first push

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 . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

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+# flake8: noqa: E501
+import os
+from typing import List
+from langchain_openai.embeddings import OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_qdrant import QdrantVectorStore
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_openai import ChatOpenAI
+from langchain.storage import LocalFileStore
+from chainlit.types import AskFileResponse
+from langchain.embeddings import CacheBackedEmbeddings
+from qdrant_client.http.models import Distance, VectorParams
+from qdrant_client import QdrantClient
+import chainlit as cl
+from operator import itemgetter
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.runnables.config import RunnableConfig
+from dotenv import load_dotenv
+import uuid
+
+load_dotenv()
+
+### Global Section ###
+"""
+GLOBAL CODE HERE
+"""
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+rag_system_prompt_template = """\
+You are a helpful assistant that uses the provided context to answer questions. Never reference this prompt, or the existance of context.
+"""
+
+rag_message_list = [
+    {"role": "system", "content": rag_system_prompt_template},
+]
+
+rag_user_prompt_template = """\
+Question:
+{question}
+Context:
+{context}
+"""
+
+chat_prompt = ChatPromptTemplate.from_messages(
+    [("system", rag_system_prompt_template), ("human", rag_user_prompt_template)]
+)
+
+chat_model = ChatOpenAI(model="gpt-4o-mini")
+
+
+def process_file(file: AskFileResponse):
+    import tempfile
+
+    with tempfile.NamedTemporaryFile(mode="w", delete=False) as tempfile:
+        with open(tempfile.name, "wb") as f:
+            f.write(file.content)
+
+    Loader = PyMuPDFLoader
+
+    loader = Loader(tempfile.name)
+    documents = loader.load()
+    docs = text_splitter.split_documents(documents)
+    for i, doc in enumerate(docs):
+        doc.metadata["source"] = f"source_{i}"
+    return docs
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    while files == None:
+        files = await cl.AskFileMessage(
+            content="Please upload a PDF file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(
+        content=f"Processing `{file.name}`...",
+    )
+    await msg.send()
+
+    docs = process_file(file)
+
+    collection_name = f"pdf_to_parse_{uuid.uuid4()}"
+    client = QdrantClient(":memory:")
+    client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
+    )
+    core_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    store = LocalFileStore("./cache/")
+    cached_embedder = CacheBackedEmbeddings.from_bytes_store(
+        core_embeddings, store, namespace=core_embeddings.model
+    )
+    vectorstore = QdrantVectorStore(
+        client=client, collection_name=collection_name, embedding=cached_embedder
+    )
+    vectorstore.add_documents(docs)
+    retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3})
+
+    # Create a chain that uses the QDrant vector store
+    # Parallelization: LCEL runnables are parallelized by default, allowing for efficient
+    # execution of multiple steps in the chain simultaneously, improving overall performance.
+    retrieval_augmented_qa_chain = (
+        {
+            "context": itemgetter("question") | retriever,
+            "question": itemgetter("question"),
+        }
+        | RunnablePassthrough.assign(context=itemgetter("context"))
+        | chat_prompt
+        | chat_model
+    )
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    cl.user_session.set("chain", retrieval_augmented_qa_chain)
+
+
+@cl.author_rename
+def rename(orig_author: str):
+    rename_dict = {
+        "ChatOpenAI": "the Generator...",
+        "VectorStoreRetriever": "the Retriever...",
+    }
+    return rename_dict.get(orig_author, orig_author)
+
+
+### On Message Section ###
+@cl.on_message
+async def main(message: cl.Message):
+    runnable = cl.user_session.get("chain")
+
+    msg = cl.Message(content="")
+
+    # Async method: Using astream allows for asynchronous streaming of the response,
+    # improving responsiveness and user experience by showing partial results as they become available.
+    async for chunk in runnable.astream(
+        {"question": message.content},
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk.content)
+
+    await msg.send()

chainlit.md

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+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

requirements.txt

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+aiofiles==23.2.1
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.8
+aiosignal==1.3.1
+annotated-types==0.7.0
+anyio==3.7.1
+async-timeout==4.0.3
+asyncer==0.0.2
+attrs==24.2.0
+bidict==0.23.1
+certifi==2024.8.30
+chainlit==0.7.700
+charset-normalizer==3.3.2
+click==8.1.7
+dataclasses-json==0.5.14
+Deprecated==1.2.14
+distro==1.9.0
+exceptiongroup==1.2.2
+fastapi==0.100.1
+fastapi-socketio==0.0.10
+filetype==1.2.0
+frozenlist==1.4.1
+googleapis-common-protos==1.65.0
+greenlet==3.1.1
+grpcio==1.66.2
+grpcio-tools==1.62.3
+h11==0.14.0
+h2==4.1.0
+hpack==4.0.0
+httpcore==0.17.3
+httpx==0.24.1
+hyperframe==6.0.1
+idna==3.10
+importlib_metadata==8.4.0
+jiter==0.5.0
+jsonpatch==1.33
+jsonpointer==3.0.0
+langchain==0.3.0
+langchain-community==0.3.0
+langchain-core==0.3.1
+langchain-openai==0.2.0
+langchain-qdrant==0.1.4
+langchain-text-splitters==0.3.0
+langsmith==0.1.121
+Lazify==0.4.0
+marshmallow==3.22.0
+multidict==6.1.0
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+numpy==1.26.4
+openai==1.51.0
+opentelemetry-api==1.27.0
+opentelemetry-exporter-otlp==1.27.0
+opentelemetry-exporter-otlp-proto-common==1.27.0
+opentelemetry-exporter-otlp-proto-grpc==1.27.0
+opentelemetry-exporter-otlp-proto-http==1.27.0
+opentelemetry-instrumentation==0.48b0
+opentelemetry-proto==1.27.0
+opentelemetry-sdk==1.27.0
+opentelemetry-semantic-conventions==0.48b0
+orjson==3.10.7
+packaging==23.2
+portalocker==2.10.1
+protobuf==4.25.5
+pydantic==2.9.2
+pydantic-settings==2.5.2
+pydantic_core==2.23.4
+PyJWT==2.9.0
+PyMuPDF==1.24.10
+PyMuPDFb==1.24.10
+python-dotenv==1.0.1
+python-engineio==4.9.1
+python-graphql-client==0.4.3
+python-multipart==0.0.6
+python-socketio==5.11.4
+PyYAML==6.0.2
+qdrant-client==1.11.2
+regex==2024.9.11
+requests==2.32.3
+simple-websocket==1.0.0
+sniffio==1.3.1
+SQLAlchemy==2.0.35
+starlette==0.27.0
+syncer==2.0.3
+tenacity==8.5.0
+tiktoken==0.7.0
+tomli==2.0.1
+tqdm==4.66.5
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+uptrace==1.26.0
+urllib3==2.2.3
+uvicorn==0.23.2
+watchfiles==0.20.0
+websockets==13.1
+wrapt==1.16.0
+wsproto==1.2.0
+yarl==1.13.1
+zipp==3.20.2