initialize

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:3.9
+
+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", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+from typing import List
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains import (
+    ConversationalRetrievalChain,
+)
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.docstore.document import Document
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from chainlit.types import AskFileResponse
+
+import chainlit as cl
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+system_template = """Use the following pieces of context to answer the users question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+ALWAYS return a "SOURCES" part in your answer.
+The "SOURCES" part should be a reference to the source of the document from which you got your answer.
+
+And if the user greets with greetings like Hi, hello, How are you, etc reply accordingly as well.
+
+Example of your response should be:
+
+The answer is foo
+SOURCES: xyz
+
+
+Begin!
+----------------
+{summaries}"""
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}"),
+]
+prompt = ChatPromptTemplate.from_messages(messages)
+chain_type_kwargs = {"prompt": prompt}
+
+welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
+1. Upload a PDF or text file
+2. Ask a question about the file
+"""
+
+def process_file(file: AskFileResponse):
+    pypdf_loader = PyPDFLoader(file.path)
+    texts = pypdf_loader.load_and_split()
+    texts = [text.page_content for text in texts]
+    return texts
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    # Wait for the user to upload a file
+    while files == None:
+        files = await cl.AskFileMessage(
+            content = welcome_message,
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(
+        content=f"Processing `{file.name}`...", disable_feedback=True
+    )
+    await msg.send()
+
+    # load the file
+    texts = process_file(file)
+
+    # print(texts[0])
+
+    # Create a metadata for each chunk
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+    # Create a Chroma vector store
+    embeddings = OpenAIEmbeddings()
+    docsearch = await cl.make_async(Chroma.from_texts)(
+        texts, embeddings, metadatas=metadatas
+    )
+
+    message_history = ChatMessageHistory()
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+
+    # Create a chain that uses the Chroma vector store
+    chain = ConversationalRetrievalChain.from_llm(
+        ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+
+    # 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", chain)
+
+
+@cl.on_message
+async def main(message):
+    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
+    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)
+            )
+        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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+pypdf==3.8.1
+pinecone-client==2.2.1
+tiktoken==0.3.3
+langchain
+chainlit