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Upload 4 files
Browse files- Dockerfile +11 -0
- rag_engine.py +149 -0
- requirements.txt +6 -0
Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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COPY --chown=user . $HOME/app
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COPY ./requirements.txt ~/app/requirements.txt
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RUN pip install -r requirements.txt
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COPY . .
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CMD ["chainlit", "run", "rag_engine.py", "--port", "7860"]
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rag_engine.py
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import os
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from typing import List
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from langchain.document_loaders import PyPDFLoader, TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores.pinecone import Pinecone
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from langchain.chains import RetrievalQA
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ChatMessageHistory, ConversationBufferMemory
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from langchain.docstore.document import Document
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import pinecone
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import chainlit as cl
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from chainlit.types import AskFileResponse
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pinecone.init(
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api_key="2b6aa6bf-2e20-4445-a560-f7dd4952e59e",
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environment="gcp-starter",
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)
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index_name = "skandhaar"
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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embeddings = OpenAIEmbeddings()
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namespaces = set()
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welcome_message = """Welcome to the Chainlit PDF QA demo! To get started:
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1. Upload a PDF or text file
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"""
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def process_file(file: AskFileResponse):
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import tempfile
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if file.type == "text/plain":
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Loader = TextLoader
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elif file.type == "application/pdf":
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Loader = PyPDFLoader
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with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tempfile:
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if file.type == "text/plain":
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tempfile.write(file.content)
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elif file.type == "application/pdf":
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with open(tempfile.name, "wb") as f:
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f.write(file.content)
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loader = Loader(tempfile.name)
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documents = loader.load()
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docs = text_splitter.split_documents(documents)
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for i, doc in enumerate(docs):
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doc.metadata["source"] = f"source_{i}"
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return docs
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def get_docsearch(file: AskFileResponse):
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docs = process_file(file)
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# Save data in the user session
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cl.user_session.set("docs", docs)
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# Create a unique namespace for the file
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namespace = str(hash(file.content))
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if namespace in namespaces:
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docsearch = Pinecone.from_existing_index(
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index_name=index_name, embedding=embeddings
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)
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else:
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docsearch = Pinecone.from_documents(
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docs, embeddings, index_name=index_name
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)
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namespaces.add(namespace)
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return docsearch
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@cl.on_chat_start
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async def start():
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await cl.Avatar(
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name="Chatbot",
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url="https://avatars.githubusercontent.com/u/128686189?s=400&u=a1d1553023f8ea0921fba0debbe92a8c5f840dd9&v=4",
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).send()
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files = None
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while files is None:
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files = await cl.AskFileMessage(
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content=welcome_message,
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accept=["text/plain", "application/pdf"],
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max_size_mb=20,
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timeout=180,
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disable_human_feedback=True,
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).send()
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for file in files:
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msg = cl.Message(
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content=f"Processing `{file.name}`...", disable_human_feedback=True
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)
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await msg.send()
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# No async implementation in the Pinecone client, fallback to sync
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docsearch = await cl.make_async(get_docsearch)(file)
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message_history = ChatMessageHistory()
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memory = ConversationBufferMemory(
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memory_key="chat_history",
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output_key="result",
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chat_memory=message_history,
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return_messages=True,
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)
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chain = RetrievalQA.from_chain_type(
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ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True, openai_api_key="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"),
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chain_type="stuff",
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retriever=docsearch.as_retriever(),
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return_source_documents=True,
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)
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# Let the user know that the system is ready
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msg.content = f"`{file.name}` processed. You can now ask questions!"
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await msg.update()
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cl.user_session.set("chain", chain)
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@cl.on_message
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async def main(message: cl.Message):
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chain = cl.user_session.get("chain") # type: ConversationalRetrievalChain
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cb = cl.AsyncLangchainCallbackHandler()
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res = await chain.acall(message.content, callbacks=[cb])
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answer = res["result"]
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source_documents = res["source_documents"] # type: List[Document]
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text_elements = [] # type: List[cl.Text]
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if source_documents:
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for source_idx, source_doc in enumerate(source_documents):
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source_name = f"source_{source_idx}"
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# Create the text element referenced in the message
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text_elements.append(
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cl.Text(content=source_doc.page_content, name=source_name)
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)
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source_names = [text_el.name for text_el in text_elements]
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if source_names:
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answer += f"\nSources: {', '.join(source_names)}"
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else:
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answer += "\nNo sources found"
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await cl.Message(content=answer, elements=text_elements).send()
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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pypdf==3.8.1
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pinecone-client==2.2.1
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tiktoken==0.3.3
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langchain
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chainlit
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protobuf==3.19.3
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