Spaces:
Sleeping
Sleeping
File size: 7,911 Bytes
116bc23 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
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
)
|