Flask App for OpenAI VectorDBQA

This is a Flask app that uses OpenAI's VectorDBQA to answer questions based on a set of documents. The app loads documents from a directory, splits them into chunks, creates OpenAI embeddings, creates a vector store from the documents, and creates a model from the vector store. The app provides two routes: / and /ask. The / route returns an HTML page with a form for submitting questions. The /ask route accepts a POST request with a JSON payload containing a question and returns a JSON response with the answers.

Installation

1. Clone the repository: git clone https://github.com/username/repo.git
2. Install the dependencies: pip install -r requirements.txt
3. Set the OpenAI API key: export OPENAI_API_KEY=your_api_key

Usage

1. Start the Flask app: python app.py
2. Open a web browser and go to http://localhost:5000
3. Enter a question in the form and click the "Submit" button
4. The app will return the answers to the question Configuration

Configuration

The app can be configured by changing the following variables in app.py:

• MODEL_NAME: The name of the OpenAI model to use
• MAX_TOKENS: The maximum number of tokens to use for the model
• TEMPERATURE: The temperature to use for the model
• LOADER: The document loader to use
• CHAR_TEXT_SPLITTER: The character text splitter to use
• OPENAI_EMBEDDINGS: The OpenAI embeddings to use
• v_store: The vector store to use
• model: The VectorDBQA model to use

License

This code is licensed under the MIT License. See the LICENSE file for details.

Code

import os
import requests
import nltk
import openai
from flask import Flask, render_template, request, jsonify
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import DirectoryLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter

app = Flask(__name__)

# Set OpenAI API key
os.environ["OPENAI_API_KEY"] = "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"
OPENAI_API_KEY = "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"

# Download NLTK data
nltk.download('averaged_perceptron_tagger')

# Set model parameters
# MODEL_NAME = "text-ada-001"
# MODEL_NAME = "gpt-3.5-turbo"
MODEL_NAME = "ada"
MAX_TOKENS = 2048
TEMPERATURE = 0.5

# Load documents from directory
LOADER = DirectoryLoader('docs', glob='**/*.csv')
try:
    docs = LOADER.load()
except Exception as e:
    print(f"Error loading documents: {e}")
    docs = []

if not docs:
    print("No documents found")
    exit()

# Split documents into chunks
CHAR_TEXT_SPLITTER = CharacterTextSplitter(chunk_size=1500, chunk_overlap=0)
try:
    doc_texts = CHAR_TEXT_SPLITTER.split_documents(docs)
except Exception as e:
    print(f"Error splitting documents: {e}")
    doc_texts = []

if not doc_texts:
    print("No document texts found")
    exit()

# Create OpenAI embeddings
OPENAI_EMBEDDINGS = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

# Create vector store from documents
try:
    v_store = Chroma.from_documents(documents=doc_texts, embedding=OPENAI_EMBEDDINGS)
except Exception as e:
    print(f"Error creating vector store: {e}")
    exit()

# Create model from vector store
v_store = Chroma.from_documents(doc_texts, OPENAI_EMBEDDINGS)
model = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=v_store)

# Define Flask routes
@app.route('/')
def index():
    return render_template('index.html', form_id='question-form', question_input_id='question', submit_button_value='Submit', answers_div_id='answers')

@app.route('/ask', methods=['POST'])
def ask():
    data = request.get_json()
    question = data['question']
    try:
        answers = model.run(question)
    except Exception as e:
        print(f"Error running model: {e}")
        answers = []
    return jsonify({'answers': answers})

# Run Flask app
if __name__ == '__main__':
    app.run(debug=True)