Create app.py

app.py

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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='./report_10kdb',
+    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="RAG-investment-recommendation-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 a data scientist working on a Retrieval-Augmented Generation (RAG) system for Finsights Grey Inc.
+
+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 the documentation 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:
+
+Select only context relevant to answer the question.
+Include the source links in your response.
+User questions will begin with the token: ###Question.
+If the question is not related to the provided context, respond with "I don't have enough information to answer that question."
+Please adhere to the following guidelines:
+
+Your response should only be about the question asked and the context provided.
+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 important for you to respond with "I don't know."
+Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
+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
+
+
+def get_predict(question, company):
+    # Implement your prediction logic here
+    if company == "AWS":
+        # Perform prediction for AWS
+        selectedCompany = "aws"
+    elif company == "IBM":
+        # Perform prediction for IBM
+        selectedCompany = "IBM"
+    elif company == "Google":
+        # Perform prediction for Google
+       selectedCompany = "Google"
+    elif company == "Meta":
+        # Perform prediction for Meta
+        selectedCompany = "meta"
+    elif company == "Microsoft":
+        # Perform prediction for Microsoft
+        selectedCompany = "msft"
+    else:
+        return "Invalid company selected"
+
+    output = predict(question, selectedCompany)
+    return output
+
+# 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.
+
+# Create the interface
+# For the inputs parameter of Interface provide [textbox,company]
+
+with gr.Blocks(theme="gradio/seafoam@>=0.0.1,<0.1.0") as demo:
+    with gr.Row():
+        company = gr.Radio(["AWS", "IBM", "Google", "Meta", "Microsoft"], label="Select a company")
+
+    with gr.Row():
+        question = gr.Textbox(label="Enter your question")
+
+    submit = gr.Button("Submit")
+    output = gr.Textbox(label="Output")
+
+    submit.click(
+        fn=get_predict,
+        inputs=[question, company],
+        outputs=output
+    )
+
+demo.queue()
+demo.launch()