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## Setup
# Import the necessary Libraries
import os
import uuid
import json
import gradio as gr
from dotenv import load_dotenv
from openai import OpenAI
from langchain_community.embeddings.sentence_transformer import (SentenceTransformerEmbeddings)
from langchain_community.vectorstores import Chroma
from huggingface_hub import CommitScheduler
from pathlib import Path
# Create Client
load_dotenv()
os.environ("anyscale_api_key") = os.getenv("anyscale_api_key")
client = OpenAI(
base_url="https://api.endpoints.anyscale.com/v1",
api_key=os.environ("anyscale_api_key")
)
# Define the embedding model and the vectorstore
embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
# Load the persisted vectorDB
collection_name = 'finsights-grey-10k-2023'
vectorstore_persisted = Chroma(
collection_name=collection_name,
embedding_function=embedding_model,
persist_directory='/content/finsights23_db'
)
retriever = vectorstore_persisted.as_retriever(
search_type="similarity",
search_kwargs={'k': 5},
)
# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
scheduler = CommitScheduler(
repo_id="finsights-qna-logs",
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
# Define the Q&A system message
qna_system_message = """
You are an assistant to a financial technology firm who answers user queries on 10-K reports from various indusrty players which contain detailed information about financial performance, risk factors, market trends, and strategic initiatives.
User input will have the context required by you to answer user questions.
This context will begin with the token: ###Context.
The context contains references to specific portions of a document relevant to the user query.
User questions will begin with the token: ###Question.
Please answer only using the context provided in the input. Do not mention anything about the context in your final answer.
If the answer is not found in the context, respond "I don't know".
"""
# Define the user message template
qna_user_message_template = """
###Context
Here are some documents that are relevant to the question mentioned below.
{context}
###Question
{question}
"""
# Define the predict function that runs when 'Submit' is clicked or when a API request is made
def predict(user_input,company):
filter = "dataset/"+company+"-10-k-2023.pdf"
relevant_document_chunks = retriever.invoke(user_input, filter=filter)
context_list = [f"Page {doc.metadata['page']}: {doc.page_content}" for doc in docs]
# Create context_for_query
context_for_query = ".".join(context_list)
# Create messages
prompt = [
{'role':'system','content': qna_system_message},
{'role':'user', 'content': qna_user_message_template.format(context=context_for_query,
question=user_input
)
}
]
# Get response from the LLM
try:
response = client.chat.completions.create(
model="mlabonne/NeuralHermes-2.5-Mistral-7B",
messages=prompt,
temperature=0
)
prediction = response.choices[0].message.content
except Exception as e:
prediction = f'Sorry, I encountered the following error: \n {e}'
print(prediction)
# While the prediction is made, log both the inputs and outputs to a local log file
# While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
# access
with scheduler.lock:
with log_file.open("a") as f:
f.write(json.dumps(
{
'user_input': user_input,
'retrieved_context': context_for_query,
'model_response': prediction
}
))
f.write("\n")
return prediction
# Set-up the Gradio UI
# Add text box and radio button to the interface
# The radio button is used to select the company 10k report in which the context needs to be retrieved.
textbox = gr.Textbox(placeholder = "Enter your query here")
company = gr.Radio(choices=["IBM", "META", "aws", "google", "msft"], label="Company")
# Create the interface
# For the inputs parameter of Interface provide [textbox,company]
demo = gr.Interface(
inputs = [textbox,company],
fn = predict,
outputs = "text",
description="This web API presents an interface to ask questions on contents of of IBM, META, AWS, GOOGLE and MSFT 10-K reports for the year 2023",
article= "Note that questions that are not relevant to the aforermentioned companies' 10-K reports will not be answered",
title = "Q&A for IBM, META, AWS, GOOG & MSFT 10-K Statements",
examples = [["Has the company made any significant acquisitions in the AI space, and how are these acquisitions being integrated into the company's strategy?", ""],
["How much capital has been allocated towards AI research and development?", ""],
["What initiatives has the company implemented to address ethical concerns sorrounding AI, such as faireness, accountability, and privacy?", ""],
["How does the company plan to differentiate itself in the AI space relave to competitors?", ""]],
concurrency_limit=16
)
demo.queue()
demo.launch(share=True, debug =False)
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