add app files

.gitignore

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+.venv/
+.env
+__pycache__

Dockerfile

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+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.11
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["chainlit", "run", "app.py", "--address", "0.0.0.0", "--port", "7860", "--allow-websocket-origin", "souravmighty-groq_doc.hf.space"]

app.py

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+import PyPDF2
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_groq import ChatGroq
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+import chainlit as cl
+from chainlit.input_widget import Select
+import os
+
+
+@cl.cache
+def get_memory():
+    # Initialize message history for conversation
+    message_history = ChatMessageHistory()
+    
+    # Memory for conversational context
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+    return memory
+
+@cl.on_chat_start
+async def on_chat_start():
+
+    user_env = cl.user_session.get("env")
+    os.environ["GROQ_API_KEY"] = user_env.get("GROQ_API_KEY")
+
+    settings = await cl.ChatSettings(
+        [
+            Select(
+                id="Model",
+                label="Open Source Model",
+                values=["llama3-8b-8192", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma-7b-it"],
+                initial_index=0,
+            )
+        ]
+    ).send()
+
+    files = None #Initialize variable to store uploaded files
+
+    # Wait for the user to upload a file
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload a pdf file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=100,
+            timeout=180, 
+        ).send()
+
+    file = files[0] # Get the first uploaded file
+    
+    # Inform the user that processing has started
+    msg = cl.Message(content=f"Processing `{file.name}`...")
+    await msg.send()
+
+    # Read the PDF file
+    pdf = PyPDF2.PdfReader(file.path)
+    pdf_text = ""
+    for page in pdf.pages:
+        pdf_text += page.extract_text()
+        
+
+    # Split the text into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_text(pdf_text)
+
+    # Create a metadata for each chunk
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+    # Create a Chroma vector store
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+    #embeddings = OllamaEmbeddings(model="llama2:7b")
+    docsearch = await cl.make_async(Chroma.from_texts)(
+        texts, embeddings, metadatas=metadatas, persist_directory='./chroma_db'
+    )
+    docsearch.persist()
+    
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    await setup_agent(settings)
+    
+
+@cl.on_settings_update
+async def setup_agent(settings):
+    print("Setup agent with settings:", settings)
+
+    user_env = cl.user_session.get("env")
+    os.environ["GROQ_API_KEY"] = user_env.get("GROQ_API_KEY")
+
+    embeddings = OllamaEmbeddings(model="nomic-embed-text")
+    memory=get_memory()
+    
+    docsearch = await cl.make_async(Chroma)(
+        persist_directory="./chroma_db",
+        embedding_function=embeddings
+    )
+
+    # Create a chain that uses the Chroma vector store
+    chain = ConversationalRetrievalChain.from_llm(
+        llm = ChatGroq(model=settings["Model"]),
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+
+    #store the chain in user session
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+        
+     # Retrieve the chain from user session
+    chain = cl.user_session.get("chain") 
+    #call backs happens asynchronously/parallel 
+    cb = cl.AsyncLangchainCallbackHandler()
+
+    user_env = cl.user_session.get("env")
+    os.environ["GROQ_API_KEY"] = user_env.get("GROQ_API_KEY")
+
+
+    print(chain)
+    
+    # call the chain with user's message content
+    res = await chain.ainvoke(message.content, callbacks=[cb])
+    answer = res["answer"]
+    source_documents = res["source_documents"] 
+
+    text_elements = [] # Initialize list to store text elements
+    
+    # Process source documents if available
+    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)
+            )
+        source_names = [text_el.name for text_el in text_elements]
+        
+         # Add source references to the answer
+        if source_names:
+            answer += f"\nSources: {', '.join(source_names)}"
+        else:
+            answer += "\nNo sources found"
+    #return results
+    await cl.Message(content=answer, elements=text_elements).send()

requirements.txt

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+chainlit
+langchain
+langchain-community
+PyPDF2
+chromadb
+groq
+langchain-groq
+ollama