feat: RAG Integration (#238)

* feat: add rag provider and retriever

* feat: retriever tool

* feat: add retriever tool to the researcher node

* feat: add rag http apis

* feat: new message input supports resource mentions

* feat: new message input component support resource mentions

* refactor: need_web_search to need_search

* chore: RAG integration docs

* chore: change example api host

* fix: user message color in dark mode

* fix: mentions style

* feat: add local_search_tool to researcher prompt

* chore: research prompt

* fix: ragflow page size and reporter with

* docs: ragflow integration and add acknowledgment projects

* chore: format

src/server/app.py

@@ -5,19 +5,22 @@ import base64
 import json
 import logging
 import os
-from typing import List, cast
+from typing import Annotated, List, cast
 from uuid import uuid4
 
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, Query
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import Response, StreamingResponse
 from langchain_core.messages import AIMessageChunk, ToolMessage, BaseMessage
 from langgraph.types import Command
 
+from src.config.tools import SELECTED_RAG_PROVIDER
 from src.graph.builder import build_graph_with_memory
 from src.podcast.graph.builder import build_graph as build_podcast_graph
 from src.ppt.graph.builder import build_graph as build_ppt_graph
 from src.prose.graph.builder import build_graph as build_prose_graph
+from src.rag.builder import build_retriever
+from src.rag.retriever import Resource
 from src.server.chat_request import (
     ChatMessage,
     ChatRequest,
@@ -28,6 +31,11 @@ from src.server.chat_request import (
 )
 from src.server.mcp_request import MCPServerMetadataRequest, MCPServerMetadataResponse
 from src.server.mcp_utils import load_mcp_tools
+from src.server.rag_request import (
+    RAGConfigResponse,
+    RAGResourceRequest,
+    RAGResourcesResponse,
+)
 from src.tools import VolcengineTTS
 
 logger = logging.getLogger(__name__)
@@ -59,6 +67,7 @@ async def chat_stream(request: ChatRequest):
         _astream_workflow_generator(
             request.model_dump()["messages"],
             thread_id,
+            request.resources,
             request.max_plan_iterations,
             request.max_step_num,
             request.max_search_results,
@@ -74,6 +83,7 @@ async def chat_stream(request: ChatRequest):
 async def _astream_workflow_generator(
     messages: List[ChatMessage],
     thread_id: str,
+    resources: List[Resource],
     max_plan_iterations: int,
     max_step_num: int,
     max_search_results: int,
@@ -101,6 +111,7 @@ async def _astream_workflow_generator(
         input_,
         config={
             "thread_id": thread_id,
+            "resources": resources,
             "max_plan_iterations": max_plan_iterations,
             "max_step_num": max_step_num,
             "max_search_results": max_search_results,
@@ -319,3 +330,18 @@ async def mcp_server_metadata(request: MCPServerMetadataRequest):
             logger.exception(f"Error in MCP server metadata endpoint: {str(e)}")
             raise HTTPException(status_code=500, detail=str(e))
         raise
+
+
+@app.get("/api/rag/config", response_model=RAGConfigResponse)
+async def rag_config():
+    """Get the config of the RAG."""
+    return RAGConfigResponse(provider=SELECTED_RAG_PROVIDER)
+
+
+@app.get("/api/rag/resources", response_model=RAGResourcesResponse)
+async def rag_resources(request: Annotated[RAGResourceRequest, Query()]):
+    """Get the resources of the RAG."""
+    retriever = build_retriever()
+    if retriever:
+        return RAGResourcesResponse(resources=retriever.list_resources(request.query))
+    return RAGResourcesResponse(resources=[])

src/server/chat_request.py

@@ -5,6 +5,8 @@ from typing import List, Optional, Union
 
 from pydantic import BaseModel, Field
 
+from src.rag.retriever import Resource
+
 
 class ContentItem(BaseModel):
     type: str = Field(..., description="The type of content (text, image, etc.)")
@@ -28,6 +30,9 @@ class ChatRequest(BaseModel):
     messages: Optional[List[ChatMessage]] = Field(
         [], description="History of messages between the user and the assistant"
     )
+    resources: Optional[List[Resource]] = Field(
+        [], description="Resources to be used for the research"
+    )
     debug: Optional[bool] = Field(False, description="Whether to enable debug logging")
     thread_id: Optional[str] = Field(
         "__default__", description="A specific conversation identifier"

src/server/rag_request.py

@@ -0,0 +1,25 @@
+from pydantic import BaseModel, Field
+
+from src.rag.retriever import Resource
+
+
+class RAGConfigResponse(BaseModel):
+    """Response model for RAG config."""
+
+    provider: str | None = Field(
+        None, description="The provider of the RAG, default is ragflow"
+    )
+
+
+class RAGResourceRequest(BaseModel):
+    """Request model for RAG resource."""
+
+    query: str | None = Field(
+        None, description="The query of the resource need to be searched"
+    )
+
+
+class RAGResourcesResponse(BaseModel):
+    """Response model for RAG resources."""
+
+    resources: list[Resource] = Field(..., description="The resources of the RAG")