129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
from pathlib import Path
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import os
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from typing import List
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# LLM and embeddings via Ollama
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from langchain_ollama import ChatOllama, OllamaEmbeddings
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# Tools
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from langchain.tools import tool
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# Vector store
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from langchain_qdrant import QdrantVectorStore
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from qdrant_client import QdrantClient
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from qdrant_client.http.models import Distance, VectorParams
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# Text splitter
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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# Agent
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from langchain.agents import create_agent
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# Documents
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from langchain_core.documents import Document
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# -------------------- 1. RAG tools --------------------
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@tool
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def search_knowledge_base(query: str, max_results: int = 5) -> str:
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"""Search the knowledge base for relevant documents."""
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results = vector_store.similarity_search_with_score(query, k=max_results)
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if not results:
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return "No relevant documents found."
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response_lines = []
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for doc, score in results:
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title = doc.metadata.get("title", "N/A")
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snippet = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
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response_lines.append(f"Score: {score:.4f}\nTitle: {title}\nContent: {snippet}")
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return "\n\n".join(response_lines)
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@tool
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def add_to_knowledge_base(content: str, title: str) -> str:
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"""Add a new document to the knowledge base."""
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doc = Document(page_content=content, metadata={"title": title})
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vector_store.add_documents([doc])
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return f"Document '{title}' added successfully."
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# -------------------- 2. Qdrant setup --------------------
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qdrant_client = QdrantClient(":memory:")
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qdrant_client.create_collection(
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collection_name="knowledge_base",
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vectors_config=VectorParams(size=384, distance=Distance.COSINE),
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)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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vector_store = QdrantVectorStore(
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client=qdrant_client,
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collection_name="knowledge_base",
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embedding=embeddings,
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)
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# -------------------- 3. Text splitter --------------------
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splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
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def load_and_index(directory: str):
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"""Load all .txt files from directory and index them."""
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docs: List[Document] = []
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for file_path in Path(directory).glob("*.txt"):
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text = file_path.read_text(encoding="utf-8")
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chunks = splitter.split_text(text)
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for i, chunk in enumerate(chunks):
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docs.append(
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Document(
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page_content=chunk,
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metadata={
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"title": f"{file_path.stem} #{i+1}",
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"source": str(file_path),
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},
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)
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)
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vector_store.add_documents(docs)
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# -------------------- 4. Agent --------------------
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system_prompt = """
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You are an assistant that can search and add documents to a knowledge base.
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Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
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"""
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agent = create_agent(
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model=ChatOllama(model="llama3", temperature=0.2),
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tools=[search_knowledge_base, add_to_knowledge_base],
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system_prompt=system_prompt,
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)
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# -------------------- 5. CLI client --------------------
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def main():
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# Load initial documents
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load_and_index("docs") # ensure a 'docs' folder with .txt files
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print(
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"RAG Agent ready. Commands: /add <title> <content>, /search <query>, /quit"
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)
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while True:
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user_input = input("> ").strip()
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if not user_input:
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continue
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if user_input.lower() in ("exit", "quit", "/quit"):
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break
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if user_input.startswith("/add"):
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try:
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_, title, content = user_input.split(" ", 2)
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result_msg = add_to_knowledge_base(content=content, title=title)
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print(result_msg)
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except ValueError:
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print("Usage: /add <title> <content>")
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elif user_input.startswith("/search"):
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query = user_input[len("/search") :].strip()
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if not query:
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print("Provide a search query.")
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continue
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response = agent.invoke({"messages": [{"role": "human", "content": query}]})
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for msg in response["messages"]:
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if hasattr(msg, "content"):
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print(msg.content)
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else:
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# Regular chat with the agent
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response = agent.invoke(
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{"messages": [{"role": "human", "content": user_input}]}
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)
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for msg in response["messages"]:
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if hasattr(msg, "content"):
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print(msg.content)
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if __name__ == "__main__":
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main() |