Удалить rag_agent.py

rag_agent.py

@@ -1,99 +0,0 @@
-"""
-RAG agent implementation with Qdrant and Ollama.
-"""
-
-import os
-from pathlib import Path
-from typing import List, Dict
-
-from langchain_ollama import ChatOllama, OllamaEmbeddings
-from langchain_qdrant import QdrantVectorStore
-from langchain.tools import tool
-from langchain_core.messages import HumanMessage
-from langchain.agents import create_agent
-
-# Initialize LLM and embeddings using Ollama
-LLM_MODEL = "llama3"
-EMBEDDING_MODEL = "nomic-embed-text"
-
-llm = ChatOllama(model=LLM_MODEL, temperature=0.0)
-embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
-
-# Qdrant client (in‑memory for simplicity)
-from qdrant_client import QdrantClient
-from qdrant_client.models import Distance, VectorParams
-
-client = QdrantClient(":memory:")
-COLLECTION_NAME = "knowledge"
-if not client.collection_exists(COLLECTION_NAME):
-    client.create_collection(
-        COLLECTION_NAME,
-        vectors_config=VectorParams(size=embeddings.embedding_size, distance=Distance.COSINE),
-    )
-vector_store = QdrantVectorStore(client=client, collection_name=COLLECTION_NAME, embedding=embeddings)
-
-# Tool: add to knowledge base
-@tool
-def add_to_knowledge_base(content: str, title: str = "document") -> str:
-    """Add a document to the vector store.
-
-    Parameters
-    ----------
-    content: str
-        Raw text of the document.
-    title: str, optional
-        Title or identifier for the document.
-    Returns
-    -------
-    str
-        Confirmation message.
-    """
-    # Split into chunks using chunker module
-    from chunker import split_text
-    chunks = split_text(content)
-    docs = []
-    for i, chunk in enumerate(chunks):
-        meta = {"title": title, "chunk_index": str(i)}
-        docs.append({"page_content": chunk, "metadata": meta})
-    vector_store.add_documents(docs)
-    return f"Added {len(chunks)} chunks from '{title}'."
-
-# Tool: search knowledge base
-@tool
-def search_knowledge_base(query: str, max_results: int = 5) -> str:
-    """Semantic search in the vector store.
-
-    Parameters
-    ----------
-    query: str
-        Search query.
-    max_results: int, optional
-        Number of top results to return.
-    Returns
-    -------
-    str
-        Formatted search results.
-    """
-    docs = vector_store.similarity_search(query, k=max_results)
-    if not docs:
-        return "No relevant documents found."
-    lines = []
-    for i, doc in enumerate(docs, 1):
-        title = doc.metadata.get("title", "unknown")
-        chunk_idx = doc.metadata.get("chunk_index", "0")
-        lines.append(f"{i}. [{title} - chunk {chunk_idx}]\n{doc.page_content[:200]}...")
-    return "\n\n".join(lines)
-
-# Create agent with tools
-SYSTEM_PROMPT = (
-    "You are an assistant that can search and add documents to a knowledge base."
-    " Use the provided tools to manage the knowledge base."
-)
-agent = create_agent(
-    llm=llm,
-    tools=[add_to_knowledge_base, search_knowledge_base],
-    system_prompt=SYSTEM_PROMPT,
-)
-
-# Expose agent for external use
-__all__ = ["agent", "add_to_knowledge_base", "search_knowledge_base"]