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from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyMuPDFLoader
import os
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
import chainlit as cl
@cl.on_chat_start
async def start():
welcome_message1 = "Hey, Welcome to **10K-GPT**!"
welcome_message2 = "**10K-GPT** is designed to provide you with an interactive, user-friendly interface that lets you pose queries and fetch answers from Form 10-K documents of some of the world's leading tech giants:\n- **Meta**\n- **Amazon**\n- **Alphabet**\n- **Apple**\n- **Microsoft**,\n\nfor the years **2022, 2021, and 2020**.\n\nPlease ask a question to begin!"
sample_questions="Some of the questions you can try:\n***"
elements = [
cl.Image(path="10KGPTLogo.png", name="10K-GPT", display="inline"),
# cl.Text(content=welcome_message1, name="10K-GPT", display="inline"),
]
await cl.Message(content=welcome_message1, elements=elements).send()
await cl.Message(content=welcome_message2).send()
@cl.langchain_factory(use_async=False)
def load():
embeddings = OpenAIEmbeddings()
llm = ChatOpenAI(temperature=0, model="gpt-4", streaming=True)
apple_2022_docs_store = FAISS.load_local(r'data/datastores/apple_2022', embeddings)
apple_2021_docs_store = FAISS.load_local(r'data/datastores/apple_2021', embeddings)
apple_2020_docs_store = FAISS.load_local(r'data/datastores/apple_2020', embeddings)
microsoft_2022_docs_store = FAISS.load_local(r'data/datastores/msft_2022', embeddings)
microsoft_2021_docs_store = FAISS.load_local(r'data/datastores/msft_2021', embeddings)
microsoft_2020_docs_store = FAISS.load_local(r'data/datastores/msft_2020', embeddings)
amazon_2022_docs_store = FAISS.load_local(r'data/datastores/amzn_2022', embeddings)
amazon_2021_docs_store = FAISS.load_local(r'data/datastores/amzn_2021', embeddings)
amazon_2020_docs_store = FAISS.load_local(r'data/datastores/amzn_2020', embeddings)
alphabet_2022_docs_store = FAISS.load_local(r'data/datastores/alphbt_2022', embeddings)
alphabet_2021_docs_store = FAISS.load_local(r'data/datastores/alphbt_2021', embeddings)
alphabet_2020_docs_store = FAISS.load_local(r'data/datastores/alphbt_2020', embeddings)
meta_2022_docs_store = FAISS.load_local(r'data/datastores/meta_2022', embeddings)
meta_2021_docs_store = FAISS.load_local(r'data/datastores/meta_2021', embeddings)
meta_2020_docs_store = FAISS.load_local(r'data/datastores/meta_2020', embeddings)
apple_2022_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=apple_2022_docs_store.as_retriever(search_kwargs={'k':5}))
apple_2021_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=apple_2021_docs_store.as_retriever(search_kwargs={'k':5}))
apple_2020_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=apple_2020_docs_store.as_retriever(search_kwargs={'k':5}))
microsoft_2022_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=microsoft_2022_docs_store.as_retriever(search_kwargs={'k':5}))
microsoft_2021_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=microsoft_2021_docs_store.as_retriever(search_kwargs={'k':5}))
microsoft_2020_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=microsoft_2020_docs_store.as_retriever(search_kwargs={'k':5}))
amazon_2022_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=amazon_2022_docs_store.as_retriever(search_kwargs={'k':5}))
amazon_2021_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=amazon_2021_docs_store.as_retriever(search_kwargs={'k':5}))
amazon_2020_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=amazon_2020_docs_store.as_retriever(search_kwargs={'k':5}))
meta_2022_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=meta_2022_docs_store.as_retriever(search_kwargs={'k':5}))
meta_2021_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=meta_2021_docs_store.as_retriever(search_kwargs={'k':5}))
meta_2020_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=meta_2020_docs_store.as_retriever(search_kwargs={'k':5}))
alphabet_2022_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=alphabet_2022_docs_store.as_retriever(search_kwargs={'k':5}))
alphabet_2021_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=alphabet_2021_docs_store.as_retriever(search_kwargs={'k':5}))
alphabet_2020_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=alphabet_2020_docs_store.as_retriever(search_kwargs={'k':5}))
tools = [
Tool(
name="Apple Form 10K 2022",
func=apple_2022_qa.run,
description="useful when you need to answer from Apple 2022",
),
Tool(
name="Apple Form 10K 2021",
func=apple_2021_qa.run,
description="useful when you need to answer from Apple 2021",
),
Tool(
name="Apple Form 10K 2020",
func=apple_2020_qa.run,
description="useful when you need to answer from Apple 2020",
),
Tool(
name="Microsoft Form 10K 2022",
func=microsoft_2022_qa.run,
description="useful when you need to answer from Microsoft 2022",
),
Tool(
name="Microsoft Form 10K 2021",
func=microsoft_2021_qa.run,
description="useful when you need to answer from Microsoft 2021",
),
Tool(
name="Microsoft Form 10K 2020",
func=microsoft_2020_qa.run,
description="useful when you need to answer from Microsoft 2020",
),
Tool(
name="Meta Form 10K 2022",
func=meta_2022_qa.run,
description="useful when you need to answer from Meta 2022",
),
Tool(
name="Meta Form 10K 2021",
func=meta_2021_qa.run,
description="useful when you need to answer from Meta 2021",
),
Tool(
name="Meta Form 10K 2020",
func=meta_2020_qa.run,
description="useful when you need to answer from Meta 2020",
),
Tool(
name="Alphabet Form 10K 2022",
func=alphabet_2022_qa.run,
description="useful when you need to answer from Alphabet or Google 2022",
),
Tool(
name="Alphabet Form 10K 2021",
func=alphabet_2021_qa.run,
description="useful when you need to answer from Alphabet or Google 2021",
),
Tool(
name="Alphabet Form 10K 2020",
func=alphabet_2020_qa.run,
description="useful when you need to answer from Alphabet or Google 2020",
),
Tool(
name="Amazon Form 10K 2022",
func=amazon_2022_qa.run,
description="useful when you need to answer from Amazon 2022",
),
Tool(
name="Amazon Form 10K 2021",
func=amazon_2021_qa.run,
description="useful when you need to answer from Amazon 2021",
),
Tool(
name="Amazon Form 10K 2020",
func=amazon_2020_qa.run,
description="useful when you need to answer from Amazon 2020",
),
]
# Construct the agent. We will use the default agent type here.
# See documentation for a full list of options.
return initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
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