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/tools/retriever.py

@@ -0,0 +1,74 @@
+import logging
+from typing import List, Optional, Type
+from langchain_core.tools import BaseTool
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from pydantic import BaseModel, Field
+
+from src.config.tools import SELECTED_RAG_PROVIDER
+from src.rag import Document, Retriever, Resource, build_retriever
+
+logger = logging.getLogger(__name__)
+
+
+class RetrieverInput(BaseModel):
+    keywords: str = Field(description="search keywords to look up")
+
+
+class RetrieverTool(BaseTool):
+    name: str = "local_search_tool"
+    description: str = (
+        "Useful for retrieving information from the file with `rag://` uri prefix, it should be higher priority than the web search or writing code. Input should be a search keywords."
+    )
+    args_schema: Type[BaseModel] = RetrieverInput
+
+    retriever: Retriever = Field(default_factory=Retriever)
+    resources: list[Resource] = Field(default_factory=list)
+
+    def _run(
+        self,
+        keywords: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> list[Document]:
+        logger.info(
+            f"Retriever tool query: {keywords}", extra={"resources": self.resources}
+        )
+        documents = self.retriever.query_relevant_documents(keywords, self.resources)
+        if not documents:
+            return "No results found from the local knowledge base."
+        return [doc.to_dict() for doc in documents]
+
+    async def _arun(
+        self,
+        keywords: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> list[Document]:
+        return self._run(keywords, run_manager.get_sync())
+
+
+def get_retriever_tool(resources: List[Resource]) -> RetrieverTool | None:
+    if not resources:
+        return None
+    logger.info(f"create retriever tool: {SELECTED_RAG_PROVIDER}")
+    retriever = build_retriever()
+
+    if not retriever:
+        return None
+    return RetrieverTool(retriever=retriever, resources=resources)
+
+
+if __name__ == "__main__":
+    resources = [
+        Resource(
+            uri="rag://dataset/1c7e2ea4362911f09a41c290d4b6a7f0",
+            title="西游记",
+            description="西游记是中国古代四大名著之一，讲述了唐僧师徒四人西天取经的故事。",
+        )
+    ]
+    retriever_tool = get_retriever_tool(resources)
+    print(retriever_tool.name)
+    print(retriever_tool.description)
+    print(retriever_tool.args)
+    print(retriever_tool.invoke("三打白骨精"))