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.
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.
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
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.
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.
Adding PostgresML as a memory backend to Auto-GPT is a relatively simple process. The steps involved are:
Download and install Auto-GPT.content_copy link editgit clone https://github.com/postgresml/Auto-GPTcd Auto-GPTgit checkout stable-0.2.2python3 -m venv venvsource venv/bin/activatepip install -r requirements.txt
postgresqlcommand line utility
sudo apt install libpq-dev
sudo yum install libpq-devel
brew install postgresql
pip install psycopg2
Setting up environment variables
.envfile set the following if you are using Docker:content_copy link editPOSTGRESML_HOST=localhostPOSTGRESML_PORT=5443POSTGRESML_USERNAME=postgresPOSTGRESML_PASSWORD=""POSTGRESML_DATABASE=pgml_developmentPOSTGRESML_TABLENAME =autogpt_text_embeddings
If you are using PostgresML cloud, use the hostname and credentials from the cloud platform.
We are using PostgresML fork of Auto-GPT for this tutorial. Our PR to add PostgresML as a memory backend to Auto-GPT is currently under review by Auto-GPT team and will be available as an official backend soon!
.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.
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.
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