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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", "rag_engine.py", "--port", "7860"]

rag_engine.py

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+import os
+from typing import List
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores.pinecone import Pinecone
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from langchain.docstore.document import Document
+import pinecone
+import chainlit as cl
+from chainlit.types import AskFileResponse
+
+pinecone.init(
+    api_key="2b6aa6bf-2e20-4445-a560-f7dd4952e59e",
+    environment="gcp-starter",
+)
+
+index_name = "skandhaar"
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+embeddings = OpenAIEmbeddings()
+
+namespaces = set()
+
+welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
+1. Upload a PDF or text file
+"""
+
+
+def process_file(file: AskFileResponse):
+    import tempfile
+
+    if file.type == "text/plain":
+        Loader = TextLoader
+    elif file.type == "application/pdf":
+        Loader = PyPDFLoader
+
+    with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tempfile:
+        if file.type == "text/plain":
+            tempfile.write(file.content)
+        elif file.type == "application/pdf":
+            with open(tempfile.name, "wb") as f:
+                f.write(file.content)
+
+        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
+
+
+def get_docsearch(file: AskFileResponse):
+    docs = process_file(file)
+
+    # Save data in the user session
+    cl.user_session.set("docs", docs)
+
+    # Create a unique namespace for the file
+    namespace = str(hash(file.content))
+
+    if namespace in namespaces:
+        docsearch = Pinecone.from_existing_index(
+            index_name=index_name, embedding=embeddings
+        )
+    else:
+        docsearch = Pinecone.from_documents(
+            docs, embeddings, index_name=index_name
+        )
+        namespaces.add(namespace)
+
+    return docsearch
+
+
+@cl.on_chat_start
+async def start():
+    await cl.Avatar(
+        name="Chatbot",
+        url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
+    ).send()
+    
+    files = None
+    while files is None:
+        files = await cl.AskFileMessage(
+            content=welcome_message,
+            accept=["text/plain", "application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+            disable_human_feedback=True,
+        ).send()
+
+    for file in files:
+        msg = cl.Message(
+            content=f"Processing `{file.name}`...", disable_human_feedback=True
+        )
+        await msg.send()
+
+        # No async implementation in the Pinecone client, fallback to sync
+        docsearch = await cl.make_async(get_docsearch)(file)
+
+        message_history = ChatMessageHistory()
+
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key="result",
+            chat_memory=message_history,
+            return_messages=True,
+        )
+
+        chain = RetrievalQA.from_chain_type(
+            ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True, openai_api_key="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"),
+            chain_type="stuff",
+            retriever=docsearch.as_retriever(),
+            return_source_documents=True,
+        )
+
+        # Let the user know that the system is ready
+        msg.content = f"`{file.name}` processed. You can now ask questions!"
+        await msg.update()
+
+        cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
+    cb = cl.AsyncLangchainCallbackHandler()
+    res = await chain.acall(message.content, callbacks=[cb])
+    answer = res["result"]
+    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
+protobuf==3.19.3