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897405a 88fea3c 897405a 3493a52 88fea3c 3493a52 897405a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import os
from typing import List
from langchain_community.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import (
ConversationalRetrievalChain,
)
from langchain.document_loaders import PyPDFLoader
from langchain.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 chainlit.types import AskFileResponse
from langchain.document_loaders import PyMuPDFLoader # Added import for PyMuPDFLoader
import chainlit as cl
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOURCES" part should be a reference to the source of the document from which you got your answer.
And if the user greets with greetings like Hi, hello, How are you, etc reply accordingly as well.
Example of your response should be:
The answer is foo
SOURCES: xyz
Begin!
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
def process_pdf_from_link(link: str):
# Download the PDF from the link
pdf_loader = PyMuPDFLoader(link)
docs = pdf_loader.load()
texts = [doc.page_content for doc in docs]
return texts
@cl.on_chat_start
async def on_chat_start():
# Process the PDF from the link
link_23 = "https://ir.tesla.com/_flysystem/s3/sec/000119312523094075/d451342ddef14a-gen.pdf"
link_22 = "https://ir.tesla.com/_flysystem/s3/sec/000156459022024064/tsla-def14a_20220804-gen.pdf"
text1= process_pdf_from_link(link_23)
text2 = process_pdf_from_link(link_22)
texts = text1 + text2
# Create metadata for each chunk
metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
# Create a Chroma vector store
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 Chroma vector store
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
await cl.Message(content="PDF processing complete. You can now ask questions about the proxy document!").send()
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() |