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d3176c0 f8c8542 d3176c0 f8c8542 d3176c0 e88b311 d3176c0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import os
from typing import List
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain.chains import (
ConversationalRetrievalChain,
)
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.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 dotenv import load_dotenv
import chainlit as cl
load_dotenv()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
system_template = """Answer the question based only on the following context. If you cannot answer the question with the context, please respond with 'I don't know':
Context:
{context}
Question:
{question}
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
@cl.on_chat_start
async def on_chat_start():
msg = cl.Message(
content=f"Processing Nvidia.pdf..."
)
await msg.send()
# Load PDF directly from local data directory
pypdf_loader = PyPDFLoader("data/nvidia.pdf")
texts = pypdf_loader.load_and_split()
texts = [text.page_content for text in texts]
# Create metadata for each chunk
metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
# Create a FAISS vectorstore
embeddings = OpenAIEmbeddings()
docsearch = await cl.make_async(FAISS.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 FAISS vectorstore
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 Nvidia.pdf 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() |