init

Dockerfile

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

README.md

@@ -1,10 +0,0 @@
----
-title: Midterm Project
-emoji: 🏢
-colorFrom: pink
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import chainlit as cl
+from chainlit.playground.providers import ChatOpenAI
+from dotenv import load_dotenv
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import tiktoken
+from langchain.prompts import ChatPromptTemplate
+from operator import  itemgetter
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
+from langchain.retrievers import MultiQueryRetriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import Runnable
+from langchain.schema.runnable.config import RunnableConfig
+from langchain.retrievers import MultiQueryRetriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain import hub
+
+template = """
+you can only answer questions related to what's in the context. If it's not in the context, then you would reply with 
+'Sorry I have no answer to your particular question. I can only answer things regarding: {context}'
+
+Context:
+{context}
+
+Question:
+{question}
+"""
+
+init_settings = {
+    "model": "gpt-3.5-turbo",
+    "temperature": 0,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+}
+
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+
+load_dotenv()
+
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-3.5-turbo").encode(
+        text,
+    )
+    return len(tokens)
+
+
+@cl.on_chat_start
+async def main():
+    model = ChatOpenAI(streaming=True)
+    
+    prompt = ChatPromptTemplate.from_template(template)
+
+    nvida_doc = PyMuPDFLoader('../docs/nvidia-document.pdf')
+    data = nvida_doc.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 1700,
+    chunk_overlap = 0,
+    length_function = tiktoken_len)
+    
+    nvidia_doc_chunks = text_splitter.split_documents(data)
+
+    vector_store = FAISS.from_documents(nvidia_doc_chunks, embedding=embeddings)
+    
+    retriever = vector_store.as_retriever()
+    advanced_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model)
+
+    runnable = (
+    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    | {"response": prompt | model, "context": itemgetter("context")})
+
+    # retrieval_qa_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
+    # document_chain = create_stuff_documents_chain(model, retrieval_qa_prompt)
+    # runnable = create_retrieval_chain(advanced_retriever, document_chain)
+
+    # cl.user_session.set("settings", init_settings)
+    # cl.user_session.set("nvidia_doc", data)
+
+    cl.user_session.set("runnable", runnable)
+
+
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    # settings = cl.user_session.get("settings")
+    # nvida_doc = cl.user_session.get("nvidia_doc")
+    runnable = cl.user_session.get("runnable")
+
+    msg = cl.Message(content="")
+
+    
+    # async for chunk in runnable.astream(
+    #     {"question": message.content},
+    #     config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    # ):
+    #     await msg.stream_token(chunk, True)
+
+    # await msg.send()
+
+    inputs = {"question": message.content}
+    result = await runnable.ainvoke(inputs)
+    msg = cl.Message(content=result["response"].content)
+    await msg.send()
+
+
+ 
+
+
+
+

requirements.txt

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+chainlit==0.7.700
+cohere==4.37
+openai==1.3.5
+tiktoken==0.5.1
+python-dotenv==1.0.0
+langchain-community=0.1.0