移除路由机制，所有任务都使用一个llm完成

deepsearcher/agent/rag_router.py

@@ -1,8 +1,6 @@
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 from deepsearcher.agent import RAGAgent
-from deepsearcher.llm.base import BaseLLM
-from deepsearcher.utils import log
 from deepsearcher.vector_db import RetrievalResult
 
 RAG_ROUTER_PROMPT = """Given a list of agent indexes and corresponding descriptions, each agent has a specific function. 
@@ -28,9 +26,7 @@ class RAGRouter(RAGAgent):
 
     def __init__(
         self,
-        llm: BaseLLM,
+        agent: RAGAgent
-        rag_agents: List[RAGAgent],
-        agent_descriptions: Optional[List[str]] = None,
     ):
         """
         Initialize the RAGRouter.
@@ -40,54 +36,12 @@ class RAGRouter(RAGAgent):
             rag_agents: A list of RAGAgent instances.
             agent_descriptions (list, optional): A list of descriptions for each agent.
         """
-        self.llm = llm
+        self.agent = agent
-        self.rag_agents = rag_agents
-        self.agent_descriptions = agent_descriptions
-        if not self.agent_descriptions:
-            try:
-                self.agent_descriptions = [
-                    agent.__class__.__description__ for agent in self.rag_agents
-                ]
-            except Exception:
-                raise AttributeError(
-                    "Please provide agent descriptions or set __description__ attribute for each agent class."
-                )
-
-    def _route(self, query: str) -> Tuple[RAGAgent, int]:
-        description_str = "\n".join(
-            [f"[{i + 1}]: {description}" for i, description in enumerate(self.agent_descriptions)]
-        )
-        prompt = RAG_ROUTER_PROMPT.format(query=query, description_str=description_str)
-        chat_response = self.llm.chat(messages=[{"role": "user", "content": prompt}])
-        try:
-            selected_agent_index = int(self.llm.remove_think(chat_response.content)) - 1
-        except ValueError:
-            # In some reasoning LLM, the output is not a number, but a explaination string with a number in the end.
-            log.warning(
-                "Parse int failed in RAGRouter, but will try to find the last digit as fallback."
-            )
-            selected_agent_index = (
-                int(self.find_last_digit(self.llm.remove_think(chat_response.content))) - 1
-            )
-
-        selected_agent = self.rag_agents[selected_agent_index]
-        log.color_print(
-            f"<think> Select agent [{selected_agent.__class__.__name__}] to answer the query [{query}] </think>\n"
-        )
-        return self.rag_agents[selected_agent_index], chat_response.total_tokens
 
     def retrieve(self, query: str, **kwargs) -> Tuple[List[RetrievalResult], int, dict]:
-        agent, n_token_router = self._route(query)
+        retrieved_results, n_token_retrieval, metadata = self.agent.retrieve(query, **kwargs)
-        retrieved_results, n_token_retrieval, metadata = agent.retrieve(query, **kwargs)
+        return retrieved_results, n_token_retrieval, metadata
-        return retrieved_results, n_token_router + n_token_retrieval, metadata
 
     def query(self, query: str, **kwargs) -> Tuple[str, List[RetrievalResult], int]:
-        agent, n_token_router = self._route(query)
+        answer, retrieved_results, n_token_retrieval = self.agent.query(query, **kwargs)
-        answer, retrieved_results, n_token_retrieval = agent.query(query, **kwargs)
+        return answer, retrieved_results, n_token_retrieval
-        return answer, retrieved_results, n_token_router + n_token_retrieval
-
-    def find_last_digit(self, string):
-        for char in reversed(string):
-            if char.isdigit():
-                return char
-        raise ValueError("No digit found in the string")

deepsearcher/configuration.py

@@ -3,7 +3,7 @@ from typing import Literal
 
 import yaml
 
-from deepsearcher.agent import ChainOfRAG, DeepSearch, NaiveRAG
+from deepsearcher.agent import DeepSearch, NaiveRAG
 from deepsearcher.agent.rag_router import RAGRouter
 from deepsearcher.embedding.base import BaseEmbedding
 from deepsearcher.llm.base import BaseLLM
@@ -210,25 +210,14 @@ def init_config(config: Configuration):
     vector_db = module_factory.create_vector_db()
 
     default_searcher = RAGRouter(
-        llm=llm,
+        agent=DeepSearch(
-        rag_agents=[
+            llm=llm,
-            DeepSearch(
+            embedding_model=embedding_model,
-                llm=llm,
+            vector_db=vector_db,
-                embedding_model=embedding_model,
+            max_iter=config.query_settings["max_iter"],
-                vector_db=vector_db,
+            route_collection=True,
-                max_iter=config.query_settings["max_iter"],
+            text_window_splitter=True,
-                route_collection=True,
+        )
-                text_window_splitter=True,
-            ),
-            ChainOfRAG(
-                llm=llm,
-                embedding_model=embedding_model,
-                vector_db=vector_db,
-                max_iter=config.query_settings["max_iter"],
-                route_collection=True,
-                text_window_splitter=True,
-            ),
-        ],
     )
     naive_rag = NaiveRAG(
         llm=llm,

deepsearcher/loader/file_loader/base.py

@@ -37,7 +37,7 @@ class BaseLoader(ABC):
             In the metadata, it's recommended to include the reference to the file.
             e.g. return [Document(page_content=..., metadata={"reference": file_path})]
         """
-        return []
+        pass
 
     def load_directory(self, directory: str) -> List[Document]:
         """
@@ -55,9 +55,7 @@ class BaseLoader(ABC):
                 for suffix in self.supported_file_types:
                     if file.endswith(suffix):
                         full_path = os.path.join(root, file)
-                        loaded_docs = self.load_file(full_path)
+                        documents.extend(self.load_file(full_path))
-                        if loaded_docs is not None:
-                            documents.extend(loaded_docs)
                         break
         return documents
 
@@ -69,4 +67,4 @@ class BaseLoader(ABC):
         Returns:
             A list of supported file extensions (without the dot).
         """
-        return []
+        pass

deepsearcher/offline_loading.py

@@ -1,5 +1,5 @@
-import os
 import hashlib
+import os
 from typing import List, Union
 
 from tqdm import tqdm