Add application file

Dockerfile

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+
+# Get a distribution that has uv already installed
+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+# Add user - this is the user that will run the app
+# If you do not set user, the app will run as root (undesirable)
+RUN useradd -m -u 1000 user
+USER user
+
+# Set the home directory and path
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH        
+
+ENV UVICORN_WS_PROTOCOL=websockets
+
+
+# Set the working directory
+WORKDIR $HOME/app
+
+# Copy the app to the container
+COPY --chown=user . $HOME/app
+
+# Install the dependencies
+# RUN uv sync --frozen
+RUN uv sync
+
+# Expose the port
+EXPOSE 7860
+
+# Run the app
+CMD ["uv", "run", "chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+from dotenv import load_dotenv
+from chainlit.types import AskFileResponse
+import chainlit as cl
+
+import pandas as pd
+from langchain_community.vectorstores import FAISS
+from langchain_core.documents import Document
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_community.document_loaders import BSHTMLLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_cohere import CohereRerank
+from langchain.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain.chat_models import init_chat_model
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from langgraph.graph import START, StateGraph, END
+from typing_extensions import List, TypedDict
+from langchain_core.documents import Document
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode
+
+
+#Load API Keys
+load_dotenv()
+
+#Load downloaded html pages of the book Genesis in Bible
+path = "data/"
+loader = DirectoryLoader(path, glob="*.html")
+docs = loader.load()
+
+#Building RAG Graph with LangGraph
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 750,
+    chunk_overlap = 100
+)
+
+split_documents = text_splitter.split_documents(docs)
+len(split_documents)
+#fine tuned embedding model
+huggingface_embeddings = HuggingFaceEmbeddings(model_name="kcheng0816/finetuned_arctic_genesis")
+
+client = QdrantClient(":memory:")
+client.create_collection(
+    collection_name="genesis_bible",
+    vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
+)
+
+vector_store = QdrantVectorStore(
+    client=client,
+    collection_name="genesis_bible",
+    embedding=huggingface_embeddings,
+)
+
+_ = vector_store.add_documents(documents=split_documents)
+#Retrieve
+retriever = vector_store.as_retriever(search_kwargs={"k": 5})
+
+
+def retrieve_adjusted(state):
+  compressor = CohereRerank(model="rerank-v3.5")
+  compression_retriever = ContextualCompressionRetriever(
+    base_compressor=compressor, base_retriever=retriever, search_kwargs={"k": 5}
+  )
+  retrieved_docs = compression_retriever.invoke(state["question"])
+  return {"context" : retrieved_docs}
+
+
+RAG_PROMPT = """\
+You are a helpful assistant who answers questions based on provided context. You must only use the provided context, and cannot use your own knowledge.
+
+### Question
+{question}
+
+### Context
+{context}
+"""
+rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+
+
+#llm 
+rate_limiter = InMemoryRateLimiter(
+    requests_per_second=1,  # <-- make a request once every 1 seconds!!
+    check_every_n_seconds=0.1,  # Wake up every 100 ms to check whether allowed to make a request,
+    max_bucket_size=10,  # Controls the maximum burst size.
+)
+llm = init_chat_model("gpt-4o-mini", rate_limiter=rate_limiter)
+
+
+def generate(state):
+  docs_content = "\n\n".join(doc.page_content for doc in state["context"])
+  messages = rag_prompt.format_messages(question=state["question"], context=docs_content)
+  response = llm.invoke(messages)
+  return {"response" : response.content}
+
+
+
+
+class State(TypedDict):
+  question: str
+  context: List[Document]
+  response: str
+
+graph_builder = StateGraph(State).add_sequence([retrieve_adjusted, generate])
+graph_builder.add_edge(START, "retrieve_adjusted")
+graph = graph_builder.compile()
+
+
+
+
+@tool
+def ai_rag_tool(question: str) -> str:
+    """Useful for when you need to answer questions about Bible """
+    response = graph.invoke({"question": question})
+    return {
+        "message": [HumanMessage(content=response["response"])],
+        "context": response["context"]
+    }
+
+tool_belt = [
+    ai_rag_tool
+]
+
+
+llm = init_chat_model("gpt-4o", temperature=0, rate_limiter=rate_limiter)
+llm_with_tools = llm.bind_tools(tool_belt)
+
+
+from langgraph.graph import END
+from langchain_core.messages import AnyMessage
+from langgraph.graph.message import add_messages
+from typing import TypedDict, Annotated
+from langchain_core.documents import Document
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+    context:List[Document]
+
+
+
+def call_mode(state):
+    messages = state["messages"]
+    response = llm_with_tools.invoke(messages)
+    return {
+        "messages": [response],
+        "context": state.get("context",[])
+    }
+
+tool_node = ToolNode(tool_belt)
+
+def should_continue(state):
+    last_message = state["messages"][-1]
+
+    if last_message.tool_calls:
+        return "action"
+    
+    return END
+
+
+#
+uncompiled_graph = StateGraph(AgentState)
+
+uncompiled_graph.add_node("agent", call_mode)
+uncompiled_graph.add_node("action", tool_node)
+
+uncompiled_graph.set_entry_point("agent")
+
+uncompiled_graph.add_conditional_edges(
+    "agent",
+    should_continue
+)
+
+uncompiled_graph.add_edge("action", "agent")
+
+# Compile and display the graph for a visual overview
+compiled_graph = uncompiled_graph.compile()
+
+
+
+
+
+@cl.on_chat_start
+async def on_chat_start():
+   cl.user_session.set("graph", compiled_graph)
+
+
+
+@cl.on_message
+async def handle(message: cl.Message):
+    graph = cl.user_session.get("graph")
+    state = {"messages": [HumanMessage(content=message.content)]}
+    response = await graph.ainvoke(state)
+    await cl.Message(content=response["messages"][-1].content).send()

pyproject.toml

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+[project]
+name = "biblestudy-agentic-rag"
+version = "0.1.0"
+description = "AIE5 midterm"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "jupyter>=1.1.1",
+    "langchain>=0.3.15",
+    "langchain-community>=0.3.15",
+    "langchain-openai>=0.3.2",
+    "langgraph>=0.2.67",
+    "chainlit>=2.0.4",
+    "pandas>=2.2.3",
+    "langchain-qdrant>=0.2.0",
+    "langchain-cohere>=0.4.2",
+    "unstructured>=0.14.8",
+    "langchain-huggingface>=0.1.2",
+    "websockets>=15.0",
+]