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Go: add development guide document (#14785)

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### What problem does this PR solve?

As the title suggests.

### Type of change

- [x] Documentation Update

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2026-05-11 17:20:41 +08:00
committed by GitHub
parent 663fc1d42c
commit 9b3850339b
2 changed files with 366 additions and 5 deletions
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13
build.sh
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@ -16,6 +16,7 @@ CPP_DIR="$PROJECT_ROOT/internal/cpp"
BUILD_DIR="$CPP_DIR/cmake-build-release"
RAGFLOW_SERVER_BINARY="$PROJECT_ROOT/bin/server_main"
ADMIN_SERVER_BINARY="$PROJECT_ROOT/bin/admin_server"
RAGFLOW_CLI_BINARY="$PROJECT_ROOT/bin/ragflow_cli"
echo -e "${GREEN}=== RAGFlow Go Server Build Script ===${NC}"
@ -73,7 +74,7 @@ build_cpp() {
# Build Go server
build_go() {
print_section "Building Go server"
print_section "Building RAGFlow go"
cd "$PROJECT_ROOT"
@ -91,9 +92,10 @@ build_go() {
sudo apt -y install libpcre2-dev
fi
echo "Building API server binary: $RAGFLOW_SERVER_BINARY and $ADMIN_SERVER_BINARY"
GOPROXY=${GOPROXY:-https://goproxy.cn,https://proxy.golang.org,direct} CGO_ENABLED=1 go build -o "$RAGFLOW_SERVER_BINARY" ./cmd/server_main.go
GOPROXY=${GOPROXY:-https://goproxy.cn,https://proxy.golang.org,direct} CGO_ENABLED=1 go build -o "$ADMIN_SERVER_BINARY" ./cmd/admin_server.go
echo "Building RAGFlow binary: $RAGFLOW_SERVER_BINARY, $ADMIN_SERVER_BINARY, and $RAGFLOW_CLI_BINARY"
GOPROXY=${GOPROXY:-https://goproxy.cn,https://proxy.golang.org,direct} CGO_ENABLED=1 go build -o "$RAGFLOW_SERVER_BINARY" cmd/server_main.go
GOPROXY=${GOPROXY:-https://goproxy.cn,https://proxy.golang.org,direct} CGO_ENABLED=1 go build -o "$ADMIN_SERVER_BINARY" cmd/admin_server.go
GOPROXY=${GOPROXY:-https://goproxy.cn,https://proxy.golang.org,direct} CGO_ENABLED=1 go build -o "$RAGFLOW_CLI_BINARY" cmd/ragflow_cli.go
if [ ! -f "$RAGFLOW_SERVER_BINARY" ]; then
echo -e "${RED}Error: Failed to build RAGFlow server binary${NC}"
@ -105,8 +107,9 @@ build_go() {
exit 1
fi
echo -e "${GREEN}✓ Go server_main built successfully: $RAGFLOW_SERVER_BINARY${NC}"
echo -e "${GREEN}✓ Go ragflow_server built successfully: $RAGFLOW_SERVER_BINARY${NC}"
echo -e "${GREEN}✓ Go admin_server built successfully: $ADMIN_SERVER_BINARY${NC}"
echo -e "${GREEN}✓ Go ragflow_cli built successfully: $RAGFLOW_CLI_BINARY${NC}"
}
# Clean build artifacts

358
internal/development.md Normal file
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@ -0,0 +1,358 @@
# RAGFlow Go Version - Startup Guide
## 1. Start Dependencies
```bash
docker compose -f docker/docker-compose-base.yml up -d
```
## 2. Build Go Version RAGFlow
- First build (includes C++ dependencies):
```bash
./build.sh --cpp
```
- Subsequent builds (Go only):
```bash
./build.sh --go
```
## 3. Run Go Version RAGFlow
Note: admin_server must be started first; otherwise, ragflow_server will encounter errors when sending heartbeats.
```bash
# Start admin server
./bin/admin_server
```
```bash
# Start RAGFlow server
./bin/ragflow_server
```
```bash
# Run CLI
./bin/ragflow_cli
```
## 4. Start Frontend
```bash
cd web && export API_PROXY_SCHEME=hybrid && npm run dev
```
## 5. Service Ports & API Routing
- ragflow_server listens on port 9384
- admin_server listens on port 9383
After updating or implementing an API, update the frontend development environment routes in web/vite.config.ts under proxySchemes.
### Proxy Schemes
| Scheme | Description |
|--------|-------------|
| `python` | All API requests from the frontend are routed to the Python server |
| `hybrid` | API requests are partially routed to the Go server and partially to the Python server |
| `go` | All API requests from the frontend are routed to the Go server |
## 6. RAGFlow commands
You can use the following CLI commands to test the corresponding API implementations.
### 6.1. Run ragflow_cli, register user, login, and logout:
```
$ ./ragflow_cli
Welcome to RAGFlow CLI
Type \? for help, \q to quit
RAGFlow(user)> REGISTER USER 'aaa@aaa.com' AS 'aaa' PASSWORD 'aaa';
Register successfully
RAGFlow(user)> login user 'aaa@aaa.com';
password for aaa@aaa.com: Password:
Login user aaa@aaa.com successfully
RAGFlow(user)> logout;
SUCCESS
```
### 6.2. List currently supported providers
```
RAGFlow(user)> list available providers;
```
### 6.3. Add or delete a provider for the current tenant
```
RAGFlow(user)> add provider 'openai';
```
```
RAGFlow(user)> delete provider 'openai';
```
### 6.4. Create a model instance for a specific provider
```
RAGFlow(user)> create provider 'openai' instance 'instance_name' key 'api-key';
```
Note: The api-key is a valid API key that needs to be applied for. You can create multiple instances for the same model provider, each with a different API key.
For locally deployed models (e.g., ollama, vLLM), use the following command to add a model instance:
```
RAGFlow(user)> create provider 'vllm' instance 'instance_name' key '' url 'http://192.168.1.96:8123/v1';
```
### 6.5. List and delete an instance
```
RAGFlow(user)> list instances from 'openai';
```
```
RAGFlow(user)> drop instance 'instance_name' from 'openai';
```
### 6.6. List models supported by a model instance
```
RAGFlow(user)> list models from 'openai' 'instance_name';
```
### 6.7. Chat with LLM
- Chat
```
RAGFlow(user)> chat with 'glm-4.5-flash@test@zhipu-ai' message '20 words introduce LLM';
Answer: A large language model is an AI trained on vast text data to understand, generate, and refine human-like language.
Time: 1.052269
```
- Chat with Thinking (Reasoning)
```
RAGFlow(user)> think chat with 'glm-4.5-flash@test@zhipu-ai' message '20 words introduce LLM';
Thinking: I need to create a concise 20-word introduction to LLMs...
Answer: Large Language Models are AI systems trained on vast datasets, enabling human-like text generation, comprehension, and problem-solving across diverse applications.
Time: 11.592358
```
- Streaming Chat
```
RAGFlow(user)> stream chat with 'glm-4.5-flash@test@zhipu-ai' message '20 words introduce LLM';
Answer: Language Models are advanced AI systems. They process text to learn, generate human-like responses, and perform diverse tasks through machine learning.
Time: 2.615930
```
- Streaming Chat with Thinking
```
RAGFlow(user)> stream think chat with 'glm-4.5-flash@test@zhipu-ai' message '20 words introduce LLM';
Thinking: The user is asking for a very concise introduction to LLMs...
Answer: language models are AI systems trained on vast text datasets to understand and generate human-like text for diverse tasks.
Time: 11.958035
```
- Image Understanding
```
RAGFlow(user)> chat with 'glm-4.6v-flash@test@zhipu-ai' message 'What are the pics talk about?' image 'https://cdn.bigmodel.cn/static/logo/register.png' 'https://cdn.bigmodel.cn/static/logo/api-key.png'
Answer: The first picture shows a login/register modal... The second picture displays the API keys management page...
Time: 31.600545
```
- Video Understanding
```
RAGFlow(user)> chat with 'glm-4.6v-flash@test@zhipu-ai' message 'What are the video talk about?' video 'https://cdn.bigmodel.cn/agent-demos/lark/113123.mov'
Answer: Based on the sequence of frames provided, the video is a demonstration of a web search and navigation process...
Time: 76.582520
```
Note: Both image and video understanding support streaming and thinking modes as well.
### 6.8. Generate Embeddings
```
RAGFlow(user)> embed text 'what is rag' 'who are you' with 'embedding-3@test@zhipu-ai' dimension 16;
```
### 6.9. Document Reranking
```
RAGFlow(user)> rerank query 'what is rag' document 'rag is retrieval augment generation' 'rag need llm' 'famous rag project includes ragflow' with 'rerank@test@zhipu-ai' top 2;
```
### 6.10. Get supported models from provider API
```
RAGFlow(user)> list supported models from 'minimax' 'test';
+------------------------+
| model_name |
+------------------------+
| MiniMax-M2.7 |
| MiniMax-M2.7-highspeed |
| MiniMax-M2.5 |
| MiniMax-M2.5-highspeed |
| MiniMax-M2.1 |
| MiniMax-M2.1-highspeed |
| MiniMax-M2 |
+------------------------+
```
### 6.11. Get preset models of a provider
```
RAGFlow(user)> list models from 'minimax';
+------------+-------------+------------------------+
| max_tokens | model_types | name |
+------------+-------------+------------------------+
| 204800 | [chat] | minimax-m2.7 |
| 204800 | [chat] | minimax-m2.7-highspeed |
| 204800 | [chat] | minimax-m2.5 |
| 204800 | [chat] | minimax-m2.5-highspeed |
| 204800 | [chat] | minimax-m2.1 |
| 204800 | [chat] | minimax-m2.1-highspeed |
| 204800 | [chat] | minimax-m2 |
| 65536 | [chat] | minimax-m2-her |
+------------+-------------+------------------------+
```
### 6.12. List instances of a provider
```
RAGFlow(user)> list instances from 'zhipu-ai';
+---------+----------------------+----------------------------------+--------------+----------------------------------+--------+
| apiKey | extra | id | instanceName | providerID | status |
+---------+----------------------+----------------------------------+--------------+----------------------------------+--------+
| api-key | {"region":"default"} | 19f620e73c7a11f1a51138a74640adcc | test | d21a3758398f11f1ab4838a74640adcc | enable |
+---------+----------------------+----------------------------------+--------------+----------------------------------+--------+
```
### 6.13. Show instance of a provider
```
RAGFlow(user)> show instance 'test' from 'zhipu-ai';
+----------------------------------+--------------+----------------------------------+---------+--------+
| id | instanceName | providerID | region | status |
+----------------------------------+--------------+----------------------------------+---------+--------+
| 19f620e73c7a11f1a51138a74640adcc | test | d21a3758398f11f1ab4838a74640adcc | default | enable |
+----------------------------------+--------------+----------------------------------+---------+--------+
```
### 6.14. List models of a specific instance
```
RAGFlow(user)> list models from 'minimax' 'test';
+------------+-------------+------------------------+--------+
| max_tokens | model_types | name | status |
+------------+-------------+------------------------+--------+
| 204800 | [chat] | minimax-m2.7 | active |
| 204800 | [chat] | minimax-m2.7-highspeed | active |
| 204800 | [chat] | minimax-m2.5 | active |
| 204800 | [chat] | minimax-m2.5-highspeed | active |
| 204800 | [chat] | minimax-m2.1 | active |
| 204800 | [chat] | minimax-m2.1-highspeed | active |
| 204800 | [chat] | minimax-m2 | active |
| 65536 | [chat] | minimax-m2-her | active |
+------------+-------------+------------------------+--------+
```
### 6.15. List added providers
```
RAGFlow(user)> list providers;
+--------------------------------------------------------------------------+-------------+--------------+
| base_url | name | total_models |
+--------------------------------------------------------------------------+-------------+--------------+
| map[default:https://ark.cn-beijing.volces.com/api/v3] | VolcEngine | 2 |
| map[default:https://api.minimaxi.com/ global:https://api.minimax.io/] | MiniMax | 8 |
| map[default:https://api.moark.com/v1] | Gitee | 5 |
+--------------------------------------------------------------------------+-------------+--------------+
```
### 6.16. Deactivate / activate a model
```
RAGFlow(user)> disable model 'deepseek-v4-pro' from 'deepseek' 'test';
SUCCESS
RAGFlow(user)> list models from 'deepseek' 'test';
+------------+-------------+-------------------+----------+
| max_tokens | model_types | name | status |
+------------+-------------+-------------------+----------+
| 1048576 | [chat] | deepseek-v4-flash | active |
| 1048576 | [chat] | deepseek-v4-pro | inactive |
+------------+-------------+-------------------+----------+
RAGFlow(user)> enable model 'deepseek-v4-pro' from 'deepseek' 'test';
SUCCESS
```
### 6.17. Set current model
```
RAGFlow(user)> use model 'glm-4.5-flash@test@zhipu-ai';
SUCCESS
RAGFlow(user)> chat message '20 words introduce LLM';
Answer: Large language models are advanced AI systems. They process text to understand, generate, and refine human-like language for countless tasks.
Time: 1.680416
```
### 6.18. Set, reset, and list default models
```
RAGFlow(user)> set default chat model 'zhipu-ai/test/glm-4.5-flash';
SUCCESS
RAGFlow(user)> set default vision model 'zhipu-ai/test/glm-4.5v';
SUCCESS
RAGFlow(user)> set default embedding model 'zhipu-ai/test/embedding-2';
SUCCESS
RAGFlow(user)> set default rerank model 'zhipu-ai/test/rerank';
SUCCESS
RAGFlow(user)> set default ocr model 'zhipu-ai/test/glm-ocr';
SUCCESS
RAGFlow(user)> set default tts model 'zhipu-ai/test/glm-tts';
SUCCESS
RAGFlow(user)> set default asr model 'zhipu-ai/test/glm-asr-2512';
SUCCESS
RAGFlow(user)> list default models;
+--------+----------------+---------------+----------------+------------+
| enable | model_instance | model_name | model_provider | model_type |
+--------+----------------+---------------+----------------+------------+
| true | test | glm-4.5-flash | zhipu-ai | chat |
| true | test | embedding-2 | zhipu-ai | embedding |
| true | test | rerank | zhipu-ai | rerank |
| true | test | glm-asr-2512 | zhipu-ai | asr |
| true | test | glm-4.5v | zhipu-ai | vision |
| true | test | glm-ocr | zhipu-ai | ocr |
| true | test | glm-tts | zhipu-ai | tts |
+--------+----------------+---------------+----------------+------------+
RAGFlow(user)> reset default embedding model;
SUCCESS
RAGFlow(user)> reset default chat model
SUCCESS
RAGFlow(user)> list default models;
+--------+----------------+--------------+----------------+------------+
| enable | model_instance | model_name | model_provider | model_type |
+--------+----------------+--------------+----------------+------------+
| true | test | rerank | zhipu-ai | rerank |
| true | test | glm-asr-2512 | zhipu-ai | asr |
| true | test | glm-4.5v | zhipu-ai | vision |
| true | test | glm-ocr | zhipu-ai | ocr |
| true | test | glm-tts | zhipu-ai | tts |
+--------+----------------+--------------+----------------+------------+
```
### 6.19. Show current balance of a provider instance
```
RAGFlow(user)> show balance from 'gitee' 'test';
+-------------+----------+
| balance | currency |
+-------------+----------+
| 82.49835029 | CNY |
+-------------+----------+
```
### 6.20. Check provider instance availability
```
RAGFlow(user)> check instance 'test' from 'zhipu-ai';
SUCCESS
```
### 6.21. Add local model to RAGFlow, only for local deployed inference server, such as ollama
```
RAGFlow(user)> add model 'Qwen/Qwen2.5-0.5B' to provider 'vllm' instance 'test' with tokens 131072 chat;
SUCCESS
RAGFlow(user)> list models from 'vllm' 'test';
+-------------------+--------+
| name | status |
+-------------------+--------+
| Qwen/Qwen2.5-0.5B | active |
+-------------------+--------+
RAGFlow(user)> drop model 'Qwen/Qwen2.5-0.5B' from 'vllm' 'test';
SUCCESS
```
### 6.22. List datasets
```
RAGFlow(user)> list datasets;
+-------------+--------------+----------------+----------------------+----------------------------------+----------+------+----------+------------+----------------------------------+-----------+---------------+
| chunk_count | chunk_method | document_count | embedding_model | id | language | name | nickname | permission | tenant_id | token_num | update_time |
+-------------+--------------+----------------+----------------------+----------------------------------+----------+------+----------+------------+----------------------------------+-----------+---------------+
| 492 | naive | 1 | embedding-2@ZHIPU-AI | e93ab2c04ad111f1b17438a74640adcc | English | aaa | aaa | me | 2ba4881420fa11f19e9c38a74640adcc | 74278 | 1778245825722 |
| 0 | naive | 1 | embedding-2@ZHIPU-AI | 0abe79f9423311f1ad8d38a74640adcc | English | ccc | aaa | me | 2ba4881420fa11f19e9c38a74640adcc | 0 | 1777375201933 |
+-------------+--------------+----------------+----------------------+----------------------------------+----------+------+----------+------------+----------------------------------+-----------+---------------+
```