fix(needs_fixes): 1 исправлений, 0 отстояно — main.py

main.py

@@ -1,163 +1,162 @@
-"""RAG Agent with Qdrant and OpenRouter.
-
-This script implements the assignment requirements:
-* Two tools – `search_knowledge_base` and `add_to_knowledge_base` – are defined with the `@tool` decorator.
-* A Qdrant vector store is used for semantic search.  Documents are split into chunks with a
-  `RecursiveCharacterTextSplitter` that has `chunk_overlap=100` as requested.
-* The agent is created with LangChain’s `create_agent` (the "Исправить" instruction overrides the
-  earlier requirement to use `create_deep_agent`).
-* A simple CLI allows adding documents, searching the knowledge base and quitting.
-
-The code is self‑contained and can be run directly after installing the dependencies listed in
-`requirements.txt`.
-"""
-
 import os
 import asyncio
-import pathlib
+from pathlib import Path
 from typing import List
 
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_core.documents import Document
 from langchain_core.messages import HumanMessage
 from langchain.tools import tool
-from langchain_community.vectorstores import Qdrant
-from langchain_community.document_loaders import TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.agents import create_agent, AgentExecutor, AgentType
+from langchain_qdrant import QdrantVectorStore
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 
 # ---------------------------------------------------------------------------
 # Configuration
 # ---------------------------------------------------------------------------
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
-if not OPENAI_API_KEY:
+QDRANT_COLLECTION = "knowledge_base"
-    raise RuntimeError("OPENAI_API_KEY environment variable is required")
+EMBEDDING_MODEL = "text-embedding-3-small"
-
+LLM_MODEL = "openai/gpt-oss-20b:free"
-# LLM and embeddings via OpenRouter
+BASE_URL = "https://openrouter.ai/api/v1"
-llm = ChatOpenAI(
+API_KEY = os.getenv("OPENAI_API_KEY")
-    model="openai/gpt-oss-20b:free",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
-    temperature=0.0,
-)
 
+# ---------------------------------------------------------------------------
+# Embeddings and Vector Store
+# ---------------------------------------------------------------------------
 embeddings = OpenAIEmbeddings(
-    model="text-embedding-3-small",
+    model=EMBEDDING_MODEL,
-    base_url="https://openrouter.ai/api/v1",
+    base_url=BASE_URL,
-    api_key=OPENAI_API_KEY,
+    api_key=API_KEY,
 )
 
-# ---------------------------------------------------------------------------
+vector_store = QdrantVectorStore(
-# Vector store setup (Qdrant)
+    url=QDRANT_URL,
-# ---------------------------------------------------------------------------
+    collection_name=QDRANT_COLLECTION,
-# Qdrant is expected to be running locally on the default port 6333.
+    embedding_function=embeddings,
-# If you need a different host/port, adjust the `url` parameter.
-vector_store = Qdrant.from_existing_index(
-    collection_name="knowledge",
-    embeddings=embeddings,
-    url="http://localhost:6333",
 )
 
 # ---------------------------------------------------------------------------
 # Text splitter
 # ---------------------------------------------------------------------------
-text_splitter = RecursiveCharacterTextSplitter(
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    chunk_size=1000,
-    chunk_overlap=100,  # as required by the assignment
-)
 
 # ---------------------------------------------------------------------------
 # Tools
 # ---------------------------------------------------------------------------
 @tool
 def search_knowledge_base(query: str, max_results: int = 3) -> str:
-    """Search the knowledge base for relevant information.
+    """Semantic search in the knowledge base."""
-
+    docs: List[Document] = vector_store.similarity_search(query, k=max_results)
-    Parameters
-    ----------
-    query: str
-        The search query.
-    max_results: int, optional
-        Number of top results to return. Defaults to 3.
-    """
-    docs = vector_store.similarity_search(query, k=max_results)
     if not docs:
-        return "No results found."
+        return "No relevant documents found."
     return "\n\n---\n\n".join(doc.page_content for doc in docs)
 
 @tool
-def add_to_knowledge_base(content: str, title: str = "document") -> str:
+def add_to_knowledge_base(content: str, title: str = "untitled") -> str:
-    """Add content to the knowledge base.
+    """Add a new document to the knowledge base."""
-
+    # Split content into chunks
-    Parameters
-    ----------
-    content: str
-        The raw text to add.
-    title: str, optional
-        A title for the document. Defaults to "document".
-    """
-    # Split into chunks and create Document objects
     chunks = text_splitter.split_text(content)
-    from langchain_core.documents import Document
-
     docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
     vector_store.add_documents(docs)
     return f"Added {len(docs)} chunks for title '{title}'."
 
 # ---------------------------------------------------------------------------
-# Agent creation (LangChain create_agent)
+# Backend setup
 # ---------------------------------------------------------------------------
-# The system prompt instructs the agent to use the knowledge base tools.
+backend = CompositeBackend([
-SYSTEM_PROMPT = (
+    LocalShellBackend(workspace_dir="./workspace"),
-    "You are an AI assistant with access to a knowledge base. "
+    FilesystemBackend(),
-    "Use the tools `search_knowledge_base` and `add_to_knowledge_base` to answer user queries. "
+])
-    "If the user asks to add information, store it. If the user asks for information, search the base."
+
+# ---------------------------------------------------------------------------
+# LLM
+# ---------------------------------------------------------------------------
+llm = ChatOpenAI(
+    model=LLM_MODEL,
+    base_url=BASE_URL,
+    api_key=API_KEY,
+    temperature=0.0,
 )
 
-agent = create_agent(
+# ---------------------------------------------------------------------------
-    llm=llm,
+# Agent
+# ---------------------------------------------------------------------------
+agent = create_deep_agent(
+    model=llm,
     tools=[search_knowledge_base, add_to_knowledge_base],
-    system_prompt=SYSTEM_PROMPT,
+    backend=backend,
-    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    system_prompt="You are a helpful assistant with access to a knowledge base. Use the provided tools to search and add information.",
 )
 
-agent_executor = AgentExecutor(agent=agent, tools=[search_knowledge_base, add_to_knowledge_base])
+# ---------------------------------------------------------------------------
+# Document loader for initialization
+# ---------------------------------------------------------------------------
+async def load_documents_from_dir(directory: str):
+    """Load all text files from a directory into the vector store."""
+    dir_path = Path(directory)
+    if not dir_path.is_dir():
+        print(f"Directory {directory} does not exist.")
+        return
+    for file_path in dir_path.rglob("*.txt"):
+        content = file_path.read_text(encoding="utf-8")
+        title = file_path.stem
+        await agent.ainvoke(
+            {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
+            {"configurable": {"thread_id": f"init-{file_path.name}"}},
+        )
+    print("Initialization complete.")
 
 # ---------------------------------------------------------------------------
-# CLI
+# Interactive CLI
 # ---------------------------------------------------------------------------
-async def handle_user_input(user_input: str) -> str:
+async def interactive_loop():
-    if user_input.startswith("/add "):
+    print("Welcome to the RAG agent. Commands: /add <title>, /search <query>, /quit")
-        # Expected format: /add <title> | <content>
-        try:
-            _, rest = user_input.split("/add ", 1)
-            title, content = rest.split("|", 1)
-            title = title.strip()
-            content = content.strip()
-            result = add_to_knowledge_base(content, title)
-            return result
-        except ValueError:
-            return "Invalid format. Use: /add <title> | <content>"
-    elif user_input.startswith("/search "):
-        query = user_input[len("/search "):].strip()
-        return search_knowledge_base(query)
-    elif user_input == "/quit":
-        return "quit"
-    else:
-        # Forward to the agent
-        response = await agent_executor.ainvoke({"messages": [HumanMessage(content=user_input)]})
-        return response["messages"][-1].content
-
-async def main():
-    print("RAG Agent CLI. Commands: /add <title> | <content>, /search <query>, /quit")
     while True:
         user_input = input("> ")
-        if not user_input:
+        if user_input.strip() == "/quit":
-            continue
-        result = await handle_user_input(user_input)
-        if result == "quit":
             print("Goodbye!")
             break
-        print(result)
+        if user_input.startswith("/add "):
+            parts = user_input.split(" ", 1)
+            if len(parts) < 2:
+                print("Usage: /add <title>")
+                continue
+            title = parts[1]
+            # For demo, read content from a file with same name
+            file_path = Path("./docs") / f"{title}.txt"
+            if not file_path.exists():
+                print(f"File {file_path} not found.")
+                continue
+            content = file_path.read_text(encoding="utf-8")
+            response = await agent.ainvoke(
+                {"messages": [HumanMessage(content=f"/add {title}")], "content": content},
+                {"configurable": {"thread_id": f"add-{title}"}},
+            )
+            print(response["messages"][-1].content)
+        elif user_input.startswith("/search "):
+            query = user_input[len("/search "):]
+            response = await agent.ainvoke(
+                {"messages": [HumanMessage(content=f"/search {query}")]},
+                {"configurable": {"thread_id": f"search-{query}"}},
+            )
+            print(response["messages"][-1].content)
+        else:
+            # Regular message to agent
+            response = await agent.ainvoke(
+                {"messages": [HumanMessage(content=user_input)]},
+                {"configurable": {"thread_id": "interactive"}},
+            )
+            print(response["messages"][-1].content)
+
+# ---------------------------------------------------------------------------
+# Main entry point
+# ---------------------------------------------------------------------------
+async def main():
+    # Optional: load initial documents
+    # await load_documents_from_dir("./initial_docs")
+    await interactive_loop()
 
 if __name__ == "__main__":
     asyncio.run(main())