1st commit

.gitattributes

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+*.pdf filter=lfs diff=lfs merge=lfs -text
+*.sqlite3 filter=lfs diff=lfs merge=lfs -text

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app.py

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+
+
+## Setup
+# Import the necessary Libraries
+import os
+import uuid
+import joblib
+import json
+import tiktoken
+import pandas as pd
+import gradio as gr
+from openai import OpenAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+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
+os.environ['OPENAI_API_KEY']  = "gl-U2FsdGVkX1+0bNWD6YsVLZUYsn0m1WfLxUzrP0xUFbtWFAfk9Z1Cz+mD8u1yqKtV"; # e.g. gl-U2FsdGVkX19oG1mRO+LGAiNeC7nAeU8M65G4I6bfcdI7+9GUEjFFbplKq48J83by
+os.environ["OPENAI_BASE_URL"] =  "https://aibe.mygreatlearning.com/openai/v1" # e.g. "https://aibe.mygreatlearning.com/openai/v1";
+client = OpenAI()
+
+# Define the embedding model and the vectorstore
+model_name = 'gpt-4o-mini'
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+# Load the persisted vectorDB
+persisted_vectordb_location = '10k-reports_db'
+collection_name = '10k-reports'
+reports_db = Chroma(
+    collection_name=collection_name,
+    persist_directory=persisted_vectordb_location,
+    embedding_function=embedding_model
+)
+reports_db.get()
+
+# Prepare the logging functionality
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="Keytaro/10K-reports-mlops-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 Gen AI Data Scientist. Your task is to automate the extraction, summarization, and analysis of information from the 10-K reports.
+
+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 only context relevant 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 Gen AI Data Scientist. I can only help you with questions related to 10-K reports."
+
+Please adhere to the following guidelines:
+- Your response should only be about the question asked and nothing else.
+- 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 10-K reports"
+- 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 and their source links 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):
+
+    companyfile = {
+        "Amazon": "aws",
+        "Google": "google",
+        "Microsoft": "mtfs",
+        "Meta": "meta",
+        "IBM": "IBM"
+    }.get(company, None)
+    if companyfile is not None:
+        user_input = user_input.replace("the company", company)
+
+    filter = "dataset/"+companyfile+"-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 + f"\n ###Source: \'{d.metadata['source']}\', p.{d.metadata['page']}\n\n " 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=model_name,
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content.strip()
+
+    except Exception as e:
+        prediction = f'Sorry, I encountered the following error: \n {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.
+
+textbox = gr.Textbox()
+company = gr.Radio()
+
+inputs = [
+    gr.Radio(label="user_input", choices=["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 by the company?",
+                                          "What initiatives has the company implemented to address ethical concerns surrounding AI, such as fairness, accountability, and privacy?",
+                                          "How does the company plan to differentiate itself in the AI space relative to competitors?"]),
+    gr.Radio(label="Company", choices=["Amazon", "Google", "Microsoft", "Meta", "IBM"]),
+]
+
+output = gr.Textbox(label="Answer")
+
+# Create the interface
+# For the inputs parameter of Interface provide [textbox,company]
+demo = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=output,
+    title="10-K reports RAG system",
+    description="This API allows you to answer one of the 5 questions based on 10-K reports.",
+    allow_flagging="auto", #
+    concurrency_limit=8 #
+)
+
+demo.queue()
+demo.launch()

requirements.txt

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+openai==1.23.2
+tiktoken==0.6.0
+pypdf==4.0.1
+langchain==0.1.1
+langchain-community==0.0.13
+chromadb==0.4.22
+sentence-transformers==2.3.1