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Dockerfile

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+FROM python:3.9
+
+# Create a user with non-root privileges
+RUN useradd -m -u 1000 user
+USER user
+
+# Set environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set working directory
+WORKDIR $HOME/app
+
+# Copy application files and requirements
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt $HOME/app/requirements.txt
+
+# Install dependencies
+RUN pip install -r requirements.txt
+
+# Define the default command to run the app
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

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+import os
+from typing import List
+
+from langchain_openai import OpenAIEmbeddings
+from langchain_chroma import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_groq import ChatGroq
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.prompts import PromptTemplate
+from langchain import hub
+
+import chainlit as cl
+from io import BytesIO
+
+##################################### Load the embeddings and model #####################################
+
+groq_api_key = os.getenv("GROQ_API_KEY")
+embeddings_api_key = os.getenv('GOOGLE_API_KEY')
+
+embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+llm = ChatGroq(model="mixtral-8x7b-32768", temperature=0)
+
+
+
+
+##################################### on_chat_start event handler #######################################
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload a text file to begin",
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=300
+        ).send()
+
+    file = files[0]
+    msg = cl.Message(content=f"Processing `{file.name}` ...")
+    await msg.send()
+
+    ##################################### Load the text from the file ####################################
+
+    pdf_loader = PyPDFLoader(file.path).load()
+
+    ##################################### Split the text into chunks #####################################
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    chunks = text_splitter.split_documents(pdf_loader)
+
+    ##################################### Chroma DB setup ################################################
+
+    docsearch = await cl.make_async(Chroma.from_documents)(
+        chunks, embedding_model
+    )
+
+    message_history = ChatMessageHistory()
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True
+    )
+
+    ##################################### Chain setup ###################################################
+
+    # Define your custom prompt template
+    custom_prompt_template = """
+    Based on the provided context please answer . if you don't know the answer. just say i don't know.
+    {context}
+
+    Question: {question}
+    """
+    custom_prompt = PromptTemplate(
+    template=custom_prompt_template,
+    input_variables=["context", "question"],)
+
+    chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+        combine_docs_chain_kwargs={"prompt": custom_prompt}
+        
+    )
+
+    msg.content = f"Processing `{file.name}` ... Done!✅ You can ask questions now!"
+    await msg.update()
+
+    cl.user_session.set("chain", chain)
+
+
+##################################### On message event handler ###########################################
+
+@cl.on_message
+async def main(message: cl.Message):
+    chain = cl.user_session.get("chain")
+    cb = cl.AsyncLangchainCallbackHandler()
+
+    res = await chain.acall(message.content, callbacks=[cb])
+    answer = res['answer']
+
+    source_documents = res["source_documents"]  # type: List[Document]
+
+    text_elements = []  # type: List[cl.Text]
+
+    if source_documents:
+        for source_idx, source_doc in enumerate(source_documents):
+            source_name = f"source_{source_idx}"
+            # Create the text element referenced in the message
+            text_elements.append(
+                cl.Text(content=source_doc.page_content, name=source_name, display="side")
+            )
+        source_names = [text_el.name for text_el in text_elements]
+
+        if source_names:
+            answer += f"\nSources: {', '.join(source_names)}"
+        else:
+            answer += "\nNo sources found"
+
+    await cl.Message(content=answer, elements=text_elements).send()

requirements.txt

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+langchain
+openai
+chromadb
+langchain-openai
+langchain-chroma
+langchain-google-genai
+langchain-groq
+chainlit
+PyPDF2