Add in memory RAG and graph generation script

app.py

@@ -10,15 +10,29 @@ from langchain.schema.runnable.config import RunnableConfig
 from langchain_community.tools.tavily_search import TavilySearchResults
 
 import chainlit as cl
+from rag import create_rag_pipeline, add_urls_to_vectorstore
+
+# Initialize RAG pipeline
+rag_components = create_rag_pipeline(collection_name="london_events")
+
+# Add some initial URLs to the vector store
+urls = [
+    "https://www.timeout.com/london/things-to-do-in-london-this-weekend",
+    "https://www.timeout.com/london/london-events-in-march"
+]
+add_urls_to_vectorstore(
+    rag_components["vector_store"],
+    rag_components["text_splitter"],
+    urls
+)
 
 class AgentState(TypedDict):
-  messages: Annotated[list, add_messages]
+    messages: Annotated[list, add_messages]
+    context: list  # Store retrieved context
 
 tavily_tool = TavilySearchResults(max_results=5)
 tool_belt = [tavily_tool]
-# Initialize the language models
-# llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)
-# final_llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0).with_config(tags=["final_node"])
+
 model = ChatOpenAI(model="gpt-4o", temperature=0)
 model = model.bind_tools(tool_belt)
 
@@ -27,17 +41,35 @@ SYSTEM_PROMPT = SystemMessage(content="""
 You are a helpful AI assistant that answers questions clearly and concisely.
 If you don't know something, simply say you don't know.
 Be engaging and professional in your responses.
+Use the provided context when available to give accurate information about events and activities.
 """)
 
+def retrieve(state: AgentState):
+    """Retrieve relevant context from the vector store"""
+    # Get the last message's content
+    last_message = state["messages"][-1]
+    if isinstance(last_message, HumanMessage):
+        # Get relevant documents
+        docs = rag_components["retriever"].get_relevant_documents(last_message.content)
+        # Extract the content from documents
+        context = [doc.page_content for doc in docs]
+        return {"context": context}
+    return {"context": []}
 
 def call_model(state: AgentState):
-    messages = state["messages"]
+    messages = [SYSTEM_PROMPT] + state["messages"]
+    
+    # Add context to system message if available
+    if state.get("context"):
+        context_str = "\n".join(state["context"])
+        context_message = SystemMessage(content=f"Context:\n{context_str}")
+        messages = [messages[0], context_message] + messages[1:]
+    
     response = model.invoke(messages)
-    return {"messages" : [response]}
+    return {"messages": [response]}
 
 tool_node = ToolNode(tool_belt)
 
-
 # Simple flow control - always go to final
 def should_continue(state):
   last_message = state["messages"][-1]
@@ -50,19 +82,25 @@ def should_continue(state):
 # Create the graph
 builder = StateGraph(AgentState)
 
-builder.set_entry_point("agent")
+# Add nodes
+builder.add_node("retrieve", retrieve)
 builder.add_node("agent", call_model)
 builder.add_node("action", tool_node)
+
 # Add edges
+builder.set_entry_point("retrieve")
+builder.add_edge("retrieve", "agent")
 builder.add_conditional_edges(
     "agent",
     should_continue,
 )
-
 builder.add_edge("action", "agent")
 
 # Compile the graph
-graph = builder.compile()
+compiled_graph = builder.compile()  # Changed variable name to be more explicit
+
+# Export the graph for visualization
+graph = compiled_graph  # This is what we'll import in the script
 
 @cl.on_chat_start
 async def on_chat_start():
@@ -87,15 +125,20 @@ async def on_message(message: cl.Message):
     
     # Stream the response
     async for chunk in graph.astream(
-        {"messages": [HumanMessage(content=message.content)]},
+        {"messages": [HumanMessage(content=message.content)], "context": []},
         config=RunnableConfig(callbacks=[cb], **config)
     ):
         for node, values in chunk.items():
-            if values.get("messages"):
+            if node == "retrieve":
+                loading_msg = cl.Message(content="🔍 Searching knowledge base...", author="System")
+                await loading_msg.send()
+            elif values.get("messages"):
                 last_message = values["messages"][-1]
                 # Check for tool calls in additional_kwargs
                 if hasattr(last_message, "additional_kwargs") and last_message.additional_kwargs.get("tool_calls"):
                     tool_name = last_message.additional_kwargs["tool_calls"][0]["function"]["name"]
+                    if loading_msg:
+                        await loading_msg.remove()
                     loading_msg = cl.Message(
                         content=f"🔍 Using {tool_name}...",
                         author="Tool"

pyproject.toml

@@ -16,6 +16,8 @@ dependencies = [
     "langchain>=0.1.8",
     "langchain-core>=0.1.23",
     "langchain-community>=0.0.19",
+    "langchain-qdrant>=0.2.0",
     "tavily-python>=0.3.1",
     "typing-extensions>=4.9.0",
+    "beautifulsoup4==4.13.3"
 ]

rag.py

@@ -0,0 +1,135 @@
+from typing import List, TypedDict
+from langchain_core.documents import Document
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_community.document_loaders import WebBaseLoader
+import tiktoken
+
+# Define state for our RAG pipeline
+class RAGState(TypedDict):
+    question: str
+    context: List[Document]
+    response: str
+
+def load_web_documents(urls: List[str]) -> List[Document]:
+    """
+    Load documents from web URLs
+    
+    Args:
+        urls: List of URLs to load
+        
+    Returns:
+        List of loaded documents
+    """
+    loader = WebBaseLoader(urls)
+    return loader.load()
+
+def create_rag_pipeline(collection_name: str = "rag_collection"):
+    # Initialize embedding model
+    embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+    embedding_dim = 1536  # Dimension for text-embedding-3-small
+
+    # Initialize Qdrant client (in-memory for development)
+    client = QdrantClient(":memory:")
+    
+    # Create collection for vectors
+    client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=embedding_dim, distance=Distance.COSINE),
+    )
+
+    # Create vector store
+    vector_store = QdrantVectorStore(
+        client=client,
+        collection_name=collection_name,
+        embedding=embedding_model,
+    )
+
+    # Create text splitter for chunking
+    def tiktoken_len(text):
+        tokens = tiktoken.encoding_for_model("gpt-4").encode(text)
+        return len(tokens)
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,  # Adjust based on your needs
+        chunk_overlap=50,
+        length_function=tiktoken_len,
+    )
+
+    # Create retriever
+    retriever = vector_store.as_retriever(search_kwargs={"k": 2})
+
+    # Create chat prompt template
+    prompt_template = """Answer the question based on the following context. If you cannot find 
+    the answer in the context, say "I don't have enough information to answer that."
+
+    Context:
+    {context}
+
+    Question:
+    {question}
+
+    Answer:"""
+
+    chat_prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are a helpful AI assistant that answers questions based on the provided context."),
+        ("human", prompt_template)
+    ])
+
+    return {
+        "vector_store": vector_store,
+        "text_splitter": text_splitter,
+        "retriever": retriever,
+        "chat_prompt": chat_prompt
+    }
+
+def add_documents(vector_store, text_splitter, documents: List[Document]):
+    """
+    Add documents to the vector store
+    
+    Args:
+        vector_store: The initialized vector store
+        text_splitter: The text splitter for chunking
+        documents: List of Document objects to add
+    """
+    # Split documents into chunks
+    chunks = []
+    for doc in documents:
+        # Split the page content of each document
+        doc_chunks = text_splitter.split_text(doc.page_content)
+        chunks.extend(doc_chunks)
+    
+    # Add chunks to vector store
+    vector_store.add_texts(texts=chunks)
+
+def add_urls_to_vectorstore(vector_store, text_splitter, urls: List[str]):
+    """
+    Load documents from URLs and add them to the vector store
+    
+    Args:
+        vector_store: The initialized vector store
+        text_splitter: The text splitter for chunking
+        urls: List of URLs to load and add
+    """
+    # Load documents from URLs
+    documents = load_web_documents(urls)
+    
+    # Add documents to vector store
+    add_documents(vector_store, text_splitter, documents)
+
+def get_relevant_context(retriever, question: str) -> List[Document]:
+    """
+    Get relevant context for a question
+    
+    Args:
+        retriever: The initialized retriever
+        question: The question to find context for
+        
+    Returns:
+        List of relevant documents
+    """
+    return retriever.get_relevant_documents(question) 

scripts/generate_graph_image.py

@@ -0,0 +1,24 @@
+import os
+import sys
+
+# Add the parent directory to the Python path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app import graph  # This will import the compiled graph
+from langchain_core.runnables.graph import MermaidDrawMethod
+
+# Create docs directory if it doesn't exist
+docs_dir = "docs"
+if not os.path.exists(docs_dir):
+    os.makedirs(docs_dir)
+
+# Generate and save the graph visualization
+graph_image = graph.get_graph().draw_mermaid_png(
+    draw_method=MermaidDrawMethod.API,
+)
+
+# Save the image
+with open(os.path.join(docs_dir, "graph.png"), "wb") as f:
+    f.write(graph_image)
+
+print("Graph visualization has been saved to docs/graph.png") 

uv.lock

@@ -6,21 +6,39 @@ name = "aie5-deploypythonicrag"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "beautifulsoup4" },
     { name = "chainlit" },
+    { name = "langchain" },
+    { name = "langchain-community" },
+    { name = "langchain-core" },
+    { name = "langchain-openai" },
+    { name = "langchain-qdrant" },
+    { name = "langgraph" },
     { name = "numpy" },
     { name = "openai" },
     { name = "pydantic" },
     { name = "pypdf2" },
+    { name = "tavily-python" },
+    { name = "typing-extensions" },
     { name = "websockets" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "beautifulsoup4", specifier = "==4.13.3" },
     { name = "chainlit", specifier = ">=2.0.4" },
+    { name = "langchain", specifier = ">=0.1.8" },
+    { name = "langchain-community", specifier = ">=0.0.19" },
+    { name = "langchain-core", specifier = ">=0.1.23" },
+    { name = "langchain-openai", specifier = ">=0.0.5" },
+    { name = "langchain-qdrant", specifier = ">=0.2.0" },
+    { name = "langgraph", specifier = ">=0.0.19" },
     { name = "numpy", specifier = ">=2.2.2" },
     { name = "openai", specifier = ">=1.59.9" },
     { name = "pydantic", specifier = "==2.10.1" },
     { name = "pypdf2", specifier = ">=3.0.1" },
+    { name = "tavily-python", specifier = ">=0.3.1" },
+    { name = "typing-extensions", specifier = ">=4.9.0" },
     { name = "websockets", specifier = ">=14.2" },
 ]
 
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