Remove extra solution.py

solution.py

@@ -1,100 +0,0 @@
-"""
-Agent with RAG memory using Qdrant and Ollama.
-
-This is a minimal example that demonstrates:
-1. Connecting to a local Qdrant instance.
-2. Creating two tools: `search_knowledge_base` and `add_to_knowledge_base`.
-3. Using LangChain's RecursiveCharacterTextSplitter to chunk documents.
-4. Building an agent with the tools via `create_agent`.
-5. A simple REPL that accepts `/add`, `/search` and `/quit` commands.
-
-To run:
-    pip install -r requirements.txt
-    python main.py
-
-Make sure a Qdrant instance is running locally (default port 6333) and Ollama is available at http://localhost:11434.
-"""
-
-import os
-from pathlib import Path
-from typing import List, Dict
-
-from langchain_community.document_loaders import DirectoryLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.embeddings.ollama import OllamaEmbeddings
-from langchain.vectorstores.qdrant import QdrantVectorStore
-from langchain.agents import create_agent, AgentExecutor, Tool
-from langchain.schema import Document
-
-# Configuration
-QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")
-OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama2")
-COLLECTION_NAME = "rag_collection"
-
-# Initialize embeddings and vector store
-embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
-vector_store = QdrantVectorStore(
-    url=QDRANT_URL,
-    collection_name=COLLECTION_NAME,
-    embedding_function=embeddings,
-)
-
-# Tool: search knowledge base
-
-def search_knowledge_base(query: str) -> str:
-    """Return the top 3 relevant snippets for a query."""
-    docs = vector_store.similarity_search_with_score(query, k=3)
-    if not docs:
-        return "No relevant documents found."
-    results = [f"{idx+1}. {doc[0].page_content[:200]}… (score: {doc[1]:.4f})" for idx, doc in enumerate(docs)]
-    return "\n".join(results)
-
-# Tool: add to knowledge base
-
-def add_to_knowledge_base(file_path: str) -> str:
-    """Load a text file, chunk it and add to Qdrant."""
-    loader = DirectoryLoader(Path(file_path).parent.as_posix(), glob=Path(file_path).name)
-    docs = loader.load()
-    if not docs:
-        return f"No documents found in {file_path}."
-    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    chunks: List[Document] = []
-    for doc in docs:
-        chunks.extend(splitter.split_documents([doc]))
-    vector_store.add_documents(chunks)
-    return f"Added {len(chunks)} chunks from {file_path} to the knowledge base."
-
-# Define tools list
-TOOLS: List[Tool] = [
-    Tool(
-        name="search_knowledge_base",
-        func=search_knowledge_base,
-        description="Search the local Qdrant knowledge base for relevant information.",
-    ),
-    Tool(
-        name="add_to_knowledge_base",
-        func=add_to_knowledge_base,
-        description="Add a text file to the knowledge base. Provide full path.",
-    ),
-]
-
-# Build agent
-agent = create_agent(TOOLS, llm=embeddings)  # embeddings can act as LLM via Ollama
-executor = AgentExecutor(agent=agent, tools=TOOLS, verbose=True)
-
-# Simple REPL
-if __name__ == "__main__":
-    print("Welcome to the RAG agent. Commands: /add <file>, /search <query>, /quit")
-    while True:
-        try:
-            inp = input(">>> ")
-        except EOFError:
-            break
-        if not inp:
-            continue
-        if inp.startswith("/quit"):
-            print("Goodbye!")
-            break
-        elif inp.startswith("/add "):
-            path = inp.split(maxsplit=1)[1]
-            print(add_to_knowledge_base(path))