PostgresML as a memory backend to Auto-GPT

Santi Adavani

May 3, 2023

Auto-GPT is an open-source, autonomous AI tool that uses GPT-4 to interact with software and services online. PostgresML is an open-source library that allows you to add machine learning capabilities to your PostgreSQL database.

In this blog post, I will show you how to add PostgresML as a memory backend to AutoGPT. This will allow you to use the power of PostgresML to improve the performance and scalability of AutoGPT.

What is Auto-GPT?

Auto-GPT is an open-source, autonomous AI tool that uses GPT-4 to interact with software and services online. It was developed by Toran Bruce Richards and released on March 30, 2023.

Auto-GPT can perform a variety of tasks, including:

• Debugging code
• Writing emails
• Conducting market research
• Developing software applications

Auto-GPT is still under development, but it has the potential to be a powerful tool for a variety of tasks. It is still early days, but Auto-GPT is already being used by some businesses and individuals to improve their productivity and efficiency.

What is PostgresML?

PostgresML is a machine learning extension to PostgreSQL that enables you to perform training and inference on text and tabular data using SQL queries. With PostgresML, you can seamlessly integrate machine learning models into your PostgreSQL database and harness the power of cutting-edge algorithms to process data efficiently.

PostgresML supports a variety of machine learning algorithms, including:

• Natural language processing
• Sentence Embeddings
• Regression
• Classification

What is a memory backend to Auto-GPT and why is it important?

A memory backend is a way to store and access data that AutoGPT needs to perform its tasks. AutoGPT has both short-term and long-term memory. Short-term memory is used to store information that AutoGPT needs to access quickly, such as the current conversation or the state of a game. Long-term memory is used to store information that AutoGPT needs to access more slowly, such as general knowledge or the rules of a game.

There are a number of different memory backends available for AutoGPT, each with its own advantages and disadvantages. The choice of memory backend depends on the specific needs of the application. Some of the most popular memory backends for AutoGPT are Redis, Pinecone, Milvus, and Weaviate.

Why add PostgresML as a memory backend to Auto-GPT?

Developing Auto-GPT-powered applications requires a range of APIs from OpenAI as well as a stateful database to store data related to business logic. PostgresML brings AI tasks like sentence embeddings to the database, reducing complexity for app developers, and yielding a host of additional performance, cost and quality advantages. We will use the vector datatype available from the pgvector extension to store (and later index) embeddings efficiently.

Register the memory backend module with Auto-GPT

Adding PostgresML as a memory backend to Auto-GPT is a relatively simple process. The steps involved are:

1. Download and install Auto-GPT.

   
                   content_copy
                   link
                   edit
               
   git clone https://github.com/postgresml/Auto-GPT
   cd Auto-GPT
   git checkout stable-0.2.2
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

2. Start PostgresML using Docker or sign up for a free PostgresML account.

3. Install postgresql command line utility

   • Ubuntu: sudo apt install libpq-dev
   • Centos/Fedora/Cygwin/Babun.: sudo yum install libpq-devel
   • Mac: brew install postgresql

4. Install psycopg2 in

   • pip install psycopg2

5. Setting up environment variables

   In your .env file set the following if you are using Docker:

   
                   content_copy
                   link
                   edit
               
   POSTGRESML_HOST=localhost
   POSTGRESML_PORT=5443
   POSTGRESML_USERNAME=postgres
   POSTGRESML_PASSWORD=""
   POSTGRESML_DATABASE=pgml_development
   POSTGRESML_TABLENAME =autogpt_text_embeddings
   

   If you are using PostgresML cloud, use the hostname and credentials from the cloud platform.

Start Auto-GPT with PostgresML memory backend

Once the .env file has all the relevant PostgresML settings you can start autogpt that uses PostgresML backend using the following command:

                content_copy
                link
                edit
            
python -m autogpt -m postgresml

You will see Auto-GPT in action with PostgresML backend as shown below. You should see Using memory of type: PostgresMLMemory in the logs.

Conclusion

In this blog post, I showed you how to add PostgresML as a memory backend to Auto-GPT. Adding PostgresML as a memory backend can significantly accelerate performance and scalability of Auto-GPT. It can enable you to rapidly prototype with Auto-GPT and build AI-powered applications.