feat: solution for 6a02e23da6fe2e4ac16acf65

solutions/6a02e23da6fe2e4ac16acf65/solution.py

@@ -11,10 +11,10 @@ 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.text_splitter import RecursiveCharacterTextSplitter
 # Agent
 from langchain.agents import create_agent
-# Document type
+# Documents
 from langchain_core.documents import Document
 
 # -------------------- 1. RAG tools --------------------
@@ -26,7 +26,7 @@ def search_knowledge_base(query: str, max_results: int = 5) -> str:
         return "No relevant documents found."
     response_lines = []
     for doc, score in results:
-        title = doc.metadata.get("title", "Untitled")
+        title = doc.metadata.get("title", "N/A")
         snippet = doc.page_content[:200] + ("..." if len(doc.page_content) > 200 else "")
         response_lines.append(f"Score: {score:.4f}\nTitle: {title}\nContent: {snippet}")
     return "\n\n".join(response_lines)
@@ -38,76 +38,92 @@ def add_to_knowledge_base(content: str, title: str) -> str:
     vector_store.add_documents([doc])
     return f"Document '{title}' added successfully."
 
-# -------------------- 2. Vector store setup --------------------
-client = QdrantClient(":memory:")
-client.create_collection(
-    collection_name="knowledge",
+# -------------------- 2. Qdrant setup --------------------
+qdrant_client = QdrantClient(":memory:")
+qdrant_client.create_collection(
+    collection_name="knowledge_base",
     vectors_config=VectorParams(size=384, distance=Distance.COSINE),
 )
 
 embeddings = OllamaEmbeddings(model="nomic-embed-text")
 vector_store = QdrantVectorStore(
-    client=client,
-    collection_name="knowledge",
+    client=qdrant_client,
+    collection_name="knowledge_base",
     embedding=embeddings,
 )
 
 # -------------------- 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"):
+def load_and_index(directory: str):
+    """Load all .txt files from directory and index them."""
+    docs: List[Document] = []
+    for file_path in Path(directory).glob("*.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}"})
+                Document(
+                    page_content=chunk,
+                    metadata={
+                        "title": f"{file_path.stem} #{i+1}",
+                        "source": str(file_path),
+                    },
+                )
             )
-    return docs
-
-def init_knowledge_base(directory: str):
-    docs = load_docs_from_dir(directory)
     vector_store.add_documents(docs)
 
-# -------------------- 6. Interactive CLI --------------------
+# -------------------- 4. Agent --------------------
+system_prompt = """
+You are an assistant that can search and add documents to a knowledge base.
+Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
+"""
+
+agent = create_agent(
+    model=ChatOllama(model="llama3", temperature=0.2),
+    tools=[search_knowledge_base, add_to_knowledge_base],
+    system_prompt=system_prompt,
+)
+
+# -------------------- 5. CLI client --------------------
 def main():
-    print("Initializing knowledge base...")
-    init_knowledge_base("./docs")  # replace with your docs folder
-    print("Ready! Use /add, /search, or /quit.")
+    # Load initial documents
+    load_and_index("docs")  # ensure a 'docs' folder with .txt files
+
+    print(
+        "RAG Agent ready. Commands: /add <title> <content>, /search <query>, /quit"
+    )
     while True:
         user_input = input("> ").strip()
         if not user_input:
             continue
-        if user_input.lower() == "/quit":
+        if user_input.lower() in ("exit", "quit", "/quit"):
             break
+
         if user_input.startswith("/add"):
             try:
                 _, title, content = user_input.split(" ", 2)
-                result = add_to_knowledge_base(content=content, title=title)
-                print(result)
+                result_msg = add_to_knowledge_base(content=content, title=title)
+                print(result_msg)
             except ValueError:
                 print("Usage: /add <title> <content>")
         elif user_input.startswith("/search"):
-            query = user_input[len("/search"):].strip()
+            query = user_input[len("/search") :].strip()
             if not query:
                 print("Provide a search query.")
                 continue
-            result = search_knowledge_base(query=query, max_results=3)
-            print(result)
+            response = agent.invoke({"messages": [{"role": "human", "content": query}]})
+            for msg in response["messages"]:
+                if hasattr(msg, "content"):
+                    print(msg.content)
         else:
-            # Regular chat with agent
-            response = agent.invoke({"messages": [{"role": "human", "content": user_input}]})
-            ai_msg = response["messages"][-1]
-            print(ai_msg.content)
+            # Regular chat with the agent
+            response = agent.invoke(
+                {"messages": [{"role": "human", "content": user_input}]}
+            )
+            for msg in response["messages"]:
+                if hasattr(msg, "content"):
+                    print(msg.content)
 
 if __name__ == "__main__":
     main()