app.py

# Import the necessary Libraries
import os
import uuid
import json
 
import gradio as gr

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
from dotenv import load_dotenv


# 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']
)

embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
# Define the embedding model and the vectorstore

collection_name = 'report-10k-2024'

vectorstore_persisted = Chroma(
    collection_name=collection_name,
    persist_directory='./dataset-10k',
    embedding_function=embedding_model
)

# Load the persisted vectorDB

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="Project_3_Log",
    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 AI assistant to help Finsights Grey Inc., an innovative financial technology firm, develop a Retrieval-Augmented Generation (RAG) 
system to automate the extraction, summarization, and analysis of information from 10-K reports. Your knowledge base was last updated in August 2023.

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 documents relevant to the user's query, along with source links.
The source for a context will begin with the token ###Source

When crafting your response:
1. Select the most relevant context or contexts to answer the question.
2. Include the source links in your response.
3. User questions will begin with the token: ###Question.
4. If the question is irrelevant to information from 10-K reports respond with - "I am an assistant for information found in the 10-K reports. I can only help you with questions related to the 10K reports."

Please adhere to the following guidelines:
- Answer only using the context provided.
- Do not mention anything about the context in your final answer.
- If the answer is not found in the context, it is very very important for you to respond with "I don't know. Please check the docs @ 'https://docs.streamlit.io/'"
- Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Sources:
- Do not make up sources. Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.
 
Here is an example of how to structure your response:
Answer:
[Answer]
Source
[Source]
"""

# Define the user message template
qna_user_message_template = """
###Context
Here are some documents that are relevant to the question.
{context}
```
{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 = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})

    # Create context_for_query
    context_list = [d.page_content for d in relevant_document_chunks]
    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='mistralai/Mixtral-8x7B-Instruct-v0.1',
            messages=prompt,
            temperature=0
        )

        prediction = response.choices[0].message.content

    except Exception as e:
        prediction = e

    # 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.

with gr.Blocks() as demo:
    with gr.Row():
        question = gr.Textbox(label="Enter your question")
        company = gr.Radio(["aws", "IBM", "google", "meta", "msft"], label="Select a company")
    
    submit = gr.Button("Submit")
    output = gr.Textbox(label="Output")

    submit.click(
        fn=predict,
        inputs=[question, company],
        outputs=output
    )

demo.launch()
# Create the interface
# For the inputs parameter of Interface provide [textbox,company]


demo.queue()
demo.launch()