Adding application file, chainlit.md, Dockerfile, README, requirements.txt, and data

.gitignore

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+.env
+__pycache__/
+.chainlit
+*.faiss
+*.pkl
+.files

Dockerfile

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+FROM python:3.12    
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH 
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+RUN pip list
+
+# Create the directory and set permissions
+USER root
+RUN mkdir -p /home/user/app/data/vectorstore && \
+    chown -R user:user /home/user/app/data && \
+    chmod -R 777 /home/user/app/data
+USER user
+
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

README.md

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 ---
-title: Airbnb 10k
-emoji: 😻
-colorFrom: red
-colorTo: red
+title: Airbnb 10k 2024
+emoji: 🏆
+colorFrom: purple
+colorTo: indigo
 sdk: docker
 pinned: false
+license: openrail
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import chainlit as cl
+import openai
+import tiktoken
+from dotenv import load_dotenv
+from operator import itemgetter
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnableConfig, RunnablePassthrough
+from langchain_openai import ChatOpenAI
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Environment variables
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+if not openai.api_key:
+    raise ValueError("OPENAI_API_KEY environment variable not set")
+
+# Set vector store path
+VECTOR_STORE_PATH = "./data/vectorstore"
+
+# Document loader
+document_loader = PyMuPDFLoader("./data/Airbnb-10k.pdf")
+documents = document_loader.load()
+
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-4o").encode(text)
+    return len(tokens)
+
+# Load embeddings
+openai_embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+# Text splitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=100)
+split_documents = text_splitter.split_documents(documents)
+
+# Create or load vector store
+if os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
+    print("Loading existing vectorstore from disk.")
+    vectorstore = FAISS.load_local(
+        VECTOR_STORE_PATH,
+        openai_embeddings,
+        allow_dangerous_deserialization=True
+    )
+    retriever = vectorstore.as_retriever()
+    print("Loaded Vectorstore")
+else:
+    print("Indexing Files")
+    os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
+    vectorstore = FAISS.from_documents(split_documents[:32], openai_embeddings)
+    for i in range(32, len(split_documents), 32):
+        vectorstore.add_documents(split_documents[i:i+32])
+    vectorstore.save_local(VECTOR_STORE_PATH)
+    print("Vectorstore created and documents indexed.")
+
+# Create retriever
+retriever = vectorstore.as_retriever()
+
+# Define the prompt template
+RAG_PROMPT_TEMPLATE = """\
+system
+You are a helpful assistant. You answer user questions based on provided context. If you can't answer the question with the provided context, say you don't know.
+
+user
+User Query:
+{query}
+
+Context:
+{context}
+
+assistant
+"""
+
+rag_prompt = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
+
+# Create ChatOpenAI instance
+llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
+
+retrieval_augmented_qa_chain = (
+    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    | {"response": rag_prompt | llm, "context": itemgetter("context")}
+)
+
+# Chainlit
+@cl.on_chat_start
+async def start_chat():
+    """
+    This function will be called at the start of every user session. 
+    We will build our LCEL RAG chain here and store it in the user session. 
+    The user session is a dictionary that is unique to each user session and is stored in the memory of the server.
+    """
+    settings = {
+        "model": "gpt-4o",
+        "temperature": 0,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
+    try:
+        lcel_rag_chain = ({"context": itemgetter("query") | retriever, "query": itemgetter("query")}
+                          | rag_prompt | llm)
+
+        cl.user_session.set("lcel_rag_chain", lcel_rag_chain)
+        print("Chat session started and LCEL RAG chain set.")
+    except Exception as e:
+        print(f"Error in start_chat: {e}")
+
+@cl.on_message
+async def main(message: cl.Message):
+    """
+    This function will be called every time a message is received from a session.
+    We will use the LCEL RAG chain to generate a response to the user query.
+    The LCEL RAG chain is stored in the user session and is unique to each user session - this is why we can access it here.
+    """
+    try:
+        lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
+        print(f"Received message: {message.content}")
+        print("Using LCEL RAG chain to generate response...")
+
+        msg = cl.Message(content="")
+
+        async for chunk in lcel_rag_chain.astream(
+            {"query": message.content},
+            config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+        ):
+            chunk_text = chunk.content if hasattr(chunk, 'content') else str(chunk)
+            print(f"Streaming chunk: {chunk_text}")
+            await msg.stream_token(chunk_text)
+
+        print("Sending final message...")
+        await msg.send()
+        print("Message sent.")
+    except KeyError as e:
+        print(f"Session error: {e}")
+        await message.send("Session error occurred. Please try again.")
+    except Exception as e:
+        print(f"Error: {e}")
+        await message.send("An error occurred. Please try again.")

chainlit.md

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+# Airbnb 10k 2024 RAG Application
+
+Welcome to the Airbnb 10k 2024 RAG application! 
+
+This RAG (retrieval augmentation generation) application allows you to query the Airbnb 10k 2024 filing dataset. It utilizes a generalized LLM and uses RAG techniques to retrieve and respond to user queries specific to knowledge of the Airbnb 10k 2024 filing dataset.
+
+Build 🏗️
+
+Data: Airbnb 10-k Filings from Q1, 2024
+LLM: OpenAI
+Embedding Model: OpenAI Embeddings (model="text-embedding-3-small")
+Infrastructure: LangChain
+Vector Store: QDrant
+Deployment: Chainlit, Hugging Face
+
+Ship 🚢
+
+Evaluate your answers to the following questions
+Q1 "What is Airbnb's 'Description of Business'?"
+Q2 "What was the total value of 'Cash and cash equivalents' as of December 31, 2023?"
+Q3 "What is the 'maximum number of shares to be sold under the 10b5-1 Trading plan' by Brian Chesky?"

requirements.txt

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+chainlit>=0.7.700
+langchain==0.2.5
+langchain_community==0.2.5
+langchain_core==0.2.9
+langchain_huggingface==0.0.3
+langchain_text_splitters==0.2.1
+langchain_openai==0.1.9
+python-dotenv==1.0.0
+pymupdf==1.24.5
+faiss-cpu==1.8.0.post1
+openai==1.35.3