AI ChatBot MongoDB

AI Chatbot that can be integrated Applications specifically using MongoDB Database. Built using Streamlit, Langchain & OpenAI & Custom MongoDB Connector.

Setup

NOTE: Instructions for Ubuntu 22

1. Install poetry globally using pipx pipx install poetry
2. Create .env file by taking reference from .env.example and based upon your Database usecase, follow the below sections for updating your .env with DB credentials.

NoSQL Database

1. For NoSQL Database, branch name is main(default branch)
2. Change your working directory to the root of this repo and run, poetry install
3. In your .env, add your MongoDB credentials.
4. Simply comment out MONDODB_REPLICA_SET_NAME environment variable since it is not required for local or development run.

SQL Database

1. For SQL Database, branch name is sql -> git checkout sql
2. Install and run the SQL Query API as well using,
   • NLPReporting Repo and dev branch.
   • Follow the README available in SQLQueryAPI folder to run
   • API available at http://localhost:8000
3. In your .env, use http://localhost:8000/SQL/query as the value for DB_TOOL_API environment variable.
4. Change your working directory to the root of this repo and run, poetry install

Run Locally

make run-app

NOTE: For SQL Database usecase, make sure that SQL Query API is also running by following the above Setup Steps for SQL Database.

There are few other useful commands that are listed in the Makefile.

1. make run-app for running the Chatbot App locally
2. make build for building the Docker Image
3. make run-docker for running the Docker Container. Access the Chatbot App at http://localhost:8501

Deployment

1. make build for building the Docker image
2. make run-docker for running the Docker container and map your Nginx/Apache to point at http://localhost:8501.

Issues with SQL DB Usecase

OpenAI models used in NLPReporting Repo for SQL Query API use the OpenAI Legacy APIs which allows max context token length of around 4K tokens which can be an issue if your SQL Database schema text consumes more than this context length. Possible solutions,

1. Replace the Model used in NLPReporting -> SQLQueryAPI -> src/services/sqllangchain_service.py from default to the OpenAI Chat Completions Models(gpt-3.5-turbo, gpt-4-turbo) which have larger context length. Basic Example,

   from langchain_openai import ChatOpenAI
   
   def get_query(chat_query, db_uri):
       db = SQLDatabase.from_uri(db_uri)
       llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)  # gpt-4-turbo
       ...
   

2. Change the prompt passed to these models to return only the SQL query and not the description too. Below is an example of MongoDB prompt to gpt-4-turbo model

   Main sentence in the prompt is Do not include any explanations, only provide a JSON object following this format without deviation.

   _mongod_prompt = """You are a MongoDB expert. Given an input question, first create a syntactically correct pymongo code to run, then look at the results of the query and return the answer to the input question.
   Unless the user specifies in the question a specific number of examples to obtain, include .limit(10) method in pymongo pipeline, if user says to get all data then don't use .limit. You can order the results to return the most informative data in the database.
   Never query for all columns from a collection. You must query only the columns that are needed to answer the question.
   Use pymongo aggregate etc helpful methods wherever needed.
   Pay attention to following points,
   - Use only the column names you can see in the collections below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which collection.
   - Todays date & time is {current_date}. If user query involves date then always use python's datetime module. Don't use ISODate or any other MongoDB Date Operator.
   - Use $lookup when referencing other collections.
   - MongoDB Operators should be suffixed with $ strictly.
   - Do not include any explanations, only provide a JSON object following this format without deviation.
   
   {{"collection": value of MongoDBCollection to run pymongo pipeline, "pipeline": value of pymongo pipeline}}
   JSON object:
   """