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# 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}
)
# Define the Q&A system message
qna_system_message = """
You are an assistant to a coder. Your task is to provide relevant information about the Python package Streamlit.
User input will include the necessary context for you to answer their 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 streamlit respond with - "I am an assistant for streamlit Docs. I can only help you with questions related to streamlit"
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
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() |