first commit , uploading 3 files

Dockerfile

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+# Start with an official Python base image
+FROM python:3.10-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy your project's requirements file
+COPY requirements.txt .
+
+# Install the Python libraries
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of your application's code
+COPY . .
+
+# Expose the port your app will run on (e.g., if it's an API)
+EXPOSE 8000
+
+# Define the command to run your application
+# This assumes your main script is named "app.py"
+CMD ["python", "app.py"]

app.py

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+# ==============================================================================
+# 1. SETUP AND IMPORTS
+# ==============================================================================
+import os
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+
+# Set Google API Key securely from an environment variable
+google_api_key = os.getenv("GOOGLE_API_KEY")
+if not google_api_key:
+    raise ValueError("Google API key not found. Please set the GOOGLE_API_KEY environment variable.")
+
+# All your other imports...
+import bs4
+from langchain import hub
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from langchain_core.vectorstores import InMemoryVectorStore
+from langgraph.graph import MessagesState, StateGraph, END
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+
+# ==============================================================================
+# 2. CORE LOGIC
+# ==============================================================================
+llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=google_api_key)
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=google_api_key)
+vector_store = InMemoryVectorStore(embeddings)
+
+# Load Web Data and Split
+web_url = "https://lilianweng.github.io/posts/2023-06-23-agent/"
+loader = WebBaseLoader(
+    web_paths=(web_url,),
+    bs_kwargs=dict(parse_only=bs4.SoupStrainer(class_=("post-content", "post-title", "post-header")))
+)
+docs = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
+all_splits = text_splitter.split_documents(docs)
+vector_store.add_documents(all_splits)
+
+# Tool Definition
+@tool
+def retrieve(query: str):
+    """retrieve information related to a query."""
+    retrieved_docs = vector_store.similarity_search(query, k=2)
+    return [doc.page_content for doc in retrieved_docs]
+
+# Graph Node Functions
+def query_or_respond(state: MessagesState):
+    llm_with_tools = llm.bind_tools([retrieve])
+    response = llm_with_tools.invoke(state["messages"])
+    return {"messages": [response]}
+
+tools = ToolNode([retrieve])
+
+def generate(state: MessagesState):
+    response = llm.invoke(state["messages"])
+    return {"messages": [response]}
+
+# Compile the LangGraph StateGraph
+graph_builder = StateGraph(MessagesState)
+graph_builder.add_node("query_or_respond", query_or_respond)
+graph_builder.add_node("tools", tools)
+graph_builder.add_node("generate", generate)
+graph_builder.set_entry_point("query_or_respond")
+graph_builder.add_conditional_edges(
+    "query_or_respond",
+    tools_condition,
+    {"tools": "tools", END: END}
+)
+graph_builder.add_edge("tools", "generate")
+graph_builder.add_edge("generate", END)
+
+memory = MemorySaver()
+graph = graph_builder.compile(checkpointer=memory)
+
+# ==============================================================================
+# 3. API SERVER (Replaces your if __name__ == "__main__": block)
+# ==============================================================================
+app = FastAPI(
+    title="LangGraph RAG Agent Server",
+    description="An API server for a RAG agent built with LangGraph.",
+)
+
+# Define the input model for the API
+class UserRequest(BaseModel):
+    message: str
+    thread_id: Optional[str] = "default_thread" # Use a default thread_id if none is provided
+
+# Define the API endpoint
+@app.post("/invoke")
+async def invoke_agent(request: UserRequest):
+    # Set up the configuration for memory
+    config = {"configurable": {"thread_id": request.thread_id}}
+    
+    # Define the input for the graph
+    inputs = {"messages": [HumanMessage(content=request.message)]}
+
+    # Invoke the graph to get the final result
+    response = graph.invoke(inputs, config=config)
+    
+    # Return the AI's final message
+    final_message = response["messages"][-1]
+    return {"response": final_message.content}
+
+# This part is for local testing, can be removed if using a production server
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+aiohappyeyeballs==2.6.1
+aiohttp==3.13.0
+aiosignal==1.4.0
+annotated-types==0.7.0
+anyio==4.11.0
+async-timeout==4.0.3
+attrs==25.4.0
+beautifulsoup4==4.14.2
+cachetools==6.2.0
+certifi==2025.10.5
+charset-normalizer==3.4.3
+click==8.1.8
+dataclasses-json==0.6.7
+exceptiongroup==1.3.0
+fastapi==0.119.0
+filetype==1.2.0
+frozenlist==1.8.0
+google-ai-generativelanguage==0.7.0
+google-api-core==2.26.0
+google-auth==2.41.1
+googleapis-common-protos==1.70.0
+grpcio==1.75.1
+grpcio-status==1.75.1
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+httpx-sse==0.4.3
+idna==3.10
+jsonpatch==1.33
+jsonpointer==3.0.0
+langchain==0.3.27
+langchain-community==0.3.31
+langchain-core==0.3.79
+langchain-google-genai==2.1.12
+langchain-text-splitters==0.3.11
+langgraph==0.6.10
+langgraph-checkpoint==2.1.2
+langgraph-prebuilt==0.6.4
+langgraph-sdk==0.2.9
+langsmith==0.4.34
+marshmallow==3.26.1
+multidict==6.7.0
+mypy_extensions==1.1.0
+numpy==2.0.2
+orjson==3.11.3
+ormsgpack==1.11.0
+packaging==25.0
+propcache==0.4.1
+proto-plus==1.26.1
+protobuf==6.32.1
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.12.0
+pydantic-settings==2.11.0
+pydantic_core==2.41.1
+python-dotenv==1.1.1
+PyYAML==6.0.3
+requests==2.32.5
+requests-toolbelt==1.0.0
+rsa==4.9.1
+sniffio==1.3.1
+soupsieve==2.8
+SQLAlchemy==2.0.44
+starlette==0.48.0
+tenacity==9.1.2
+typing-inspect==0.9.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+urllib3==2.5.0
+uvicorn==0.37.0
+xxhash==3.6.0
+yarl==1.22.0
+zstandard==0.25.0