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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.

dockerfile.txt

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY ./* /code/
+
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

pdf_qa.py

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+# Import necessary modules and define env variables
+
+from langchain.chains import RetrievalQA
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import FAISS
+from langchain.prompts.chat import (
+    ChatPromptTemplate
+)
+from langchain_community.llms import HuggingFaceHub
+import tempfile
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import os
+import io
+import chainlit as cl
+import PyPDF2
+
+#os.environ["HUGGINGFACEHUB_API_TOKEN"] = ""
+template = """Answer the question based only on the following context from the book 48 Laws of Power:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+chain_type_kwargs = {"prompt": prompt}
+
+from langchain.text_splitter import SpacyTextSplitter
+
+text_splitter = SpacyTextSplitter(chunk_size=1000)
+
+@cl.on_chat_start
+async def on_chat_start():
+    await cl.Message(content="Hello there, Welcome to Laws of Power chat app!").send()
+    msg = cl.Message(content=f"Processing Laws of Power...")
+    await msg.send()
+    loader = PyPDFLoader('./48lawsofpower.pdf')
+    pages = loader.load_and_split()
+
+# Create a Chroma vector store
+    embeddings = HuggingFaceEmbeddings()
+    faiss_index = FAISS.from_documents(pages, embeddings)
+
+# Clean up the temporary file   
+    pdf = PyPDF2.PdfReader('./48lawsofpower.pdf')
+    pdf_text = ""
+    for page in pdf.pages:
+        pdf_text += page.extract_text()
+
+    # Split the text into chunks
+    texts = text_splitter.split_text(pdf_text)
+
+    # Create metadata for each chunk
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+    repo_id = "HuggingFaceH4/zephyr-7b-beta"
+
+    chain_type_kwargs = {"prompt": prompt}
+
+    llm = HuggingFaceHub(
+        repo_id=repo_id, model_kwargs={"temperature": 0.1, "max_new_tokens":1024, "max_length": 728}
+    )
+
+    # Create a chain that uses the Chroma vector store
+    chain = RetrievalQA.from_chain_type(
+        llm,
+        chain_type="stuff",
+        retriever=faiss_index.as_retriever(),
+    )
+    
+
+    # Save the metadata and texts in the user session
+    cl.user_session.set("metadatas", metadatas)
+    cl.user_session.set("texts", texts)
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing Laws of Power done. You can now ask questions!"
+    await msg.update()
+
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message:str):
+    message = message.content
+    print("This" , message)
+    chain = cl.user_session.get("chain") 
+    cb = cl.AsyncLangchainCallbackHandler(
+        stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
+    )
+    cb.answer_reached = True
+    res = await chain.acall(message, callbacks=[cb])
+
+    answer = res['result']
+    source_elements = []
+    if cb.has_streamed_final_answer:
+        cb.final_stream.elements = source_elements
+        await cb.final_stream.update()
+    else:
+        await cl.Message(content=answer, elements=source_elements).send()

requirements.txt

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+langchain
+chainlit 
+transformers
+huggingface_hub
+faiss_cpu
+tiktoken
+spacy
+PyPDF2