Удалить agent.py

agent.py

@@ -1,79 +0,0 @@
-"""
-Agent definition for the RAG system.
-
-Provides two tools:
-* search_knowledge_base(query, max_results)
-* add_to_knowledge_base(content, title)
-
-The agent is created with create_agent from langchain.agents.
-"""
-
-import os
-from typing import List
-from langchain_ollama import OllamaEmbeddings
-from chromadb import PersistentClient
-from chromadb.utils import embedding_functions as ef
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.tools import tool
-from langchain.agents import create_agent
-from langchain_core.messages import HumanMessage
-
-# Initialize embeddings and Chroma client
-EMBEDDINGS = OllamaEmbeddings(model="nomic-embed-text")
-CHROMA_PATH = os.path.join(os.getcwd(), "chromadb_store")
-CLIENT = PersistentClient(path=CHROMA_PATH)
-COLLECTION_NAME = "knowledge"
-if COLLECTION_NAME not in CLIENT.list_collections():
-    CLIENT.create_collection(name=COLLECTION_NAME, embedding_function=EMBEDDINGS)
-COLL = CLIENT.get_or_create_collection(name=COLLECTION_NAME, embedding_function=EMBEDDINGS)
-
-# Chunker from chunker.py
-from chunker import CHUNKER
-
-@tool
-def add_to_knowledge_base(content: str, title: str) -> str:
-    """
-    Add a document to the knowledge base.
-    The content is split into chunks and stored with metadata.
-    Returns confirmation message.
-    """
-    # Split content
-    chunks = CHUNKER.split_text(content)
-    ids = [f"{title}_{i}" for i in range(len(chunks))]
-    metadatas = [{"title": title} for _ in chunks]
-    COLL.add(ids=ids, documents=chunks, metadatas=metadatas)
-    return f"Added {len(chunks)} chunks from '{title}'."
-
-@tool
-def search_knowledge_base(query: str, max_results: int = 5) -> str:
-    """
-    Search the knowledge base for relevant documents.
-    Returns a formatted string of results.
-    """
-    results = COLL.query(
-        query_texts=[query],
-        n_results=max_results,
-        include=['documents', 'distances'],
-    )
-    docs = results.get("documents", [])[0]
-    dists = results.get("distances", [])[0]
-    if not docs:
-        return "No relevant documents found."
-    output_lines = []
-    for i, (doc, dist) in enumerate(zip(docs, dists), 1):
-        output_lines.append(f"{i}. (score: {dist:.4f})\n{doc[:200]}...")
-    return "\n\n".join(output_lines)
-
-# Create agent
-SYSTEM_PROMPT = (
-    "You are an assistant that can search and add to a knowledge base. Use the provided tools."
-)
-AGENT = create_agent(
-    llm=None,  # No LLM needed for tool calls; agent will use system prompt only
-    tools=[add_to_knowledge_base, search_knowledge_base],
-    system_prompt=SYSTEM_PROMPT,
-)
-
-# Expose a simple invoke function
-async def run_agent(messages: List[HumanMessage]):
-    return await AGENT.ainvoke({"messages": messages}, {"configurable": {"thread_id": "rag-agent"}})