### What problem does this PR solve?
Closes#15048.
Several SDK session routes in `api/apps/sdk/session.py` called
`.split()` directly on `request.headers.get("Authorization")`. When
clients omitted the header, the handlers raised `AttributeError` before
returning the existing `Authorization is not valid!` response.
This PR centralizes SDK Authorization parsing in a small helper and
keeps the existing error response for missing, empty, or malformed
headers.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Tests
- `ZHIPU_AI_API_KEY=dummy uv run --python 3.13 --group test pytest
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py::test_sdk_session_routes_missing_authorization_unit
-q`
- `uv run --python 3.13 --group test ruff check api/apps/sdk/session.py
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
- `python3 -m py_compile api/apps/sdk/session.py
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
- `git diff --check`
## What problem does this PR solve?
Closes#13384.
The `/api/v1/agentbots/<agent_id>/completions` non-streaming path
returned the first yielded SSE chunk and exited:
```python
async for answer in agent_completion(objs[0].tenant_id, agent_id, **req):
return get_result(data=answer)
```
That meant structured output, the full assistant message, and reference
data were all dropped when an agent was called with `stream=false`.
Streaming worked because each event was forwarded individually;
non-streaming was returning a raw SSE-formatted string from a single
early event.
The v1 endpoint at
[`agent_api.py:1006-1050`](https://github.com/infiniflow/ragflow/blob/main/api/apps/restful_apis/agent_api.py#L1006-L1050)
already handles this correctly. This PR mirrors that aggregation in the
SDK beta endpoint: parse each SSE line, accumulate `content` from
`message` events, merge `reference`, collect `outputs.structured` from
each `node_finished` event keyed by `component_id`, and attach all of
them to the final response.
## Type of change
- [x] Bug fix (non-breaking change which fixes an issue)
## Test plan
- [ ] Build an agent with a node that emits structured output, call
`POST /api/v1/agentbots/<agent_id>/completions` with `stream=false` and
a beta API token, verify `data.structured.<component_id>` is present in
the response.
- [ ] Same agent with `stream=true` — verify behavior is unchanged.
- [ ] Agent without structured output — verify `data.structured` is
omitted, `content` and `reference` still aggregated correctly.
### Related issues
Closes#14781
### What problem does this PR solve?
Some retrieval endpoints accepted caller-supplied `tenant_rerank_id` and
resolved it through `get_model_config_by_id(...)`. That helper loaded
`TenantLLM` rows by global database id and returned decoded model
configuration without checking whether the model belonged to the
authenticated tenant or the dataset owner tenant.
This meant dataset access was validated, but rerank-model selection was
not. A caller who knew or could guess another tenant's
`tenant_rerank_id` could attempt retrieval with a foreign rerank model
config, creating a cross-tenant authorization gap for model usage.
This PR closes that gap by making `tenant_rerank_id` resolution
tenant-aware across the retrieval paths that accept it.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### Solution
- Extend `get_model_config_by_id(...)` to accept an optional
`allowed_tenant_ids` set and reject `TenantLLM` rows whose `tenant_id`
is outside that set.
- Pass the allowed tenant scope from retrieval endpoints that accept
`tenant_rerank_id`:
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- `api/apps/services/dataset_api_service.py`
- Use the authenticated tenant plus dataset-owner tenant ids already
derived by each retrieval flow as the authorization boundary for rerank
model selection.
- Add focused unit coverage to assert unauthorized `tenant_rerank_id`
values are rejected and that the allowed tenant set is propagated
correctly.
### Testing
- `python -m py_compile` on:
- `api/db/joint_services/tenant_model_service.py`
- `api/apps/services/dataset_api_service.py`
- `api/apps/sdk/doc.py`
- `api/apps/sdk/session.py`
- Added unit tests in:
-
`test/testcases/test_http_api/test_file_management_within_dataset/test_doc_sdk_routes_unit.py`
-
`test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
### Notes for reviewers
- This change is intentionally narrow: it affects only the
`tenant_rerank_id` path, not the normal `rerank_id` name-based
resolution path.
- Local lint/syntax checks passed.
- Full pytest execution could not be completed in this environment
because the local test runtime is missing `strenum`, so the route-test
files fail during collection before exercising the updated cases.
---------
Co-authored-by: jony376 <jony376@gmail.com>
### What problem does this PR solve?
Addresses event-loop blocking under high concurrency reported in #13825.
When multiple requests hit the API simultaneously, synchronous DB/Redis
calls block the async event loop, preventing Quart from handling other
requests and causing cascading 502/504 timeouts.
This PR wraps all remaining blocking DB/Redis calls in `canvas_app.py`,
`chat_api.py`, `session.py`, and `canvas_service.py` with `await
thread_pool_exec()`
- Offload all synchronous `Service.*`, `REDIS_CONN.*`, and
`APIToken.query` calls to the thread pool
- Convert sync endpoint handlers (`list_chats`, `get_chat`, `templates`,
`sessions`, etc.) to `async def`
- Convert sync helper functions (`_ensure_owned_chat`,
`_validate_llm_id`, `_validate_dataset_ids`, etc.) to async - no
duplicate sync/async pairs
- Wrap `CanvasReplicaService` Redis IO calls (`bootstrap`,
`replace_for_set`, `commit_after_run`)
- Use `asyncio.gather()` for concurrent file uploads and chat response
building
**Note:** This fixes the code-level event-loop blocking, which is a
prerequisite for handling concurrent requests. For the full "30
concurrent requests without 502/504" goal described in the issue, users
should also tune deployment config:
- `WS=4` or higher (HTTP worker processes, default 1)
- `MAX_CONCURRENT_CHATS=50` (default 10)
- `SANDBOX_EXECUTOR_MANAGER_POOL_SIZE` for workflow-heavy workloads
### Performance verification
Reviewer asked for a before-vs-after comparison
([comment](https://github.com/infiniflow/ragflow/pull/13941#issuecomment-4393667231)).
I built a self-contained microbenchmark that reproduces the exact
failure mode this PR targets: an async handler that performs blocking
DB/Redis-style calls (50 ms each, 3 per request, 30 concurrent requests)
is run twice — once with the pre-PR pattern (sync call directly inside
the async handler) and once with the post-PR pattern (`await
thread_pool_exec(...)`). The benchmark imports nothing from RAGFlow
except `thread_pool_exec` itself, so it is hermetic and reproducible
(`THREAD_POOL_MAX_WORKERS=128`, Python 3.13.12).
**Throughput — wall-clock for 30 concurrent requests (lower is better)**
| flavour | wall(s) | p50(s) | p95(s) | max(s) |
|---|---:|---:|---:|---:|
| before | 4.986 | 0.158 | 0.207 | 0.269 |
| after | 0.248 | 0.181 | 0.230 | 0.231 |
The pre-PR handler serializes the entire load on the event-loop thread,
so 30 × 3 × 50 ms ≈ 4.5 s shows up as the wall time. The post-PR handler
parallelizes the blocking work across the thread pool and finishes the
same load in 248 ms — a **~20× speedup** on this workload.
**Event-loop responsiveness — latency of an unrelated probe coroutine
while the 30 slow requests are running (lower is better)**
| flavour | samples | probe p50 (ms) | probe p95 (ms) | probe max (ms) |
|---|---:|---:|---:|---:|
| before | 1 | 5442.26 | 5442.26 | 5442.26 |
| after | 28 | 0.88 | 11.53 | 98.02 |
This is the metric that maps directly to "the API still answers other
requests while one is busy". A 5 ms-interval probe was scheduled while
the 30 slow handlers ran. With the pre-PR code the event loop was frozen
for the entire duration of the blocking work, so only one probe sample
was ever picked up and it waited **5,442 ms**. After the PR, 28 probe
samples landed with **p50 0.88 ms / p95 11.53 ms**, meaning unrelated
requests are no longer starved by the slow ones. That is the regression
mode behind the cascading 502/504s reported in #13825.
<details>
<summary>Raw benchmark output</summary>
```
config: 30 concurrent requests, 3 blocking calls of 50ms each per request, THREAD_POOL_MAX_WORKERS=128
=== Throughput (lower wall is better) ===
flavour wall(s) p50(s) p95(s) max(s)
before 4.986 0.158 0.207 0.269
after 0.248 0.181 0.230 0.231
=== Event-loop responsiveness (lower probe latency is better) ===
flavour samples probe p50(ms) probe p95(ms) probe max(ms)
before 1 5442.26 5442.26 5442.26
after 28 0.88 11.53 98.02
```
</details>
The benchmark script is included as a comment on the PR for
reproducibility.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Performance Improvement
Closes [#13825](https://github.com/infiniflow/ragflow/issues/13825)
---------
Co-authored-by: tmimmanuel <tmimmanuel@users.noreply.github.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
Closes#14590
## Self Checks
- [x] I have searched for existing issues [search for existing
issues](https://github.com/infiniflow/ragflow/issues), including closed
ones.
- [x] I confirm that I am using English to submit this report ([Language
Policy](https://github.com/infiniflow/ragflow/issues/5910)).
- [x] Non-english title submitions will be closed directly (
非英文标题的提交将会被直接关闭 ) ([Language
Policy](https://github.com/infiniflow/ragflow/issues/5910)).
- [x] Please do not modify this template :) and fill in all the required
fields.
## RAGFlow workspace code commit ID
`a1b2c3d4e5f67890123456789abcdef12345678`
## RAGFlow image version
`0.13.1`
## Other environment information
- Hardware parameters: N/A
- OS type: Linux 6.17.0-22-generic
- Others: API key authentication via `Authorization: Bearer <token>`
## Actual behavior
The chatbot API endpoints:
- `POST /chatbots/<dialog_id>/completions`
- `GET /chatbots/<dialog_id>/info`
validate only that the bearer token exists in `APIToken`, but do not
verify that `dialog_id` belongs to the same tenant as that token.
Current flow (simplified):
1. Route extracts bearer token and checks `APIToken.query(beta=token)`.
2. If token exists, request is accepted.
3. Downstream service resolves dialog globally by ID
(`DialogService.get_by_id(dialog_id)` in `conversation_service.py`).
4. No tenant ownership check is enforced for `dialog_id`.
Impact: Any user with a valid API key can attempt arbitrary `dialog_id`
values and access/invoke chatbots outside their own tenant boundary if
IDs are known/guessed/leaked.
Security classification:
- Vulnerability class: Broken Access Control (IDOR, OWASP Top 10 A01)
- Severity recommendation: Critical
- Exploit prerequisite: any valid API key + discoverable target
`dialog_id`
## Expected behavior
Requests to `/chatbots/<dialog_id>/completions` and
`/chatbots/<dialog_id>/info` must be authorized only when:
1. bearer token is valid, and
2. `dialog_id` belongs to the same `tenant_id` as the token.
Otherwise, reject with authorization failure (e.g., 403 or
404-equivalent policy).
## Steps to reproduce
1. Prepare two tenants:
- Tenant A with API key `TOKEN_A`
- Tenant B with chatbot `dialog_id = DIALOG_B`
2. Send request from Tenant A to Tenant B chatbot completion endpoint:
```bash
curl -X POST "https://<host>/chatbots/DIALOG_B/completions" \
-H "Authorization: Bearer TOKEN_A" \
-H "Content-Type: application/json" \
-d '{"question":"hello","stream":false}'
```
3. Observe request is processed (or reaches dialog resolution) without
tenant ownership rejection.
4. Repeat against info endpoint:
```bash
curl -X GET "https://<host>/chatbots/DIALOG_B/info" \
-H "Authorization: Bearer TOKEN_A"
```
5. Observe the same missing ownership enforcement.
## Additional information
Affected code paths:
- `api/apps/sdk/session.py`
- `chatbot_completions(dialog_id)`
- `chatbots_inputs(dialog_id)`
- `api/db/services/conversation_service.py`
- `async_iframe_completion(...)` uses global dialog lookup
Suggested fix:
1. In both chatbot endpoints:
- Resolve `tenant_id = objs[0].tenant_id` from validated token.
- Fetch dialog with tenant-scoped query
(`DialogService.query(id=dialog_id, tenant_id=tenant_id)`).
- Reject if dialog is not found/owned by tenant.
2. Defense in depth:
- Require and enforce `tenant_id` in service-layer dialog resolution for
external flows.
- Avoid global `get_by_id(dialog_id)` where user-controlled dialog IDs
are reachable.
3. Add regression tests:
- Positive: same-tenant token + dialog succeeds.
- Negative: cross-tenant token + dialog fails for both endpoints.
### What problem does this PR solve?
Fixes#14412.
`common.metadata_utils.meta_filter` evaluates user-defined metadata
conditions in Python after `DocMetadataService.get_flatted_meta_by_kbs`
loads the entire `meta_fields` table into memory. Past a few thousand
documents per knowledge base this becomes a memory bottleneck and a
wasted ES round-trip — every filter request currently fetches up to
10000 metadata rows even when the resulting `doc_ids` list is tiny.
This PR adds an ES push-down path that translates the same filter
language into a `bool` query and returns just the matching document IDs.
**Changes**
- `common/metadata_es_filter.py` *(new)*: pure-Python translator from
the RAGflow filter list to ES DSL. Covers every operator the in-memory
path supports (`=`, `≠`, `>`, `<`, `≥`, `≤`, `in`, `not in`, `contains`,
`not contains`, `start with`, `end with`, `empty`, `not empty`) with
`case_insensitive: true` on `prefix` and `wildcard` for parity with the
existing lower-cased Python comparisons. User wildcard metacharacters
are escaped before being injected into `wildcard` patterns. Negative
operators (`≠`, `not in`, `not contains`, ranges) are wrapped with an
`exists` guard so they do not accidentally match documents missing the
key, matching the legacy `if k not in metas` behaviour.
- `api/db/services/doc_metadata_service.py`: new
`DocMetadataService.filter_doc_ids_by_meta_pushdown(kb_ids, filters,
logic)` that returns the doc IDs ES matched, or `None` to signal the
caller should fall back to the in-memory path. Returns `None` when the
active doc store is Infinity (`meta_fields` is a JSON column, not a
dotted-object mapping), when any filter cannot be expressed in DSL
(`UnsupportedMetaFilter`), or when the ES request or metadata index
lookup errors.
- `common/metadata_utils.py`: `apply_meta_data_filter` accepts an
optional `kb_ids` argument. When supplied, conditions go through
push-down first via a new `_try_meta_pushdown` helper; on `None` the
function falls back to the original `meta_filter` call. Default
behaviour is unchanged for callers that don't pass `kb_ids`.
- Updated all four callers (`agent/tools/retrieval.py`,
`api/db/services/dialog_service.py` ×2,
`api/apps/services/dataset_api_service.py`, `api/apps/sdk/session.py`)
to forward `kb_ids` so the push-down path is exercised in production.
- `test/unit_test/common/test_metadata_es_filter.py` *(new)*: 35 unit
tests covering every operator's DSL shape, value coercion
(`ast.literal_eval`, lowercasing, ISO-date pass-through), wildcard
escaping, OR-logic wrapping that protects negative clauses, and the
doc-ID extractor.
**Behaviour preserved**
- The in-memory `meta_filter` is untouched and still services every
fallback case (Infinity backend, unknown operators, ES outages).
- The eligibility / credibility / issue-multiplier semantics described
in the LLM-driven `auto` and `semi_auto` modes still hand the LLM the
full in-memory `metas` dict to choose conditions from. Only the
*evaluation* of those generated conditions is pushed down.
- Existing tests in
`test/unit_test/common/test_metadata_filter_operators.py` continue to
pass (14/14).
**Test plan**
- `pytest test/unit_test/common/test_metadata_es_filter.py` — 35 passed.
- `pytest test/unit_test/common/test_metadata_filter_operators.py` — 14
passed.
- `ruff check` clean on every modified file.
- Reviewer please validate the ES query shapes against a live cluster —
particularly `case_insensitive` on `wildcard` and `prefix` (requires ES
7.10+) and the `exists` + `must_not` pairing for `≠`.
**Notes**
- The first cut caps each push-down request at 10000 results, matching
the existing `get_flatted_meta_by_kbs` limit, and logs a warning when
the cap is hit. A `search_after` follow-up would let us drop the cap
entirely once the push-down path is validated.
- Operator parity with the in-memory path is exact for the canonical
unicode operators (`≥`, `≤`, `≠`) used internally; the ASCII aliases
(`>=`, `<=`, `!=`) are normalised by `convert_conditions` before they
reach the translator.
### Type of change
- [x] Performance Improvement
---------
Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
### What problem does this PR solve?
Currently, RAGFlow's Search and Chat interfaces display only raw
vectorized text chunks during retrieval, without contextual information
about their source documents. Users cannot see document titles, page
numbers, upload dates, or custom metadata fields that would help them
understand and trust the retrieved results.
This PR introduces an **optional metadata display feature** that
enriches retrieved chunks with document-level metadata in both the
Search tab and Chatbot interface.
**Key improvements:**
- **Search results**: Display document metadata as styled badges beneath
chunk snippets
- **Chat citations**: Show metadata in citation popovers and reference
lists for better source context
- **LLM context**: Metadata is injected into the LLM prompt to enable
more accurate, citation-aware responses
- **External API support**: Applications using RAGFlow's SDK retrieval
endpoints (`/v1/retrieval`, `/v1/searchbots/retrieval_test`) can opt-in
via request parameters
- **User control**: Multi-select dropdown UI allows users to choose
which metadata fields to display
**Implementation approach:**
- ✅ Reuses existing `DocMetadataService` infrastructure (no new database
tables or indices)
- ✅ Settings stored in existing JSON configuration fields
(`search_config.reference_metadata`, `prompt_config.reference_metadata`)
- ✅ No database migrations required
- ✅ Disabled by default (fully opt-in and backward-compatible)
- ✅ Dynamic metadata field selection populated from actual document
metadata keys
- ✅ Fixed critical bug where Python's builtin `set()` was shadowed by a
route handler function
**Modified endpoints (all backward-compatible):**
- `POST /v1/retrieval` (Public SDK)
- `POST /v1/searchbots/retrieval_test` (Searchbots)
- `POST /v1/chunk/retrieval_test` (UI/Internal)
- Chat completions endpoints (via `extra_body.reference_metadata` or
`prompt_config`)
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
###Images
-
<img width="879" height="1275" alt="image"
src="https://github.com/user-attachments/assets/95b2d731-31ae-45a1-b081-bf5893f52aeb"
/>
<br><br>
<br><br>
<img width="1532" height="362" alt="image"
src="https://github.com/user-attachments/assets/9cebc65b-b7a7-459f-b25e-3b13fa9b638e"
/>
<br><br>
<br><br>
<img width="2586" height="1320" alt="image"
src="https://github.com/user-attachments/assets/2153d493-d899-461f-a7a9-041391e07776"
/>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Attili-sys <Attili-sys@users.noreply.github.com>
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
### What problem does this PR solve?
## Summary
Fixed a bug where the **File Logs** tab in the dataset ingestion page
always showed "No logs" even after files were parsed successfully.
## Root Cause
Both the **File Logs** and **Dataset Logs** tabs on the frontend called
the same backend endpoint `/datasets/{dataset_id}/ingestions`. However,
the backend only queried `get_dataset_logs_by_kb_id`, which
hard-filtered records by `document_id == GRAPH_RAPTOR_FAKE_DOC_ID`
(dataset-level logs). As a result, real file-level logs were never
returned, causing the table to appear empty.
## Changes
### Backend
- **`api/apps/restful_apis/dataset_api.py`**
- Added two new query parameters to `list_ingestion_logs`:
- `log_type` — `"file"` or `"dataset"` (default: `"dataset"`)
- `keywords` — search keyword for filtering by document / task name
- **`api/apps/services/dataset_api_service.py`**
- Updated `list_ingestion_logs` signature to accept `log_type` and
`keywords`.
- Added conditional routing:
- When `log_type == "file"`, call
`PipelineOperationLogService.get_file_logs_by_kb_id`
- Otherwise, call
`PipelineOperationLogService.get_dataset_logs_by_kb_id`
- **`api/db/services/pipeline_operation_log_service.py`**
- Extended `get_dataset_logs_by_kb_id` with an optional `keywords`
parameter so dataset logs can also be searched.
### Frontend
- **`web/src/pages/dataset/dataset-overview/hook.ts`**
- Removed the separate API function switching (`listPipelineDatasetLogs`
vs `listDataPipelineLogDocument`).
- Unified both tabs to call `listDataPipelineLogDocument` with the new
`log_type` query parameter (`"file"` or `"dataset"`).
- Ensured `keywords` and filter values are passed through correctly.
## Behavior After Fix
| Tab | `log_type` | Returned Records | Searchable Field |
|---|---|---|---|
| File Logs | `file` | Real document-level logs | `document_name` (file
name) |
| Dataset Logs | `dataset` | GraphRAG / RAPTOR / MindMap logs |
`document_name` (task type) |
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
Co-authored-by: Yingfeng Zhang <yingfeng.zhang@gmail.com>
### What problem does this PR solve?
Feat: Export Agent Logs.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: balibabu <assassin_cike@163.com>
### What problem does this PR solve?
Follow-up expose agent structured outputs in non-stream completions
#13389.
### Type of change
- [x] Documentation Update
- [x] Refactoring
---------
Co-authored-by: writinwaters <cai.keith@gmail.com>
### What problem does this PR solve?
Feat: Modify the style of the release confirmation box.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: balibabu <assassin_cike@163.com>
Co-authored-by: 6ba3i <isbaaoui09@gmail.com>
### What problem does this PR solve?
Add delete all support for delete operations.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
---------
Co-authored-by: writinwaters <cai.keith@gmail.com>
### What problem does this PR solve?
Previously, when an Agent component was configured with structured
output, the non-streaming /agents/{agent_id}/completions API never
returned the structured field in its response.
The root cause: the non-streaming code path only collected message
events to build full_content, then returned the workflow_finished
payload — which only contains the output of the last component in the
execution path (typically a Message component).
Any structured output set by upstream components (e.g., Agent or LLM)
was silently discarded.
This PR fixes the non-streaming handler to iterate node_finished events
and collect structured output from intermediate components.
If any component produced a non-empty structured value, it is included
in the final response under data.structured. The streaming path is
unaffected, as it already exposes node_finished events to the caller.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Empty ids means no-op operation.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Documentation Update
- [x] Refactoring
---------
Co-authored-by: writinwaters <cai.keith@gmail.com>
### What problem does this PR solve?
Add id for table tenant_llm and apply in LLMBundle.
### Type of change
- [x] Refactoring
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: Liu An <asiro@qq.com>
### What problem does this PR solve?
Feat: published agent version control
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
When using a chat assistant that has a hardcoded `empty_response`, that
response was not returned correctly in streaming mode when no
information is found in the knowledge base. In this case only one
response with `"content": null` was yielded. If `"references": true`,
then the `empty_response` is still put into the `final_content` so there
is technically some content returned, but when `"references": false` no
content at all is returned.
I update the OpenAI chat completion endpoint to yield an additional
response with the `empty_response` in the content.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
This PR adds an opt‑in way to include document‑level metadata in
OpenAI‑compatible reference chunks. Until now, metadata could be used
for filtering but wasn’t returned in responses. The change enables
clients to show richer citations (author/year/source, etc.) while
keeping payload size and privacy under control via an explicit request
flag and optional field allowlist.
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
Contribution during my time at RAGcon GmbH.
### What problem does this PR solve?
Put document metadata in ES/Infinity.
Index name of meta data: ragflow_doc_meta_{tenant_id}
### Type of change
- [x] Refactoring
### What problem does this PR solve?
The OpenAI-compatible chat endpoint
(`/chats_openai/<chat_id>/chat/completions`) was not returning accurate
token
usage in streaming responses. The token counts were either missing or
inaccurate because the underlying LLM API
responses weren't being properly parsed for usage data.
This PR adds proper token counting using tiktoken (cl100k_base encoding)
as a fallback when the LLM API doesn't provide usage data in streaming
chunks. This ensures clients always receive token usage information in
the
response, which is essential for billing and quota management.
**Changes:**
- Add tiktoken-based token counting for streaming responses in
OpenAI-compatible endpoint
- Ensure `usage` field is always populated in the final streaming chunk
- Add unit tests for token usage calculation
Fixes#7850
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat: Add a web search button to the chat box on the chat page.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
**Backend**
\rag\nlp\search.py
*Before the fix*
The top_k parameter was not applied to limit the total number of chunks,
and the rerank model also uses the exact whole valid_idx rather than
assigning valid_idx = valid_idx[:top] firstly.
*After the fix*
The top_k limit is applied to the total results before pagination, using
a default value of top = 1024 if top_k is not modified.
session.py
*Before the fix:*
When the frontend calls the retrieval API with `search_id`, the backend
only reads `meta_data_filter` from the saved `search_config`. The
`rerank_id`, `top_k`, `similarity_threshold`, and
`vector_similarity_weight` parameters are only taken from the direct
request body. Since the frontend doesn't pass these parameters
explicitly (it only passes `search_id`), they always fall back to
default values:
- `similarity_threshold` = 0.0
- `vector_similarity_weight` = 0.3
- `top_k` = 1024
- `rerank_id` = "" (no rerank)
This means user settings saved in the Search Settings page have no
effect on actual search results.
*After the fix:*
When a `search_id` is provided, the backend now reads all relevant
configuration from the saved `search_config`, including `rerank_id`,
`top_k`, `similarity_threshold`, and `vector_similarity_weight`. Request
parameters can still override these values if explicitly provided,
allowing flexibility. The rerank model is now properly instantiated
using the configured `rerank_id`, making the rerank feature actually
work.
**Frontend**
\web\src\pages\next-search\search-setting.tsx
*Before the fix*
search-setting.tsx file, the top_k input box is only displayed when
rerank is enabled (wrapped in the rerankModelDisabled condition). If the
rerank switch is turned off, the top_k input field will be hidden, but
the form value will remain unchanged. In other words: - When rerank is
enabled, users can modify top_k (default 1024). - When rerank is
disabled, top_k retains the previous value, but it's not visible on the
interface. Therefore, the backend will always receive the top_k
parameter; it's just that the frontend UI binds this configuration item
to the rerank switch. When rerank is turned off, top_k will not
automatically reset to 1024, but will retain its original value.
*After the fix*
On the contrary, if we switch off the button rerank model, the value
top-k will be reset to 1024. By the way, If we use top-k in an
individual method, rather than put it into the method retrieval, we can
control it separately
Now all methods valid
Using rerank
<img width="2378" height="1565" alt="Screenshot 2026-01-21 190206"
src="https://github.com/user-attachments/assets/fa2b0df0-1334-4ca3-b169-da6c5fd59935"
/>
Not using rerank
<img width="2596" height="1559" alt="Screenshot 2026-01-21 190229"
src="https://github.com/user-attachments/assets/c5a80522-a0e1-40e7-b349-42fe86df3138"
/>
Before fixing they are the same
### Type of change
- Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
API adds audio to text and text to speech functions
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
change:
Enhance delta streaming in chat functions for improved reasoning and
content handling
### Type of change
- [x] Refactoring
## Summary
Fixes#12311
Changes the `reference` field initialization from `[{}]` to `[]` in
session creation.
### Problem
When creating a session via the SDK API, the `reference` field was
incorrectly initialized as `[{}]`. This caused:
- First dialogue round: Empty reference
- Second dialogue round: Reference pointing to first round's data
- Overall misalignment between dialogue rounds and their references
### Solution
Changed the initialization to `[]` (empty list), which:
- Matches the `Conversation` model's expected default
- Ensures references grow correctly one-to-one with assistant responses
- Aligns with the service layer's expectations
### Testing
After applying this fix:
1. Create a session via `POST /api/v1/chats/{conversation_id}/sessions`
2. Send multiple questions via `POST
/api/v1/chats/{conversation_id}/completions`
3. View the conversation on web - references should now align correctly
with each dialogue round
### What problem does this PR solve?
Manage message and use in agent.
Issue #4213
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Chats completions API supports metadata filtering.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Trace information can be returned by the agent completion API.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Refactor metadata filter.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
### What problem does this PR solve?
Add DeepseekV3.2 of Tongyi-Qianwen and remove unused code
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
change：
async issue and sensitive logging
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Retrieval metadata filtering adds semi-automatic mode, and users can
manually check the metadata key that participates in LLM to generate
filter conditions.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Cleanup synchronous functions in chat_model and implement
synchronization for conversation and dialog chats.
### Type of change
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
Make RAGFlow more asynchronous 2. #11551, #11579, #11619.
### Type of change
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
Make RAGFlow more asynchronous 2. #11551, #11579, #11619.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
- [x] Performance Improvement
### What problem does this PR solve?
Try to make this more asynchronous. Verified in chat and agent
scenarios, reducing blocking behavior. #11551, #11579.
However, the impact of these changes still requires further
investigation to ensure everything works as expected.
### Type of change
- [x] Refactoring
