers/common/schema.py b/api/controllers/common/schema.py
new file mode 100644
index 0000000000..e0896a8dc2
--- /dev/null
+++ b/api/controllers/common/schema.py
@@ -0,0 +1,26 @@
+"""Helpers for registering Pydantic models with Flask-RESTX namespaces."""
+
+from flask_restx import Namespace
+from pydantic import BaseModel
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+def register_schema_model(namespace: Namespace, model: type[BaseModel]) -> None:
+ """Register a single BaseModel with a namespace for Swagger documentation."""
+
+ namespace.schema_model(model.__name__, model.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+
+
+def register_schema_models(namespace: Namespace, *models: type[BaseModel]) -> None:
+ """Register multiple BaseModels with a namespace."""
+
+ for model in models:
+ register_schema_model(namespace, model)
+
+
+__all__ = [
+ "DEFAULT_REF_TEMPLATE_SWAGGER_2_0",
+ "register_schema_model",
+ "register_schema_models",
+]
diff --git a/api/controllers/console/admin.py b/api/controllers/console/admin.py
index 2c4d8709eb..a25ca5ef51 100644
--- a/api/controllers/console/admin.py
+++ b/api/controllers/console/admin.py
@@ -3,21 +3,47 @@ from functools import wraps
from typing import ParamSpec, TypeVar
from flask import request
-from flask_restx import Resource, fields, reqparse
+from flask_restx import Resource
+from pydantic import BaseModel, Field, field_validator
from sqlalchemy import select
-from sqlalchemy.orm import Session
from werkzeug.exceptions import NotFound, Unauthorized
-P = ParamSpec("P")
-R = TypeVar("R")
from configs import dify_config
from constants.languages import supported_language
-from controllers.console import api, console_ns
+from controllers.console import console_ns
from controllers.console.wraps import only_edition_cloud
+from core.db.session_factory import session_factory
from extensions.ext_database import db
from libs.token import extract_access_token
from models.model import App, InstalledApp, RecommendedApp
+P = ParamSpec("P")
+R = TypeVar("R")
+
+DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
+
+
+class InsertExploreAppPayload(BaseModel):
+ app_id: str = Field(...)
+ desc: str | None = None
+ copyright: str | None = None
+ privacy_policy: str | None = None
+ custom_disclaimer: str | None = None
+ language: str = Field(...)
+ category: str = Field(...)
+ position: int = Field(...)
+
+ @field_validator("language")
+ @classmethod
+ def validate_language(cls, value: str) -> str:
+ return supported_language(value)
+
+
+console_ns.schema_model(
+ InsertExploreAppPayload.__name__,
+ InsertExploreAppPayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+)
+
def admin_required(view: Callable[P, R]):
@wraps(view)
@@ -38,61 +64,36 @@ def admin_required(view: Callable[P, R]):
@console_ns.route("/admin/insert-explore-apps")
class InsertExploreAppListApi(Resource):
- @api.doc("insert_explore_app")
- @api.doc(description="Insert or update an app in the explore list")
- @api.expect(
- api.model(
- "InsertExploreAppRequest",
- {
- "app_id": fields.String(required=True, description="Application ID"),
- "desc": fields.String(description="App description"),
- "copyright": fields.String(description="Copyright information"),
- "privacy_policy": fields.String(description="Privacy policy"),
- "custom_disclaimer": fields.String(description="Custom disclaimer"),
- "language": fields.String(required=True, description="Language code"),
- "category": fields.String(required=True, description="App category"),
- "position": fields.Integer(required=True, description="Display position"),
- },
- )
- )
- @api.response(200, "App updated successfully")
- @api.response(201, "App inserted successfully")
- @api.response(404, "App not found")
+ @console_ns.doc("insert_explore_app")
+ @console_ns.doc(description="Insert or update an app in the explore list")
+ @console_ns.expect(console_ns.models[InsertExploreAppPayload.__name__])
+ @console_ns.response(200, "App updated successfully")
+ @console_ns.response(201, "App inserted successfully")
+ @console_ns.response(404, "App not found")
@only_edition_cloud
@admin_required
def post(self):
- parser = (
- reqparse.RequestParser()
- .add_argument("app_id", type=str, required=True, nullable=False, location="json")
- .add_argument("desc", type=str, location="json")
- .add_argument("copyright", type=str, location="json")
- .add_argument("privacy_policy", type=str, location="json")
- .add_argument("custom_disclaimer", type=str, location="json")
- .add_argument("language", type=supported_language, required=True, nullable=False, location="json")
- .add_argument("category", type=str, required=True, nullable=False, location="json")
- .add_argument("position", type=int, required=True, nullable=False, location="json")
- )
- args = parser.parse_args()
+ payload = InsertExploreAppPayload.model_validate(console_ns.payload)
- app = db.session.execute(select(App).where(App.id == args["app_id"])).scalar_one_or_none()
+ app = db.session.execute(select(App).where(App.id == payload.app_id)).scalar_one_or_none()
if not app:
- raise NotFound(f"App '{args['app_id']}' is not found")
+ raise NotFound(f"App '{payload.app_id}' is not found")
site = app.site
if not site:
- desc = args["desc"] or ""
- copy_right = args["copyright"] or ""
- privacy_policy = args["privacy_policy"] or ""
- custom_disclaimer = args["custom_disclaimer"] or ""
+ desc = payload.desc or ""
+ copy_right = payload.copyright or ""
+ privacy_policy = payload.privacy_policy or ""
+ custom_disclaimer = payload.custom_disclaimer or ""
else:
- desc = site.description or args["desc"] or ""
- copy_right = site.copyright or args["copyright"] or ""
- privacy_policy = site.privacy_policy or args["privacy_policy"] or ""
- custom_disclaimer = site.custom_disclaimer or args["custom_disclaimer"] or ""
+ desc = site.description or payload.desc or ""
+ copy_right = site.copyright or payload.copyright or ""
+ privacy_policy = site.privacy_policy or payload.privacy_policy or ""
+ custom_disclaimer = site.custom_disclaimer or payload.custom_disclaimer or ""
- with Session(db.engine) as session:
+ with session_factory.create_session() as session:
recommended_app = session.execute(
- select(RecommendedApp).where(RecommendedApp.app_id == args["app_id"])
+ select(RecommendedApp).where(RecommendedApp.app_id == payload.app_id)
).scalar_one_or_none()
if not recommended_app:
@@ -102,9 +103,9 @@ class InsertExploreAppListApi(Resource):
copyright=copy_right,
privacy_policy=privacy_policy,
custom_disclaimer=custom_disclaimer,
- language=args["language"],
- category=args["category"],
- position=args["position"],
+ language=payload.language,
+ category=payload.category,
+ position=payload.position,
)
db.session.add(recommended_app)
@@ -118,9 +119,9 @@ class InsertExploreAppListApi(Resource):
recommended_app.copyright = copy_right
recommended_app.privacy_policy = privacy_policy
recommended_app.custom_disclaimer = custom_disclaimer
- recommended_app.language = args["language"]
- recommended_app.category = args["category"]
- recommended_app.position = args["position"]
+ recommended_app.language = payload.language
+ recommended_app.category = payload.category
+ recommended_app.position = payload.position
app.is_public = True
@@ -131,14 +132,14 @@ class InsertExploreAppListApi(Resource):
@console_ns.route("/admin/insert-explore-apps/