Merge branch 'developing' into bot_manager

This commit is contained in:
朱潮 2026-04-04 23:24:17 +08:00
commit 314a693793
6 changed files with 632 additions and 10 deletions

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@ -108,7 +108,7 @@ workflows:
filters: filters:
branches: branches:
only: only:
- master - dev
- deploy: - deploy:
name: deploy-for-test name: deploy-for-test
docker-tag: '' docker-tag: ''
@ -120,7 +120,7 @@ workflows:
filters: filters:
branches: branches:
only: only:
- master - dev
requires: requires:
- build-for-test - build-for-test
- build-and-push: - build-and-push:
@ -183,7 +183,7 @@ workflows:
filters: filters:
branches: branches:
only: only:
- onprem - onprem-release
- docker-hub-build-push: - docker-hub-build-push:
name: docker-hub-build-push-arm name: docker-hub-build-push-arm
repo: gptbasesparticle/catalog-agent repo: gptbasesparticle/catalog-agent
@ -192,4 +192,31 @@ workflows:
filters: filters:
branches: branches:
only: only:
- onprem - onprem-release
# 为 onprem-dev 环境部署
- build-and-push:
name: build-for-onprem-dev
context:
- ecr-new
path: .
dockerfile: Dockerfile
repo: catalog-agent
docker-tag: ''
filters:
branches:
only:
- onprem-dev
- deploy:
name: deploy-for-onprem-dev
docker-tag: ''
path: '/home/ubuntu/cluster-for-B/onprem-dev/catalog-agent/deploy.yaml'
deploy-name: catalog-agent
deploy-namespace: onprem-dev
context:
- ecr-new
filters:
branches:
only:
- onprem-dev
requires:
- build-for-onprem-dev

435
docs/file_manage_apis.md Normal file
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@ -0,0 +1,435 @@
# 文件管理 API 接口文档
**Base URL:** `/api/v1/file-manager`
---
## 1. 列出目录内容
**GET** `/api/v1/file-manager/list`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | query | 否 | 相对路径空字符串返回根目录projects/prompt |
| recursive | bool | query | 否 | 是否递归列出子目录,默认 false |
```bash
curl -X GET "/api/v1/file-manager/list?path=projects&recursive=false"
```
响应示例:
```json
{
"success": true,
"path": "projects",
"items": [
{
"name": "report.pdf",
"path": "projects/report.pdf",
"type": "file",
"size": 102400,
"modified": 1700000000.0,
"created": 1699000000.0
}
],
"total": 1
}
```
---
## 2. 上传文件
**POST** `/api/v1/file-manager/upload`
请求方式表单数据上传multipart/form-data
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| file | file | form | 是 | 上传的文件 |
| path | string | form | 否 | 目标目录路径,默认 `projects` |
```bash
curl -X POST "/api/v1/file-manager/upload" \
-F "file=@document.pdf" \
-F "path=projects/dataset"
```
响应示例:
```json
{
"success": true,
"message": "文件上传成功",
"filename": "document.pdf",
"original_filename": "document.pdf",
"path": "projects/dataset/document.pdf",
"size": 102400
}
```
> 注:文件名中的空格会自动替换为下划线。
---
## 3. 下载文件
**GET** `/api/v1/file-manager/download/{file_path}`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| file_path | string | path | 是 | 文件相对路径 |
```bash
curl -X GET "/api/v1/file-manager/download/projects/report.pdf" -o report.pdf
```
响应:文件二进制流,`Content-Type` 根据文件类型自动推断。
---
## 4. 删除文件或目录
**DELETE** `/api/v1/file-manager/delete`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | query | 是 | 要删除的路径 |
```bash
curl -X DELETE "/api/v1/file-manager/delete?path=projects/report.pdf"
```
响应示例:
```json
{
"success": true,
"message": "文件删除成功",
"path": "projects/report.pdf"
}
```
---
## 5. 创建文件夹
**POST** `/api/v1/file-manager/create-folder`
请求方式JSON
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | body | 否 | 父目录路径,默认 `projects` |
| name | string | body | 是 | 文件夹名称 |
```bash
curl -X POST "/api/v1/file-manager/create-folder" \
-H "Content-Type: application/json" \
-d '{"path": "projects", "name": "new-folder"}'
```
响应示例:
```json
{
"success": true,
"message": "文件夹创建成功",
"path": "projects/new-folder"
}
```
---
## 6. 重命名文件或文件夹
**POST** `/api/v1/file-manager/rename`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| old_path | string | query | 是 | 原路径 |
| new_name | string | query | 是 | 新名称 |
```bash
curl -X POST "/api/v1/file-manager/rename?old_path=projects/old-name.pdf&new_name=new-name.pdf"
```
响应示例:
```json
{
"success": true,
"message": "重命名成功",
"old_path": "projects/old-name.pdf",
"new_path": "projects/new-name.pdf"
}
```
---
## 7. 移动文件或文件夹
**POST** `/api/v1/file-manager/move`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| source_path | string | query | 是 | 源路径 |
| target_path | string | query | 是 | 目标路径 |
```bash
curl -X POST "/api/v1/file-manager/move?source_path=projects/a.pdf&target_path=projects/sub/a.pdf"
```
响应示例:
```json
{
"success": true,
"message": "移动成功",
"source_path": "projects/a.pdf",
"target_path": "projects/sub/a.pdf"
}
```
---
## 8. 复制文件或文件夹
**POST** `/api/v1/file-manager/copy`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| source_path | string | query | 是 | 源路径 |
| target_path | string | query | 是 | 目标路径 |
```bash
curl -X POST "/api/v1/file-manager/copy?source_path=projects/a.pdf&target_path=projects/a-copy.pdf"
```
响应示例:
```json
{
"success": true,
"message": "复制成功",
"source_path": "projects/a.pdf",
"target_path": "projects/a-copy.pdf"
}
```
---
## 9. 搜索文件
**GET** `/api/v1/file-manager/search`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| query | string | query | 是 | 搜索关键词(匹配文件名) |
| path | string | query | 否 | 搜索路径,为空则搜索所有支持目录 |
| file_type | string | query | 否 | 文件类型过滤(如 `.pdf` |
```bash
curl -X GET "/api/v1/file-manager/search?query=report&path=projects&file_type=.pdf"
```
响应示例:
```json
{
"success": true,
"query": "report",
"path": "projects",
"results": [
{
"name": "report.pdf",
"path": "projects/report.pdf",
"type": "file"
}
],
"total": 1
}
```
---
## 10. 读取文件内容
**GET** `/api/v1/file-manager/read`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | query | 是 | 文件相对路径 |
```bash
curl -X GET "/api/v1/file-manager/read?path=projects/config.json"
```
响应示例:
```json
{
"success": true,
"content": "{\"key\": \"value\"}",
"path": "projects/config.json",
"size": 16,
"modified": 1700000000.0,
"encoding": "utf-8",
"mime_type": "application/json"
}
```
> 限制:最大 10MB。自动尝试 utf-8 / gbk / gb2312 / latin-1 编码。
---
## 11. 保存文件内容
**POST** `/api/v1/file-manager/save`
请求方式JSON
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | body | 是 | 文件相对路径 |
| content | string | body | 是 | 文件内容 |
```bash
curl -X POST "/api/v1/file-manager/save" \
-H "Content-Type: application/json" \
-d '{"path": "projects/config.json", "content": "{\"key\": \"new-value\"}"}'
```
响应示例:
```json
{
"success": true,
"message": "文件保存成功",
"path": "projects/config.json",
"size": 20,
"modified": 1700000000.0,
"encoding": "utf-8"
}
```
> 限制:最大 5MB。保存前自动创建备份写入失败时自动回滚。
---
## 12. 获取文件/文件夹详细信息
**GET** `/api/v1/file-manager/info/{file_path}`
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| file_path | string | path | 是 | 文件或文件夹路径 |
```bash
curl -X GET "/api/v1/file-manager/info/projects/report.pdf"
```
响应示例:
```json
{
"success": true,
"info": {
"name": "report.pdf",
"path": "projects/report.pdf",
"type": "file",
"size": 102400,
"modified": 1700000000.0,
"created": 1699000000.0,
"permissions": "644",
"mime_type": "application/pdf",
"preview": "文件前1000字符预览..."
}
}
```
> 小于 1MB 的文本文件会包含 `preview` 字段。
---
## 13. 下载文件夹为 ZIP
**POST** `/api/v1/file-manager/download-folder-zip`
请求方式JSON
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| path | string | body | 是 | 文件夹路径 |
```bash
curl -X POST "/api/v1/file-manager/download-folder-zip" \
-H "Content-Type: application/json" \
-d '{"path": "projects/dataset"}' -o dataset.zip
```
响应ZIP 文件二进制流(`application/zip`)。
> 限制:最大 500MB最多 10000 个文件。
---
## 14. 批量下载为 ZIP
**POST** `/api/v1/file-manager/download-multiple-zip`
请求方式JSON
| 参数 | 类型 | 位置 | 必填 | 说明 |
|------|------|------|------|------|
| paths | string[] | body | 是 | 文件/文件夹路径数组 |
| filename | string | body | 否 | ZIP 文件名,默认 `batch_download.zip` |
```bash
curl -X POST "/api/v1/file-manager/download-multiple-zip" \
-H "Content-Type: application/json" \
-d '{"paths": ["projects/a.pdf", "projects/sub"], "filename": "export.zip"}' \
-o export.zip
```
响应ZIP 文件二进制流(`application/zip`)。
> 限制:最大 500MB最多 10000 个文件。
---
## 15. 批量操作
**POST** `/api/v1/file-manager/batch-operation`
请求方式JSON操作数组
支持操作类型:`delete` / `move` / `copy`
```bash
curl -X POST "/api/v1/file-manager/batch-operation" \
-H "Content-Type: application/json" \
-d '[
{"type": "delete", "path": "projects/old.pdf"},
{"type": "move", "source_path": "projects/a.pdf", "target_path": "projects/archive/a.pdf"},
{"type": "copy", "source_path": "projects/b.pdf", "target_path": "projects/backup/b.pdf"}
]'
```
响应示例:
```json
{
"success": true,
"results": [
{"type": "delete", "success": true, "message": "删除成功"},
{"type": "move", "success": true, "message": "移动成功"},
{"type": "copy", "success": true, "message": "复制成功"}
],
"total": 3,
"successful": 3
}
```
---
## 通用说明
- **路径约束**:所有路径必须以 `projects``prompt` 开头,否则返回 400
- **安全机制**:路径经过规范化处理,防止目录遍历攻击
- **隐藏文件**:以 `.` 开头的文件/目录在列出和搜索时自动跳过
- **错误响应格式**
```json
{
"detail": "错误描述信息"
}
```

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@ -148,6 +148,21 @@ Output: {{"facts" : []}}
Input: DR1の照明状態を教えて Input: DR1の照明状態を教えて
Output: {{"facts" : []}} Output: {{"facts" : []}}
Input: 私は林檎好きです
Output: {{"facts" : ["林檎が好き"]}}
Input: コーヒー飲みたい、毎朝
Output: {{"facts" : ["毎朝コーヒーを飲みたい"]}}
Input: 昨日映画見た、すごくよかった
Output: {{"facts" : ["昨日映画を見た", "映画がすごくよかった"]}}
Input: 我喜欢吃苹果
Output: {{"facts" : ["喜欢吃苹果"]}}
Input: 나는 사과를 좋아해
Output: {{"facts" : ["사과를 좋아함"]}}
Return the facts and preferences in a json format as shown above. Return the facts and preferences in a json format as shown above.
Remember the following: Remember the following:
@ -159,12 +174,15 @@ Remember the following:
- If you do not find anything relevant in the below conversation, you can return an empty list corresponding to the "facts" key. - If you do not find anything relevant in the below conversation, you can return an empty list corresponding to the "facts" key.
- Create the facts based on the user and assistant messages only. Do not pick anything from the system messages. - Create the facts based on the user and assistant messages only. Do not pick anything from the system messages.
- Make sure to return the response in the format mentioned in the examples. The response should be in json with a key as "facts" and corresponding value will be a list of strings. - Make sure to return the response in the format mentioned in the examples. The response should be in json with a key as "facts" and corresponding value will be a list of strings.
- **CRITICAL - Do NOT memorize actions or operations**: Do not extract facts about queries the user asked you to perform, devices the user asked you to operate, or any one-time transient actions. Only memorize information ABOUT the user (preferences, relationships, personal details, plans), not actions the user asked the assistant to DO. Ask yourself: "Is this a fact about WHO the user IS, or what the user asked me to DO?" Only remember the former. - **CRITICAL - Do NOT memorize actions or operations**: Do not extract facts about queries the user asked you to perform, devices the user asked you to operate, or any one-time transient actions. Only memorize information ABOUT the user (preferences, relationships, personal details, plans), not actions the user asked the assistant to DO. Ask yourself: "Is this a fact about WHO the user IS, or what the user asked me to DO?" Only remember the former.
- **CRITICAL for Semantic Completeness**: - **CRITICAL for Semantic Completeness**:
- Each extracted fact MUST preserve the complete semantic meaning. Never truncate or drop key parts of the meaning. - Each extracted fact MUST preserve the complete semantic meaning. Never truncate or drop key parts of the meaning.
- For colloquial or grammatically informal expressions (common in spoken Japanese, Chinese, Korean, etc.), understand the full intended meaning and record it in a clear, semantically complete form. - For colloquial or grammatically informal expressions (common in spoken Japanese, Chinese, Korean, etc.), understand the full intended meaning and record it in a clear, semantically complete form.
- In Japanese, spoken language often omits particles (e.g., が, を, に). When extracting facts, include the necessary particles to make the meaning unambiguous. For example: "私は林檎好きです" should be understood as "林檎が好き" (likes apples), not literally "私は林檎好き". - In Japanese, spoken language often omits particles (e.g., が, を, に). When extracting facts, include the necessary particles to make the meaning unambiguous. For example: "私は林檎好きです" should be understood as "林檎が好き" (likes apples), not literally "私は林檎好き".
- When the user expresses a preference or opinion in casual speech, record the core preference/opinion clearly. Remove the subject pronoun (私は/I) since facts are about the user by default, but keep all other semantic components intact. - When the user expresses a preference or opinion in casual speech, record the core preference/opinion clearly. Remove the subject pronoun (私は/I) since facts are about the user by default, but keep all other semantic components intact.
- **CRITICAL for People/Relationship Tracking**: - **CRITICAL for People/Relationship Tracking**:
- Write people-related facts in plain, natural language. Do NOT use structured formats like "Contact:", "referred as", or "DEFAULT when user says". - Write people-related facts in plain, natural language. Do NOT use structured formats like "Contact:", "referred as", or "DEFAULT when user says".
- Good examples: "Michael Johnson is a colleague, also called Mike", "田中さんは友達", "滨田太郎は「滨田」とも呼ばれている" - Good examples: "Michael Johnson is a colleague, also called Mike", "田中さんは友達", "滨田太郎は「滨田」とも呼ばれている"

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@ -14,7 +14,19 @@ The filesystem backend is currently operating in: `{agent_dir_path}`
- Never use relative paths in bash commands - always construct full absolute paths - Never use relative paths in bash commands - always construct full absolute paths
- Use the working directory from <env> to construct absolute paths - Use the working directory from <env> to construct absolute paths
**2. Skill Script Path Conversion** **2. Skills vs Tools - CRITICAL DISTINCTION**
**Skills are NOT tools.** Do NOT attempt to call a skill as a tool_call/function_call.
- **Tools** (e.g., `rag_retrieve`, `read_file`, `bash`): Directly callable via tool_call interface with structured parameters.
- **Skills** (e.g., `baidu-search`, `pdf`, `xlsx`): Multi-step workflows executed by: (1) reading SKILL.md, (2) extracting the command, (3) running it via the `bash` tool.
❌ WRONG: Generating a tool_call with `{"name": "baidu-search", "arguments": {...}}`
✅ CORRECT: Using `read_file` to read SKILL.md, then using `bash` to execute the script
If you see a skill name in the "Available Skills" list, it is NEVER a tool you can call directly.
**3. Skill Script Path Conversion**
When executing scripts from SKILL.md files, you MUST convert relative paths to absolute paths: When executing scripts from SKILL.md files, you MUST convert relative paths to absolute paths:
@ -29,14 +41,14 @@ When executing scripts from SKILL.md files, you MUST convert relative paths to a
└── scriptB.py # Actual script B file └── scriptB.py # Actual script B file
``` ```
**3. Workspace Directory Structure** **4. Workspace Directory Structure**
- **`{agent_dir_path}/skills/`** - Skill packages with embedded scripts - **`{agent_dir_path}/skills/`** - Skill packages with embedded scripts
- **`{agent_dir_path}/dataset/`** - Store file datasets and document data - **`{agent_dir_path}/dataset/`** - Store file datasets and document data
- **`{agent_dir_path}/executable_code/`** - Place generated executable scripts here (not skill scripts) - **`{agent_dir_path}/executable_code/`** - Place generated executable scripts here (not skill scripts)
- **`{agent_dir_path}/download/`** - Store downloaded files and content - **`{agent_dir_path}/download/`** - Store downloaded files and content
**4. Executable Code Organization** **5. Executable Code Organization**
When creating scripts in `executable_code/`, follow these organization rules: When creating scripts in `executable_code/`, follow these organization rules:
@ -80,3 +92,15 @@ Trace Id: {trace_id}
- Even when the user writes in a different language, you MUST still reply in [{language}]. - Even when the user writes in a different language, you MUST still reply in [{language}].
- Do NOT mix languages. Do NOT fall back to English or any other language under any circumstances. - Do NOT mix languages. Do NOT fall back to English or any other language under any circumstances.
- Technical terms, code identifiers, file paths, and tool names may remain in their original form, but all surrounding text MUST be in [{language}]. - Technical terms, code identifiers, file paths, and tool names may remain in their original form, but all surrounding text MUST be in [{language}].
**Citation Requirement (RAG Only)**: When answering questions based on `rag_retrieve` tool results, you MUST add XML citation tags for factual claims derived from the knowledge base.
**MANDATORY FORMAT**: `The cited factual claim <CITATION file="file_uuid" page="3" />`
**Citation Rules**:
- The citation tag MUST be placed immediately after the factual claim or paragraph
- The `file` attribute MUST use the exact `File ID` from `rag_retrieve` document
- The `page` attribute MUST use the exact `Page Number` from `rag_retrieve` document
- If multiple sources support the same claim, include separate citation tags for each source
- Example: `According to the policy, returns are accepted within 30 days <CITATION file="abc123" page="5" />.`
- This requirement ONLY applies when using `rag_retrieve` results to answer questions.

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@ -83,6 +83,18 @@ When using the write_todos tool:
The todo list is a planning tool - use it judiciously to avoid overwhelming the user with excessive task tracking. The todo list is a planning tool - use it judiciously to avoid overwhelming the user with excessive task tracking.
### Skills vs Tools - CRITICAL DISTINCTION
**Skills are NOT tools.** Do NOT attempt to call a skill as a tool_call/function_call.
- **Tools** (e.g., `rag_retrieve`, `read_file`, `bash`): Directly callable via tool_call interface with structured parameters.
- **Skills** (e.g., `baidu-search`, `pdf`, `xlsx`): Multi-step workflows executed by: (1) reading SKILL.md, (2) extracting the command, (3) running it via the `bash` tool.
❌ WRONG: Generating a tool_call with `{"name": "baidu-search", "arguments": {...}}`
✅ CORRECT: Using `read_file` to read SKILL.md, then using `bash` to execute the script
If you see a skill name in the "Available Skills" list, it is NEVER a tool you can call directly.
### Skill Execution Workflow ### Skill Execution Workflow
**CRITICAL**: When you need to use a skill, follow this exact workflow: **CRITICAL**: When you need to use a skill, follow this exact workflow:

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@ -1,13 +1,13 @@
""" """
Memory 管理 API 路由 Memory 管理 API 路由
提供记忆查看和删除功能 提供记忆查看添加和删除功能
""" """
import logging import logging
from typing import Optional, List, Dict, Any from typing import Literal, Optional, List, Dict, Any
from fastapi import APIRouter, HTTPException, Header, Query from fastapi import APIRouter, HTTPException, Header, Query
from fastapi.responses import JSONResponse from fastapi.responses import JSONResponse
from pydantic import BaseModel from pydantic import BaseModel, Field
logger = logging.getLogger('app') logger = logging.getLogger('app')
@ -33,6 +33,26 @@ class DeleteAllResponse(BaseModel):
deleted_count: int deleted_count: int
class ConversationMessage(BaseModel):
"""对话消息"""
role: Literal["user", "assistant"]
content: str = Field(..., min_length=1)
class AddMemoryRequest(BaseModel):
"""添加记忆的请求体"""
bot_id: str = Field(..., min_length=1)
user_id: str = Field(..., min_length=1)
messages: List[ConversationMessage] = Field(..., max_length=200)
class AddMemoryResponse(BaseModel):
"""添加记忆的响应"""
success: bool
pairs_processed: int
pairs_failed: int = 0
async def get_user_identifier_from_request( async def get_user_identifier_from_request(
authorization: Optional[str], authorization: Optional[str],
user_id: Optional[str] = None user_id: Optional[str] = None
@ -63,6 +83,92 @@ async def get_user_identifier_from_request(
) )
@router.post("/memory", response_model=AddMemoryResponse)
async def add_memory_from_conversation(data: AddMemoryRequest):
"""
从对话消息中提取并保存记忆
将用户和助手的对话配对通过 Mem0 提取关键事实并存储
用于 realtime 语音对话等不经过 Agent 中间件的场景
此端点供内部服务调用 felo-mygpt不暴露给外部用户
"""
try:
from agent.mem0_manager import get_mem0_manager
from utils.settings import MEM0_ENABLED
if not MEM0_ENABLED:
raise HTTPException(
status_code=503,
detail="Memory feature is not enabled"
)
if not data.messages:
return AddMemoryResponse(success=True, pairs_processed=0)
manager = get_mem0_manager()
# 将消息配对为 user-assistant 对,然后调用 add_memory
pairs_processed = 0
pairs_failed = 0
i = 0
while i < len(data.messages):
msg = data.messages[i]
if msg.role == 'user':
# 收集连续的 user 消息
user_contents = [msg.content]
j = i + 1
while j < len(data.messages) and data.messages[j].role == 'user':
user_contents.append(data.messages[j].content)
j += 1
user_text = '\n'.join(user_contents)
# 检查是否有对应的 assistant 回复
assistant_text = ""
if j < len(data.messages) and data.messages[j].role == 'assistant':
assistant_text = data.messages[j].content or ""
j += 1
if user_text and assistant_text:
conversation_text = f"User: {user_text}\nAssistant: {assistant_text}"
try:
await manager.add_memory(
text=conversation_text,
user_id=data.user_id,
agent_id=data.bot_id,
metadata={"type": "realtime_conversation"},
)
pairs_processed += 1
except Exception as pair_error:
pairs_failed += 1
logger.error(
f"Failed to add memory for pair: {pair_error}"
)
i = j
else:
i += 1
logger.info(
f"Added {pairs_processed} memory pairs (failed={pairs_failed}) "
f"for user={data.user_id}, bot={data.bot_id}"
)
return AddMemoryResponse(
success=pairs_failed == 0,
pairs_processed=pairs_processed,
pairs_failed=pairs_failed,
)
except HTTPException:
raise
except Exception as e:
logger.error(f"Failed to add memory from conversation: {e}")
raise HTTPException(
status_code=500,
detail="Failed to add memory from conversation"
)
@router.get("/memory", response_model=MemoryListResponse) @router.get("/memory", response_model=MemoryListResponse)
async def get_memories( async def get_memories(
bot_id: str = Query(..., description="Bot ID (对应 agent_id)"), bot_id: str = Query(..., description="Bot ID (对应 agent_id)"),