pgai

A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL

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pgai simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL. It brings embedding and generation AI models closer to the database, allowing users to create embeddings, retrieve LLM chat completions, reason over data for classification, summarization, and data enrichment directly from within PostgreSQL in a SQL query. The tool requires an OpenAI API key and a PostgreSQL client to enable AI functionality in the database. Users can install pgai from source, run it in a pre-built Docker container, or enable it in a Timescale Cloud service. The tool provides functions to handle API keys using psql or Python, and offers various AI functionalities like tokenizing, detokenizing, embedding, chat completion, and content moderation.

README:

Power your AI applications with PostgreSQL

Docs · Join the pgai Discord! · Try timescale for free! · Changelog

Supercharge your PostgreSQL database with AI capabilities. Supports:

Automatic creation and synchronization of vector embeddings for your data
Seamless vector and semantic search
Retrieval Augmented Generation (RAG) directly in SQL
Ability to call out to leading LLMs like OpenAI, Ollama, Cohere, and more via SQL.
Built-in utilities for dataset loading and processing

All with the reliability, scalability, and ACID compliance of PostgreSQL.

Docker

See the install via docker guide for docker compose files and detailed container instructions.

Timescale Cloud

Try pgai on cloud by creating a free trial account on Timescale Cloud.

Installing pgai into an existing PostgreSQL instance (Linux / MacOS)

See the install from source guide for instructions on how to install pgai from source.

Quick Start

This section will walk you through the steps to get started with pgai and Ollama using docker and show you the major features of pgai.

Please note that using Ollama requires a large (>4GB) download of the docker image and model. If you don't want to download so much data, you may want to use the OpenAI quick start or VoyageAI quick start instead.

Setup

Download the docker compose file file.

curl -O https://raw.githubusercontent.com/timescale/pgai/main/examples/docker_compose_pgai_ollama/docker-compose.yml

Start the docker compose file.
```
docker compose up -d
```
This will start Ollama and a PostgreSQL instance with the pgai extension installed.
Download the Ollama models. We'll use the all-minilm model for embeddings and the tinyllama model for reasoning.
```
docker compose exec ollama ollama pull all-minilm
docker compose exec ollama ollama pull tinyllama
```

Create a table, run a vectorizer, and perform semantic search

Connect to the database in your local developer environment The easiest way connect to the database is with the following command: docker compose exec -it db psql.

Alternatively, you can connect to the database with the following connection string: postgres://postgres:postgres@localhost:5432/postgres.

Enable pgai on your database

CREATE EXTENSION IF NOT EXISTS ai CASCADE;

Create a table with the data you want to embed from a huggingface dataset

We'll create a table named wiki from a few rows of the english-language wikimedia/wikipedia dataset.

First, we'll create the table:
```
CREATE TABLE wiki (
    id      TEXT PRIMARY KEY,
    url     TEXT,
    title   TEXT,
    text    TEXT
);
```
Then, we'll load the data from the huggingface dataset:
```
SELECT ai.load_dataset('wikimedia/wikipedia', '20231101.en', table_name=>'wiki', batch_size=>5, max_batches=>1, if_table_exists=>'append');
```
Related documentation: load dataset from huggingface.
Create a vectorizer for wiki

To enable semantic search on the wiki table, we need to create vector embeddings for the text column. We use a vectorizer to automatically create these embeddings and keep them in sync with the data in the wiki table.
```
SELECT ai.create_vectorizer(
     'wiki'::regclass,
     loading => ai.loading_column('text'),
     destination => 'wiki_embeddings',
     embedding => ai.embedding_ollama('all-minilm', 384)
);
```
Related documentation: vectorizer usage guide and vectorizer API reference.
Check the progress of the vectorizer embedding creation
```
select * from ai.vectorizer_status;
```
Click to see the output

id source_table target_table view pending_items

1 public.wiki public.wiki_embeddings_store public.wiki_embeddings 10000

All the embeddings have been created when the pending_items column is 0. This may take a few minutes as the model is running locally and not on a GPU.
Search the embeddings

We'll search the embeddings for the concept of "properties of light" even though these words are not in the text of the articles. This is possible because vector embeddings capture the semantic meaning of the text.

Semantic search is a powerful feature in its own right, but it is also a key component of Retrieval Augmented Generation (RAG).
```
SELECT title, chunk
FROM wiki_embeddings 
ORDER BY embedding <=> ai.ollama_embed('all-minilm', 'properties of light')
LIMIT 1;
```
Click to see the output

title chunk

Albedo Water reflects light very differently from typical terrestrial materials. The reflectivity of a water surface is calculated using the Fresnel equations....

This query selects from the wiki_embeddings view, which is created by the vectorizer and joins the embeddings with the original data in the wiki table to give us the ability to search using the embeddings but still be able to access (or filter on) all the data in the original table (e.g. the title column).

Note the ai.ollama_embed function is used to call the all-minilm model. This is part of pgai's model calling capabilities.

id	source_table	target_table	view	pending_items
1	public.wiki	public.wiki_embeddings_store	public.wiki_embeddings	10000

title	chunk
Albedo	Water reflects light very differently from typical terrestrial materials. The reflectivity of a water surface is calculated using the Fresnel equations....

Modify your data and have the vectorizer automatically update the embeddings

We'll add a row about pgai to the wiki table and have the vectorizer automatically update the embeddings.

INSERT INTO wiki (id, url, title, text) VALUES (11,'https://en.wikipedia.org/wiki/Pgai', 'pgai - Power your AI applications with PostgreSQL', 'pgai is a tool to make developing RAG and other AI applications easier. It makes it simple to give an LLM access to data in your PostgreSQL database by enabling semantic search on your data and using the results as part of the Retrieval Augmented Generation (RAG) pipeline. This allows the LLM to answer questions about your data without needing to being trained on your data.');

And now you don't need to do anything to update the embeddings. The vectorizer will automatically create the embeddings for the new row without any intervention from you. After a few seconds, you can run a search query related to the new entry and see it returned as part of the results:

SELECT title, chunk
FROM wiki_embeddings 
ORDER BY embedding <=> ai.ollama_embed('all-minilm', 'AI tools')
LIMIT 1;

Perform Retrieval Augmented Generation (RAG)

In this section, we'll have the LLM answer questions about pgai based on the wiki entry we added by using RAG. The LLM was never trained on the pgai wiki entry, and so it needs data in the database to answer questions about pgai.

You can perform RAG purely from within the database using SQL or use a python script to interact with the database and perform RAG. We often find that using SQL is easier to create a quick prototype and get started but as the project matures people easily switch to using Python to have more control and make use of Python tooling.

Click to perform RAG within SQL

Define a function to perform RAG

We'll create a function that uses RAG to allow an LLM to answer questions about pgai based on the wiki entry we added.

RAG involves two steps:

Perform a similarity search to find the most relevant chunks of data.
Use the LLM to generate a response using the relevant chunks as context.

CREATE OR REPLACE FUNCTION generate_rag_response(query_text TEXT)
RETURNS TEXT AS $$
DECLARE
    context_chunks TEXT;
    response JSONB;
BEGIN
    -- Perform similarity search to find relevant wiki article
    SELECT string_agg(title || ': ' || chunk, E'\n') INTO context_chunks
    FROM
    (
        SELECT title, chunk
        FROM wiki_embeddings 
        ORDER BY embedding <=> ai.ollama_embed('all-minilm', query_text)
        LIMIT 3
    ) AS relevant_posts;

    raise notice 'Context provided to LLM: %', context_chunks;

    -- Generate a summary using tinyllama
    select ai.ollama_generate('tinyllama', 
    query_text || E'\nUse the following context to respond.\n' || context_chunks) INTO response;

    RETURN response->>'response';
END;
$$ LANGUAGE plpgsql;

Use the RAG function to answer questions about the wiki data

SELECT generate_rag_response('What can I use pgai for?') as response;

Click here to see the output

response
PGAI is a tool that makes it easier for developers to create AI applications by providing access to data in a PostgreSQL database using Semantic Search and answering RAG (Recommendation and Answer Generation) questions. This allows the LLM (Language Model) to answer questions about unseen data without being trained on your data, making it an important tool for building accurate and efficient AI applications. The context suggests that PGAI can be useful in a variety of industries or use cases where data access is critical, such as healthcare, finance, or customer service.

response

PGAI is a tool that makes it easier for developers to create AI applications by providing access to data in a PostgreSQL database using Semantic Search and answering RAG (Recommendation and Answer Generation) questions. This allows the LLM (Language Model) to answer questions about unseen data without being trained on your data, making it an important tool for building accurate and efficient AI applications. The context suggests that PGAI can be useful in a variety of industries or use cases where data access is critical, such as healthcare, finance, or customer service.

Click to perform RAG with Python

Install python dependencies
```
pip install psycopg pgvector ollama
```

Run the Python script to perform RAG

import psycopg
from pgvector.psycopg import register_vector
from typing import Optional, List, NamedTuple
from ollama import Client
from dataclasses import dataclass

@dataclass
class ChunkData:
    """Represents a chunk of text with its title and content."""
    title: str
    chunk: str

def create_db_connection() -> psycopg.Connection:
    """Create and return a database connection."""
    conn = psycopg.connect(
        "postgres://postgres:postgres@localhost:5432/postgres"
        # Modify connection string as needed for your setup
    )
    register_vector(conn)
    return conn

def get_embedding(client: Client, text: str) -> list[float]:
    """Get embeddings using Ollama's all-minilm model."""
    response = client.embeddings(model='all-minilm', prompt=text)
    return response['embedding']

def get_relevant_chunks(cur: psycopg.Cursor, embedding: list[float], limit: int = 1) -> List[ChunkData]:
    """
    Retrieve the most relevant chunks based on vector similarity.
    
    Args:
        cur: Database cursor
        embedding: Query embedding vector
        limit: Number of chunks to retrieve
    
    Returns:
        List of ChunkData objects containing relevant chunks
    """
    query = """
    SELECT title, chunk
    FROM wiki_embeddings 
    ORDER BY embedding <=> %s::vector
    LIMIT %s
    """
    
    cur.execute(query, (embedding, limit))
    return [ChunkData(title=row[0], chunk=row[1]) for row in cur.fetchall()]

def format_context(chunks: List[ChunkData]) -> str:
    """
    Format the chunks into a single context string.
    
    Args:
        chunks: List of ChunkData objects
    
    Returns:
        Formatted context string
    """
    return "\n\n".join(f"{chunk.title}:\n{chunk.chunk}" for chunk in chunks)

def generate_rag_response(query_text: str) -> Optional[str]:
    """
    Generate a RAG response using pgai, Ollama embeddings, and database content.
    
    Args:
        query_text: The question or query to answer
    
    Returns:
        str: The generated response from the LLM
    """
    try:
        # Initialize Ollama client
        client = Client(host='http://localhost:11434')
        
        with create_db_connection() as conn:
            with conn.cursor() as cur:
                # Get embeddings for the query using Ollama SDK
                query_embedding = get_embedding(client, query_text)
                
                # Get relevant chunks
                relevant_chunks = get_relevant_chunks(cur, query_embedding)
                
                # Format context
                context = format_context(relevant_chunks)
                
                # Print context for debugging (optional)
                print("Context provided to LLM:")
                print("------------------------")
                print(context)
                print("------------------------")
                
                # Construct prompt with context
                prompt = f"""Question: {query_text}

Please use the following context to provide an accurate response:

{context}

Answer:"""
                
                # Generate response using Ollama SDK
                response = client.generate(
                    model='tinyllama',
                    prompt=prompt,
                    stream=False
                )
                
                return response['response']
                
    except Exception as e:
        print(f"Error generating RAG response: {e}")
        return None

def main():
    # Example usage
    questions = [
        "What can I use pgai for?",
    ]
    
    for question in questions:
        print("\n" + "="*50)
        print(f"Question: {question}")
        print("-"*50)
        
        response = generate_rag_response(question)
        if response:
            print("\nResponse:")
            print(response)
        else:
            print("Failed to generate response")

if __name__ == "__main__":
    main()

Generate a summary of the article in the database

Generate a summary of the article in the database

We'll generate a summary of the search results using the ai.ollama_generate function (this will take a few minutes).

SELECT answer->>'response' as summary
FROM ai.ollama_generate('tinyllama', 
'Summarize the following and output the summary in a single sentence: '|| (SELECT text FROM wiki WHERE title like 'pgai%')) as answer;

Click to see the output

summary
Pgai is a tool that simplifies the process of making AI applications easier by providing easy access to data in PostgreSQL and enabling semantic search on the data for the Retrieval Augmented Generation (RAG) pipeline. This allows the AI system to answer questions about unseen data without being trained on it, simplifying the entire process.

This is just one example of model calling capabilities. Model calling can be used for a variety of tasks, including classification, summarization, moderation, and other forms of data enrichment.

Features

Work with embeddings generated from your data

Automatically create and sync vector embeddings for your data (learn more)
Search your data using vector and semantic search (learn more)
Implement Retrieval Augmented Generation inside a single SQL statement (learn more)
Perform high-performance, cost-efficient ANN search on large vector workloads with pgvectorscale, which complements pgvector.

Leverage LLMs for data processing tasks

Retrieve LLM chat completions from models like Claude Sonnet 3.5, OpenAI GPT4o, Cohere Command, and Llama 3 (via Ollama). (learn more)
Reason over your data and facilitate use cases like classification, summarization, and data enrichment on your existing relational data in PostgreSQL (see an example).

Useful utilities

Load datasets from Hugging Face into your database with ai.load_dataset.
Use chunking algorithms to split text with SQL functions.

Resources

Why we built it

Quick start guides

Tutorials about pgai vectorizer

Tutorials about pgai model calling

Contributing

We welcome contributions to pgai! See the Contributing page for more information.

Automated embedding and semantic search

Automatically create and sync vector embeddings for your data
Search your data using vector and semantic search
Implement Retrieval Augmented Generation inside a single SQL statement

Automatically create and sync LLM embeddings for your data

The pgvector and pgvectorscale extensions allow you to store vector embeddings in your database and perform fast and efficient vector search. The pgai Vectorizer builds on top of these extensions to automatically create and synchronize embeddings for any text data in your database.

With one line of code, you can define a vectorizer that creates embeddings for data in a table:

SELECT ai.create_vectorizer(
    <table_name>::regclass,
    destination => <embedding_table_name>,
    loading => ai.loading_column(<column_name>),
    embedding => ai.embedding_ollama(<model_name>, <dimensions>)
);

This newly created vectorizer will automatically track any changes to the data in the source table and update the destination embedding table with the new embeddings asynchronously.

Automate AI embedding with pgai Vectorizer shows you how to implement embeddings in your own data. On a self-hosted Postgres installation, you use a Vectorizer Worker to asynchronously processes your vectorizers. When you create Vectorizers in a Timescale Cloud database, embeddings are automatically created and synchronized in the background.

Note: Timescale Cloud currently supports embedding natively with OpenAI. To use Ollama on the data in your Timescale Cloud service, set scheduling => ai.scheduling_none() in the configuration for your service, then install the vectorizer worker locally and configure it to connect to your Timescale Cloud service.

Search your data using vector and semantic search

pgai exposes a set of functions to directly interact with the LLM models through SQL, enabling you to do semantic search directly in your database:

SELECT
   chunk,
   embedding <=> ai.ollama_embed(<embedding_model>, 'some-query') as distance
FROM <embedding_table>
ORDER BY distance
LIMIT 5;

This is a perfectly normal SQL query. You can combine it with where clauses and other SQL features to further refine your search. pgai solves the missing where clause in vector search problem for real.

Implement Retrieval Augmented Generation inside a single SQL statement

Similar to semantic search, pgai LLM functions enable you to implement RAG directly in your database. For example:

Create a RAG function:

CREATE OR REPLACE FUNCTION generate_rag_response(query_text TEXT)
RETURNS TEXT AS $$
DECLARE
   context_chunks TEXT;
   response TEXT;
BEGIN
   -- Perform similarity search to find relevant blog posts
   SELECT string_agg(title || ': ' || chunk, E'\n') INTO context_chunks
   FROM
   (
       SELECT title, chunk
       FROM blogs_embedding
       ORDER BY embedding <=> ai.ollama_embed('nomic-embed-text', query_text)
       LIMIT 3
   ) AS relevant_posts;

   -- Generate a summary using llama3
   SELECT ai.ollama_chat_complete
   ( 'llama3'
   , jsonb_build_array
     ( jsonb_build_object('role', 'system', 'content', 'you are a helpful assistant')
     , jsonb_build_object
       ('role', 'user'
       , 'content', query_text || E'\nUse the following context to respond.\n' || context_chunks
       )
     )
   )->'message'->>'content' INTO response;

   RETURN response;
END;
$$ LANGUAGE plpgsql;

Execute your function in a SQL query:

SELECT generate_rag_response('Give me some startup advice');

Model calling

Model calling is a feature of pgai that allows you to call LLM models from SQL. This lets you leverage the power of LLMs for a variety of tasks, including classification, summarization, moderation, and other forms of data enrichment.

The following models are supported (click on the model to learn more):

Model	Tokenize	Embed	Chat Complete	Generate	Moderate	Classify	Rerank
Ollama		✔️	✔️	✔️
OpenAI	✔️️	✔️	✔️		✔️
Anthropic				✔️
Cohere	✔️	✔️	✔️			✔️	✔️
Voyage AI		✔️
Huggingface (with LiteLLM)		✔️
Mistral (with LiteLLM)		✔️
Azure OpenAI (with LiteLLM)		✔️
AWS Bedrock (with LiteLLM)		✔️
Vertex AI (with LiteLLM)		✔️

Some examples:

Learn how to moderate content directly in the database using triggers and background jobs.
load datasets directly from Hugging Face into your database.
Leverage LLMs for data processing tasks such as classification, summarization, and data enrichment (see the OpenAI example).

Get involved

pgai is still at an early stage. Now is a great time to help shape the direction of this project; we are currently deciding priorities. Have a look at the list of features we're thinking of working on. Feel free to comment, expand the list, or hop on the Discussions forum.

To get started, take a look at how to contribute and how to set up a dev/test environment.

About Timescale

Timescale is a PostgreSQL database company. To learn more visit the timescale.com.

Timescale Cloud is a high-performance, developer focused, cloud platform that provides PostgreSQL services for the most demanding AI, time-series, analytics, and event workloads. Timescale Cloud is ideal for production applications and provides high availability, streaming backups, upgrades over time, roles and permissions, and great security.

For Tasks:

Click tags to check more tools for each tasks

create embeddings retrieve chat completions reason over data classify data summarize data

For Jobs:

data scientist database administrator ai engineer software developer ai researcher

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