feat: solution for 6a02e23da6fe2e4ac16acf65

solutions/6a02e23da6fe2e4ac16acf65/solution.py

@@ -1,122 +1,113 @@
 from pathlib import Path
-import sys
+import os
+from typing import List
 
-from langchain_ollama import Ollama, OllamaEmbeddings
+# LLM and embeddings via Ollama
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+# Tools
+from langchain.tools import tool
+# Vector store
 from langchain_qdrant import QdrantVectorStore
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
+# Text splitter
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.tools import tool
+# Agent
 from langchain.agents import create_agent
+# Document type
 from langchain_core.documents import Document
 
-# ---------- LLM and embeddings ----------
+# -------------------- 1. RAG tools --------------------
-llm = Ollama(
-    model="llama3",          # local Ollama model
-    temperature=0.7,
-)
-
-embeddings = OllamaEmbeddings(model="nomic-embed-text")
-
-# ---------- Qdrant client ----------
-client = QdrantClient(":memory:")
-client.create_collection(
-    collection_name="knowledge_base",
-    vectors_config=VectorParams(size=embeddings.embedding_size, distance=Distance.COSINE),
-)
-vector_store = QdrantVectorStore(client=client, collection_name="knowledge_base", embedding=embeddings)
-
-# ---------- Text splitter ----------
-splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-
-# ---------- Tools ----------
 @tool
 def search_knowledge_base(query: str, max_results: int = 5) -> str:
     """Search the knowledge base for relevant documents."""
     results = vector_store.similarity_search_with_score(query, k=max_results)
     if not results:
-        return "No relevant information found."
+        return "No relevant documents found."
-    out_lines = []
+    response_lines = []
     for doc, score in results:
         title = doc.metadata.get("title", "Untitled")
-        content_preview = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
+        snippet = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
-        out_lines.append(f"Score: {score:.3f}\nTitle: {title}\nContent: {content_preview}")
+        response_lines.append(f"Score: {score:.4f}\nTitle: {title}\nContent: {snippet}")
-    return "\n\n".join(out_lines)
+    return "\n\n".join(response_lines)
 
 @tool
-def add_to_knowledge_base(content: str, title: str = "Untitled") -> str:
+def add_to_knowledge_base(content: str, title: str) -> str:
     """Add a new document to the knowledge base."""
-    chunks = splitter.split_text(content)
+    doc = Document(page_content=content, metadata={"title": title})
-    documents = [
+    vector_store.add_documents([doc])
-        Document(page_content=chunk, metadata={"title": f"{title} (part {i+1})"})
+    return f"Document '{title}' added successfully."
-        for i, chunk in enumerate(chunks)
-    ]
-    vector_store.add_documents(documents)
-    return f"Added {len(chunks)} chunks to the knowledge base under title '{title}'."
 
-# ---------- Agent ----------
+# -------------------- 2. Vector store setup --------------------
-system_prompt = """
+client = QdrantClient(":memory:")
-You are an assistant that can search and add information to a local knowledge base.
+client.create_collection(
-Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
+    collection_name="knowledge",
-"""
+    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
-
-agent = create_agent(
-    model=llm,
-    tools=[search_knowledge_base, add_to_knowledge_base],
-    system_prompt=system_prompt,
 )
 
-# ---------- CLI ----------
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
-def load_documents_from_dir(directory: Path):
+vector_store = QdrantVectorStore(
-    for file_path in directory.rglob("*"):
+    client=client,
-        if file_path.is_file() and file_path.suffix.lower() in {".txt", ".md"}:
+    collection_name="knowledge",
-            content = file_path.read_text(encoding="utf-8")
+    embedding=embeddings,
-            title = file_path.stem
+)
-            add_to_knowledge_base(content=content, title=title)
 
+# -------------------- 3. Text splitter --------------------
+splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+
+# -------------------- 4. Agent --------------------
+agent = create_agent(
+    model=ChatOllama(model="llama3", temperature=0.2),
+    tools=[search_knowledge_base, add_to_knowledge_base],
+    system_prompt="You are a helpful assistant that can search and add documents to the knowledge base.",
+)
+
+# -------------------- 5. Load docs from directory --------------------
+def load_docs_from_dir(directory: str) -> List[Document]:
+    docs = []
+    for file_path in Path(directory).rglob("*.txt"):
+        text = file_path.read_text(encoding="utf-8")
+        chunks = splitter.split_text(text)
+        for i, chunk in enumerate(chunks):
+            docs.append(
+                Document(page_content=chunk, metadata={"title": f"{file_path.name} #{i+1}"})
+            )
+    return docs
+
+def init_knowledge_base(directory: str):
+    docs = load_docs_from_dir(directory)
+    vector_store.add_documents(docs)
+
+# -------------------- 6. Interactive CLI --------------------
 def main():
-    # Load initial docs if provided as first arg
+    print("Initializing knowledge base...")
-    if len(sys.argv) > 1:
+    init_knowledge_base("./docs")  # replace with your docs folder
-        load_documents_from_dir(Path(sys.argv[1]))
+    print("Ready! Use /add, /search, or /quit.")
-
-    print("Agent ready. Commands: /add <title> <file>, /search <query>, /quit")
     while True:
         user_input = input("> ").strip()
         if not user_input:
             continue
-        if user_input.lower() in {"quit", "exit"} or user_input == "/quit":
+        if user_input.lower() == "/quit":
-            print("Goodbye!")
             break
-
         if user_input.startswith("/add"):
-            parts = user_input.split(maxsplit=2)
+            try:
-            if len(parts) < 3:
+                _, title, content = user_input.split(" ", 2)
-                print("Usage: /add <title> <file_path>")
+                result = add_to_knowledge_base(content=content, title=title)
-                continue
+                print(result)
-            title, file_path = parts[1], Path(parts[2])
+            except ValueError:
-            if not file_path.is_file():
+                print("Usage: /add <title> <content>")
-                print(f"File {file_path} does not exist.")
-                continue
-            content = file_path.read_text(encoding="utf-8")
-            result = add_to_knowledge_base(content=content, title=title)
-            print(result)
-
         elif user_input.startswith("/search"):
             query = user_input[len("/search"):].strip()
             if not query:
-                print("Usage: /search <query>")
+                print("Provide a search query.")
                 continue
-            response = agent.invoke({"messages": [{"role": "human", "content": query}]})
+            result = search_knowledge_base(query=query, max_results=3)
-            for msg in response["messages"]:
+            print(result)
-                if hasattr(msg, "content"):
-                    print(msg.content)
-
         else:
             # Regular chat with agent
             response = agent.invoke({"messages": [{"role": "human", "content": user_input}]})
-            for msg in response["messages"]:
+            ai_msg = response["messages"][-1]
-                if hasattr(msg, "content"):
+            print(ai_msg.content)
-                    print(msg.content)
 
 if __name__ == "__main__":
     main()