Spaces:
Runtime error
Runtime error
File size: 3,232 Bytes
073e458 2294434 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 abaa054 073e458 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import chainlit as cl
from langchain_community.document_loaders import PyMuPDFLoader
from chainlit.playground.providers import ChatOpenAI
from dotenv import load_dotenv
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 import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.retrievers import MultiQueryRetriever
from langchain.prompts import ChatPromptTemplate
from langchain.retrievers import MultiQueryRetriever
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()
|