mirror of
https://github.com/GNS3/gns3-server.git
synced 2026-08-31 06:20:12 +03:00
feat(docs): enhance AI chat API documentation with examples and details
- Improve POST /chat endpoint documentation with request/response examples
- Add session ID management flow explanation
- Enhance GET /sessions endpoint with query parameters and response example
- Update GET /sessions/{session_id}/history with detailed response structure
- Format parameters as tables for better readability
- Clarify session ID usage in streaming conversations
This commit is contained in:
YueGuobin
parent
fd34ef494f
commit
9ba02e9436
@ -404,42 +404,152 @@ All endpoints are under `/v3/projects/{project_id}/chat/` path.
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**Function**: Streaming conversation interface
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**Request Parameters**:
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- message: User message content
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- session_id: Session ID (optional, creates new session if not provided)
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- stream: Enable streaming response (default true)
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- temperature: LLM temperature parameter (Note: currently unused, reserved for future implementation. Actual temperature is read from user's database LLM configuration)
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- mode: Interaction mode (currently only supports "text")
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| message | string | Yes | User message content |
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| session_id | string | No | Session ID (creates new session if not provided) |
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| stream | boolean | No | Enable streaming response (default true) |
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| temperature | float | No | LLM temperature (reserved, currently unused) |
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| mode | string | No | Interaction mode (fixed as "text", reserved for future expansion) |
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**Response**: SSE stream, contains multiple types of messages (see message format above)
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**Request Example**:
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```json
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// First message (new session)
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{
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"message": "Hello, can you help me?",
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"stream": true
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}
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// Subsequent messages (continue session)
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{
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"message": "Show me the network topology",
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"session_id": "d7e76375-6960-419a-9367-211ef64af877",
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"stream": true
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}
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```
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**Response**: SSE stream, contains multiple types of messages (see message format section)
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**Session ID Management**:
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- **First message**: Do not send `session_id` in request, backend generates a new UUID
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- **Retrieve session_id**: Each SSE message (including `done` message) contains `session_id` field
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- **Subsequent messages**: Include the saved `session_id` in request body to continue conversation
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- **Example flow**:
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1. First request: `{"message": "hello", "stream": true}` → generates new session
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2. Get `session_id` from SSE response: `{"type": "done", "session_id": "xxx-xxx-xxx"}`
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3. Second request: `{"message": "how are you?", "session_id": "xxx-xxx-xxx", "stream": true}`
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**Project Status Check**: Only allows conversation when project status is "opened"
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**Response Example** (SSE stream):
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```
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data: {"type": "content", "content": "Hello", "session_id": "d7e76375-6960-419a-9367-211ef64af877"}
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data: {"type": "content", "content": "! I can help", "session_id": "d7e76375-6960-419a-9367-211ef64af877"}
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data: {"type": "done", "session_id": "d7e76375-6960-419a-9367-211ef64af877"}
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```
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### GET /v3/projects/{project_id}/chat/sessions
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**Function**: List all sessions in a project
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**Query Parameters**:
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| user_id | string | No | Filter by user ID |
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| limit | int | No | Maximum number of sessions (default 100) |
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**Response**: Session list, includes statistics (message count, token usage, etc.), sorted by pin status and update time
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**Response Example**:
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```json
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[
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{
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"id": 1,
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"thread_id": "d7e76375-6960-419a-9367-211ef64af877",
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"user_id": "admin",
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"project_id": "a0f46d81-e564-443c-b321-2cdebe80e321",
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"title": "GNS3 Topology Assistance",
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"message_count": 4,
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"llm_calls_count": 2,
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"input_tokens": 8500,
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"output_tokens": 1200,
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"total_tokens": 9700,
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"last_message_at": "2026-03-08T01:34:07",
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"created_at": "2026-03-07T17:48:07",
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"updated_at": "2026-03-08T01:34:07",
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"metadata": {},
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"stats": {},
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"pinned": false
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}
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]
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```
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### GET /v3/projects/{project_id}/chat/sessions/{session_id}/history
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**Function**: Get complete history of a session
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**Parameters**:
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**Path Parameters**:
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- session_id: Session ID
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- limit: Maximum number of messages (default 100)
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**Response**:
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- thread_id: Session ID
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- title: Session title
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- messages: Message list (OpenAI format)
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- llm_calls: Number of LLM calls
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**Query Parameters**:
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| limit | int | No | Maximum number of messages (default 100) |
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**Response Example**:
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```json
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{
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"thread_id": "d7e76375-6960-419a-9367-211ef64af877",
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"title": "GNS3 Topology Assistance",
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"messages": [
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{
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"id": "f0247568-071d-412f-9e3e-4cbe815834ea",
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"role": "user",
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"content": "你能干点啥。",
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"metadata": {
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"created_at": "2026-03-07T17:48:07.848519"
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}
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},
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{
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"id": "lc_run--019cc969-eb81-7dd1-a894-e819daf81cd0",
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"role": "assistant",
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"content": "我可以作为GNS3网络实验的助教...",
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"tool_calls": [
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{
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"id": "call_00_xxx",
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"type": "function",
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"function": {
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"name": "get_gns3_topology",
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"arguments": {}
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}
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}
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],
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"metadata": {}
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}
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],
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"created_at": null,
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"updated_at": null,
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"llm_calls": 2
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}
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```
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### PATCH /v3/projects/{project_id}/chat/sessions/{session_id}
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**Function**: Rename session
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**Request Parameters**:
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- title: New title (1-255 characters)
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| title | string | Yes | New title (1-255 characters) |
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**Request Example**:
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```json
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{
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"title": "New Session Title"
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}
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```
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**Response**: Updated session information
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@ -453,13 +563,31 @@ All endpoints are under `/v3/projects/{project_id}/chat/` path.
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**Function**: Pin session to top of list
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**Response**: Updated session information (includes pinned=true)
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**Response Example**:
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```json
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{
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"id": 1,
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"thread_id": "d7e76375-6960-419a-9367-211ef64af877",
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"title": "GNS3 Topology Assistance",
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"pinned": true,
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...
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}
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```
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### DELETE /v3/projects/{project_id}/chat/sessions/{session_id}/pin
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**Function**: Unpin session
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**Response**: Updated session information (includes pinned=false)
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**Response Example**:
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```json
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{
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"id": 1,
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"thread_id": "d7e76375-6960-419a-9367-211ef64af877",
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"title": "GNS3 Topology Assistance",
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"pinned": false,
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...
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}
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```
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**Sorting Rules**:
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- Pinned sessions (pinned=true) appear at the front
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@ -523,7 +651,9 @@ OpenAI-compatible message model.
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- id: str - Message unique identifier (auto-generated or inherited from LangChain message)
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- role: Literal["user", "assistant", "system", "tool"] - Message role
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- content: str - Message content (supports text, JSON string)
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- created_at: str - Creation time (ISO 8601)
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- metadata: Optional[Dict] - Message metadata (includes created_at and other custom fields)
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- created_at: str - Message creation time (ISO 8601 format)
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- Other custom fields can be added as needed
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**Tool-related Fields**:
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- name: Optional[str] - Tool message name (tool message)
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@ -533,8 +663,10 @@ OpenAI-compatible message model.
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- type: Literal["function"] - Fixed as "function"
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- function: Dict - Contains name and arguments (dict or JSON string)
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**Metadata**:
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- metadata: Optional[Dict] - Additional message metadata
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**Important Notes**:
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- Message creation time is stored in `metadata.created_at` field
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- Frontend should read `metadata.created_at` for message timestamp
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- Historical messages may not have `created_at` in metadata (empty `{}`)
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## Core Components
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@ -555,12 +687,19 @@ OpenAI-compatible message model.
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- Auto-generate UUID if message has no ID
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- Ensure all returned messages have unique identifier
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2. **Tool Calls Format Conversion**
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2. **Metadata and Timestamp Handling**
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- Extract entire `metadata` dict from LangChain message
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- Message creation time stored in `metadata.created_at` field (ISO 8601 format)
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- No top-level `created_at` field in returned message
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- Frontend should read `message.metadata.created_at` for timestamp
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- Historical messages without metadata will have empty `{}`
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3. **Tool Calls Format Conversion**
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- LangChain format: `{'name': 'xxx', 'args': {...}, 'id': 'yyy', 'type': 'tool_call'}`
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- OpenAI format: `{'id': 'yyy', 'type': 'function', 'function': {'name': 'xxx', 'arguments': '{...}'}}`
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- Automatically convert `args` object to JSON string (if needed)
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3. **Content Type Handling**
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4. **Content Type Handling**
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- Supports string, dict, list types
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- Non-string types automatically converted to JSON string
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@ -583,7 +722,7 @@ OpenAI-compatible message model.
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2. Get or create chat session (from `chat_sessions` table)
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3. Set ContextVars (JWT token, LLM config)
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4. Build LangGraph config
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5. Create initial message with ID: `HumanMessage(content=message, id=str(uuid4()))`
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5. Create initial message with ID and timestamp: `HumanMessage(content=message, id=str(uuid4()), metadata={"created_at": datetime.utcnow().isoformat()})`
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6. Stream Agent execution, collecting statistics simultaneously
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7. Update session statistics to database after stream ends
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8. Sync auto-generated title
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@ -667,6 +806,55 @@ Handle different types based on SSE message's `type` field:
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| done | Mark stream end, stop loading state |
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| heartbeat | Ignore (keepalive signal) |
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### Session ID Management (Important)
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The frontend must properly manage session_id to maintain conversation continuity:
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1. **First request**: Do not include `session_id` in request body
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2. **Save session_id**: Extract `session_id` from each SSE message (especially the `done` message)
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3. **Subsequent requests**: Include the saved `session_id` in request body to continue the conversation
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4. **State management**: Store `session_id` in React state/localStorage to persist across page refreshes
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**Example**:
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```javascript
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// First message
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const response = await fetch('/chat/stream', {
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method: 'POST',
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body: JSON.stringify({ message: 'Hello', stream: true })
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});
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// Get session_id from first done message
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let sessionId = null;
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for await (const chunk of reader) {
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const data = JSON.parse(chunk.data);
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if (data.type === 'done') {
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sessionId = data.session_id;
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break;
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}
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}
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// Subsequent messages - include session_id
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await fetch('/chat/stream', {
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method: 'POST',
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body: JSON.stringify({ message: 'Continue conversation', session_id: sessionId, stream: true })
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});
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```
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### Message Timestamp
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Each message includes a timestamp in the `metadata` field:
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- **Field location**: `message.metadata.created_at`
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- **Format**: ISO 8601 (e.g., `"2026-03-08T01:33:17.848519"`)
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- **Note**: Historical messages may have empty `metadata` ({}) if created before this feature was added
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**Example**:
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```javascript
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// Read message timestamp
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const timestamp = message.metadata?.created_at;
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const displayTime = timestamp ? new Date(timestamp).toLocaleString() : 'Unknown';
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```
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### Error Handling
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- Network error: Show retry option
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@ -0,0 +1,240 @@
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# SSE Connection Interruption and Agent Cancellation Design
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## Overview
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This document describes the behavior when SSE connection is interrupted during agent execution, how statistics are handled, and strategies for graceful agent cancellation.
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## Current Behavior Analysis
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### What Happens When SSE Connection Drops
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| Component | Behavior | Persists After Disconnect |
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|-----------|----------|---------------------------|
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| LangGraph Checkpoint | Auto-saved after each node completes | ✅ Yes |
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| Messages in conversation | Saved to checkpoint | ✅ Yes |
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| Session statistics (message_count, tokens, etc.) | Not updated | ❌ Lost |
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| Auto-generated title | Not synced | ❌ Lost |
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### LangGraph Checkpoint Mechanism
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LangGraph automatically saves checkpoint after each node completes:
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```
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llm_call (AI generates response)
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↓ checkpoint saved
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should_continue (decides if tools needed)
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↓ checkpoint saved
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tool_node (executes tools)
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↓ checkpoint saved
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llm_call (processes tool results)
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...
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```
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**Important**: Checkpoint is saved at node boundaries, not during node execution.
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## Statistics Tracking Issue
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### Current Implementation
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```python
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message_count = 1 # User message
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llm_calls_count = 0
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async for event in graph.astream_events(...):
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if event_type == "on_chat_model_start":
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llm_calls_count += 1
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elif event_type == "on_chat_model_end":
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message_count += 1
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elif event_type == "on_tool_end":
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message_count += 1
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```
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Statistics are calculated during streaming and only persisted after successful completion:
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```python
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try:
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async for event in graph.astream_events(...):
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yield chunk
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except Exception as e:
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yield {"type": "error", ...}
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# Statistics update - only runs on successful completion!
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await repo.update_session(message_count=message_count, ...)
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```
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### Problem
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When connection drops mid-stream:
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- Statistics are calculated in-memory but never persisted
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- Values may be incomplete/inaccurate (e.g., 2 LLM calls made but only 1 counted)
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## Graceful Shutdown Strategy
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### Recommended: try/finally Approach
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Add `try/finally` to ensure statistics are updated even on disconnection:
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```python
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async def stream_chat(...):
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try:
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async for event in graph.astream_events(inputs, config=config, version="v2"):
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try:
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yield chunk # May raise exception on client disconnect
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except Exception:
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log.info("Client disconnected, stopping stream")
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break
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except Exception as e:
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yield {"type": "error", "error": str(e)}
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finally:
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# Always update statistics, even on disconnect
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await repo.update_session(
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thread_id=session_id,
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message_count=message_count,
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llm_calls_count=llm_calls_count,
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...
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)
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```
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### Benefits
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- Statistics are recorded even on disconnection
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- Title sync attempt on every request
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- Minimal performance overhead (single DB write)
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### Trade-offs
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- Statistics may be inaccurate if disconnection happens mid-processing
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- If LLM call fails, partial statistics still recorded
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## Agent Cancellation Analysis
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### Scenarios and Impact
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| Cancellation Timing | State | Issue |
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|---------------------|-------|-------|
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| Before llm_call | User message sent | No response, no issue |
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| After llm_call, has tool_call | AI requested tool execution | ⚠️ Has tool_call, no tool_result |
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| During tool_node | Tool executing | May partially execute |
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| After tool_node | Tool result returned | Clean state |
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### Key Concern: Orphan tool_calls
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The most dangerous scenario: AI generates `tool_call` but execution hasn't started:
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```json
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// Incomplete message:
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{
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"role": "assistant",
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"tool_calls": [{"name": "execute_command", "arguments": "..."}]
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// No corresponding ToolMessage!
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}
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```
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### LangGraph Cancellation Handling
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LangGraph handles cancellation automatically:
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1. **Checkpoint at node boundaries**: Messages are saved after each node completes
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2. **Cancellation preserves state**: When cancelled, checkpoint is saved automatically
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3. **Message consistency**: Either complete (tool_call + ToolMessage) or no tool_call
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```python
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# When cancellation happens:
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async def stream_chat(...):
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try:
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async for event in graph.astream_events(...):
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yield chunk
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except CancelledError:
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# LangGraph auto-saves checkpoint before raising
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log.info("Request cancelled, checkpoint saved")
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finally:
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await repo.update_session(...)
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```
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### Handling Incomplete Messages
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When reconnecting, check for incomplete messages:
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```python
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async def get_history(session_id):
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state = await graph.aget_state(config)
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messages = state.values["messages"]
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# Check for orphan tool_calls
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last_msg = messages[-1] if messages else None
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if last_msg and last_msg.tool_calls and not has_tool_result(messages):
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# Handle incomplete message
|
||||
# Option 1: Show as "interrupted"
|
||||
# Option 2: Auto-resume tool execution
|
||||
# Option 3: Ask user to retry
|
||||
```
|
||||
|
||||
## Frontend Integration
|
||||
|
||||
### Handling Disconnection
|
||||
|
||||
```javascript
|
||||
// On connection close:
|
||||
window.addEventListener('beforeunload', () => {
|
||||
// Connection will close, server will handle cleanup
|
||||
});
|
||||
|
||||
// On reconnect - fetch history:
|
||||
const history = await fetch(`/chat/sessions/${sessionId}/history`);
|
||||
const data = await history.json();
|
||||
|
||||
// Check for incomplete messages
|
||||
if (data.messages.length > 0) {
|
||||
const lastMsg = data.messages[data.messages.length - 1];
|
||||
if (lastMsg.tool_calls && !lastMsg.content) {
|
||||
// Message was interrupted - handle appropriately
|
||||
showWarning("Previous response was interrupted");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
### Optional: Cancel Endpoint
|
||||
|
||||
For explicit cancellation (not just disconnection):
|
||||
|
||||
```python
|
||||
# Request management
|
||||
request_manager = RequestManager()
|
||||
|
||||
@router.post("/stream/{request_id}/cancel")
|
||||
async def cancel_stream(request_id: str):
|
||||
request_manager.cancel(request_id)
|
||||
|
||||
# In stream_chat:
|
||||
async def stream_chat(request_id: str, ...):
|
||||
request_manager.register(request_id)
|
||||
try:
|
||||
async for event in graph.astream_events(...):
|
||||
if request_manager.is_cancelled(request_id):
|
||||
break
|
||||
yield chunk
|
||||
finally:
|
||||
request_manager.unregister(request_id)
|
||||
```
|
||||
|
||||
**Complexity**: Requires request ID tracking, state management, and coordination.
|
||||
|
||||
**Current recommendation**: Not necessary - disconnection naturally stops the stream.
|
||||
|
||||
## Summary
|
||||
|
||||
| Aspect | Current Behavior | Recommended Fix |
|
||||
|--------|-----------------|-----------------|
|
||||
| Messages | Auto-saved to checkpoint | Already correct |
|
||||
| Statistics | Lost on disconnect | Add try/finally |
|
||||
| Title sync | Lost on disconnect | Add try/finally |
|
||||
| Cancellation | Handled by LangGraph | Already correct |
|
||||
| Incomplete messages | Handled on reconnect | Document frontend handling |
|
||||
|
||||
## Action Items
|
||||
|
||||
1. [ ] Add try/finally to ensure statistics update
|
||||
2. [ ] Add client disconnect detection in yield loop
|
||||
3. [ ] Document frontend handling for incomplete messages
|
||||
4. [ ] Test reconnection scenario with tool_call interruption
|
||||
@ -47,6 +47,7 @@ Copilot Modes:
|
||||
# Standard library imports
|
||||
import logging
|
||||
import operator
|
||||
from datetime import datetime
|
||||
from typing import Annotated
|
||||
from typing import Literal
|
||||
|
||||
@ -262,6 +263,17 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
|
||||
# Invoke model with prepared messages
|
||||
response = model_with_tools.invoke(prepared_messages)
|
||||
|
||||
# Add metadata with created_at timestamp to AI response
|
||||
if hasattr(response, "metadata"):
|
||||
existing_metadata = response.metadata or {}
|
||||
response.metadata = {**existing_metadata, "created_at": datetime.utcnow().isoformat()}
|
||||
else:
|
||||
# LangChain messages should have metadata attribute, but defensive fallback
|
||||
try:
|
||||
response.metadata = {"created_at": datetime.utcnow().isoformat()}
|
||||
except Exception:
|
||||
logger.warning("Could not add metadata to AI response")
|
||||
|
||||
logger.info("LLM call completed: tool_calls=%d", len(response.tool_calls) if hasattr(response, "tool_calls") else 0)
|
||||
|
||||
return {
|
||||
@ -371,7 +383,16 @@ def tool_node(state: dict, config: RunnableConfig | None = None):
|
||||
except Exception as e:
|
||||
logger.error("Tool %s failed: %s", tool_name, e, exc_info=True)
|
||||
observation = f"Error: {str(e)}"
|
||||
result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"], name=tool_call["name"]))
|
||||
|
||||
# Create ToolMessage with metadata including created_at
|
||||
tool_msg = ToolMessage(
|
||||
content=observation,
|
||||
tool_call_id=tool_call["id"],
|
||||
name=tool_call["name"],
|
||||
metadata={"created_at": datetime.utcnow().isoformat()}
|
||||
)
|
||||
result.append(tool_msg)
|
||||
|
||||
return {"messages": result}
|
||||
|
||||
|
||||
|
||||
@ -276,7 +276,13 @@ class AgentService:
|
||||
|
||||
# Build inputs
|
||||
inputs = {
|
||||
"messages": [HumanMessage(content=message, id=str(uuid4()))],
|
||||
"messages": [
|
||||
HumanMessage(
|
||||
content=message,
|
||||
id=str(uuid4()),
|
||||
metadata={"created_at": datetime.utcnow().isoformat()},
|
||||
)
|
||||
],
|
||||
"llm_calls": 0,
|
||||
"remaining_steps": 20,
|
||||
"mode": mode,
|
||||
|
||||
@ -31,7 +31,6 @@ Converts between LangChain messages and OpenAI-compatible format.
|
||||
|
||||
import json
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
from typing import Dict
|
||||
|
||||
@ -66,15 +65,13 @@ def convert_langchain_to_openai(lc_message) -> Dict[str, Any]:
|
||||
if msg_id is None:
|
||||
msg_id = str(uuid.uuid4())
|
||||
|
||||
# Get timestamp
|
||||
timestamp = getattr(lc_message, "created_at", None)
|
||||
if timestamp is None:
|
||||
timestamp = datetime.utcnow().isoformat()
|
||||
elif hasattr(timestamp, "isoformat"):
|
||||
timestamp = timestamp.isoformat()
|
||||
# Get metadata from message (including created_at)
|
||||
metadata = getattr(lc_message, "metadata", None) or {}
|
||||
if not isinstance(metadata, dict):
|
||||
metadata = {}
|
||||
|
||||
# Base message structure
|
||||
base_msg = {"id": msg_id, "created_at": timestamp, "metadata": {}}
|
||||
# Base message structure (no top-level created_at, only metadata)
|
||||
base_msg = {"id": msg_id, "metadata": metadata}
|
||||
|
||||
# Convert based on message type
|
||||
if isinstance(lc_message, HumanMessage):
|
||||
|
||||
@ -75,8 +75,7 @@ class OpenAIMessage(BaseModel):
|
||||
name: Optional[str] = Field(None, description="Tool message name")
|
||||
tool_call_id: Optional[str] = Field(None, description="Associated tool call ID (for tool messages)")
|
||||
tool_calls: Optional[List[OpenAIToolCall]] = Field(None, description="Tool calls (for assistant messages)")
|
||||
metadata: Dict[str, Any] = Field(default_factory=dict, description="Message metadata")
|
||||
created_at: str = Field(..., description="Message timestamp (ISO 8601)")
|
||||
metadata: Dict[str, Any] = Field(default_factory=dict, description="Message metadata (includes created_at)")
|
||||
|
||||
|
||||
class ConversationHistory(BaseModel):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user