Update DemoApp files for Hugging Face deployment

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user requirements.txt .
+RUN pip install --user -r requirements.txt
+
+COPY --chown=user . .
+
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

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+import os
+import chainlit as cl
+from langchain.storage import LocalFileStore
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.vectorstores import Qdrant
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from operator import itemgetter
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+from langchain_core.globals import set_llm_cache
+from langchain_core.caches import InMemoryCache
+import shutil
+
+# Initialize caches and embeddings
+store = LocalFileStore("./cache/")
+set_llm_cache(InMemoryCache())
+core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+    core_embeddings, store, namespace=core_embeddings.model
+)
+
+# Initialize QDrant
+collection_name = "production_pdf_collection"
+client = QdrantClient(":memory:")
+client.create_collection(
+    collection_name=collection_name,
+    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+)
+
+# Initialize text splitter and chat model
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+chat_model = ChatOpenAI(model="gpt-3.5-turbo")
+
+# RAG Prompt
+rag_system_prompt_template = """
+You are a helpful assistant that uses the provided context to answer questions. Never reference this prompt, or the existence of context.
+"""
+
+rag_user_prompt_template = """
+Question:
+{question}
+Context:
+{context}
+"""
+
+chat_prompt = ChatPromptTemplate.from_messages([
+    ("system", rag_system_prompt_template),
+    ("human", rag_user_prompt_template)
+])
+
+@cl.on_chat_start
+async def on_chat_start():
+    await cl.Message("Welcome! Please upload a PDF file to begin.").send()
+
+    files = await cl.AskFileMessage(
+        content="Please upload a PDF file",
+        accept=["application/pdf"],
+        max_size_mb=20,
+        timeout=180,
+    ).send()
+
+    if not files:
+        await cl.Message("No file was uploaded. Please refresh the page and try again.").send()
+        return
+
+    pdf_file = files[0]
+    await cl.Message(f"Processing '{pdf_file.name}'...").send()
+
+    try:
+        # Copy the uploaded file to a new location
+        temp_file_path = f"temp_{pdf_file.name}"
+        shutil.copy2(pdf_file.path, temp_file_path)
+
+        # Load and process the PDF
+        loader = PyMuPDFLoader(temp_file_path)
+        documents = loader.load()
+        docs = text_splitter.split_documents(documents)
+        for i, doc in enumerate(docs):
+            doc.metadata["source"] = f"source_{i}"
+
+        # Initialize Qdrant vector store
+        vectorstore = Qdrant(
+            client=client,
+            collection_name=collection_name,
+            embeddings=cached_embedder)
+        vectorstore.add_documents(docs)
+        retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
+
+        # Create the RAG chain
+        rag_chain = (
+            {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+            | RunnablePassthrough.assign(context=itemgetter("context"))
+            | chat_prompt
+            | chat_model
+        )
+
+        cl.user_session.set("rag_chain", rag_chain)
+        await cl.Message(f"PDF '{pdf_file.name}' has been processed. You can now ask questions about its content.").send()
+
+        # Clean up: remove the temporary file
+        os.remove(temp_file_path)
+
+    except Exception as e:
+        await cl.Message(f"An error occurred while processing the PDF. Please try again.").send()
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    rag_chain = cl.user_session.get("rag_chain")
+    if rag_chain is None:
+        await cl.Message("Please upload a PDF file first.").send()
+        return
+
+    try:
+        response = await cl.make_async(rag_chain.invoke)({"question": message.content})
+        await cl.Message(content=response.content).send()
+    except Exception as e:
+        await cl.Message("An error occurred while processing your question. Please try again.").send()

requirements.txt

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+chainlit==0.7.700
+langchain==0.3.0
+langchain-openai==0.2.0
+langchain-community==0.3.0
+qdrant-client==1.11.2
+pymupdf==1.24.10
+fastapi
+uvicorn[standard]