The pgrag extension

The pgrag extension and its accompanying model extensions are designed for creating end-to-end Retrieval-Augmented Generation (RAG) pipelines without leaving your SQL client. No additional programming languages or libraries are required. With functions provided by pgrag and a Postgres database with pgvector, you can build a complete RAG pipeline via SQL.

What is RAG?

RAG stands for Retrieval-Augmented Generation. It's the search for information relevant to a question that includes information alongside the question in a prompt to an AI chat model. For example, "ChatGPT, please answer questions x using information Y".

What's included in a RAG pipeline?

There are two main stages in a RAG pipeline:

1. Preparing and indexing the information: This stage involves:
   • Loading documents and extracting text
   • Splitting the documents into chunks
   • Generating embeddings for the chunks
   • Storing the embeddings alongside chunks in your vector database
2. Handling incoming questions: This second stage involves:
   • Vectorizing the question to create an embedding
   • Using the question embedding to find relevant document chunks based on the shortest vector distances
   • Retrieving document chunks from the database
   • Reranking the chunks for the best-matching ones
   • Prompting the generative AI chat model with the question and relevant document chunks
   • Generating the answer

What does pgrag support?

With the exception of storing and retrieving embeddings, which is supported by Postgres with pgvector, pgrag supports all of the steps listed above. Specifically, pgrag supports:

• Text extraction and conversion

  • Simple text extraction from PDF documents (using pdf-extract). Currently, there is no Optical Character Recognition (OCR) or support for complex layout and formatting.
  • Simple text extraction from .docx documents (using docx-rs).
  • HTML conversion to Markdown (using htmd).

• Text chunking

  • Text chunking by character count (using text-splitter).
  • Text chunking by token count (also using text-splitter).

• Local embedding and reranking models

  • Local tokenising + embedding generation with 33M parameter model bge-small-en-v1.5 (using ort via fastembed).
  • Local tokenising + reranking with 33M parameter model jina-reranker-v1-tiny-en (also using ort via fastembed).

  note

  These models run locally on your Postgres server. They are packaged as separate extensions that accompany pgrag, because they are large (>100MB), and because we may want to add support for more models in future in the form of additional pgrag model extensions.

• Remote embedding and chat models

Installation

SET neon.allow_unstable_extensions='true';

To install pgrag to a Neon Postgres database, run the following commands:

create extension if not exists rag cascade;
create extension if not exists rag_bge_small_en_v15 cascade;
create extension if not exists rag_jina_reranker_v1_tiny_en cascade;

The first extension is the pgrag extension. The other two extensions are the model extensions for local tokenising, embedding generation, and reranking. The three extensions have no dependencies on each other, but all depend on pgvector. Specifying cascade ensures that pgvector is installed.

pgrag functions

This section lists the functions provided by pgrag. For function usage examples, refer to the end-to-end RAG example below or the pgrag GitHub repository.

• Text extraction

  These functions extract text from PDFs, Word files, and HTML.

  • rag.text_from_pdf(bytea) -> text
  • rag.text_from_docx(bytea) -> text
  • rag.markdown_from_html(text) -> text

• Splitting text into chunks

  These functions split the extracted text into chunks by character count or token count.

  • rag.chunks_by_character_count(text, max_chars, overlap) -> text[]
  • rag_bge_small_en_v15.chunks_by_token_count(text, max_tokens, overlap) -> text[]

• Generating embeddings for chunks

  These functions generate embeddings for chunks either directly in the extension using a small but best-in-class model on the database server or by calling out to a 3rd-party API such as OpenAI.

  • rag_bge_small_en_v15.embedding_for_passage(text) -> vector(384)
  • rag.openai_text_embedding_3_small(text) -> vector(1536)

• Generating embeddings for questions

  These functions generate embeddings for the questions.

  • rag_bge_small_en_v15.embedding_for_query(text) -> vector(384)
  • rag.openai_text_embedding_3_small(text) -> vector(1536)

• Reranking

  This function reranks chunks against the question using a small but best-in-class model that runs locally on your Postgres server.

  • rag_jina_reranker_v1_tiny_en.rerank_distance(text, text) -> real

• Calling out to chat models

  This function makes API calls to AI chat models such as ChatGPT to generate an answer using the question and the chunks together.

  • rag.openai_chat_completion(json) -> json

End-to-end RAG example

1. Create a docs table and ingest some PDF documents as text

drop table docs cascade;
create table docs
( id int primary key generated always as identity
, name text not null
, fulltext text not null
);

\set contents `base64 < /path/to/first.pdf`
insert into docs (name, fulltext)
values ('first.pdf', rag.text_from_pdf(decode(:'contents','base64')));

\set contents `base64 < /path/to/second.pdf`
insert into docs (name, fulltext)
values ('second.pdf', rag.text_from_pdf(decode(:'contents','base64')));

\set contents `base64 < /path/to/third.pdf`
insert into docs (name, fulltext)
values ('third.pdf', rag.text_from_pdf(decode(:'contents','base64'))));

2. Create an embeddings table, chunk the text, and generate embeddings for the chunks (performed locally)

drop table embeddings;
create table embeddings
( id int primary key generated always as identity
, doc_id int not null references docs(id)
, chunk text not null
, embedding vector(384) not null
);

create index on embeddings using hnsw (embedding vector_cosine_ops);

with chunks as (
  select id, unnest(rag_bge_small_en_v15.chunks_by_token_count(fulltext, 192, 8)) as chunk
  from docs
)
insert into embeddings (doc_id, chunk, embedding) (
  select id, chunk, rag_bge_small_en_v15.embedding_for_passage(chunk) from chunks
);

3. Query the embeddings and rerank the results (performed locally)

\set query 'what is [...]? how does it work?'

with ranked as (
  select
    id, doc_id, chunk, embedding <=> rag_bge_small_en_v15.embedding_for_query(:'query') as cosine_distance
  from embeddings
  order by cosine_distance
  limit 10
)
select *, rag_jina_reranker_v1_tiny_en.rerank_distance(:'query', chunk)
from ranked
order by rerank_distance;

4. Feed the query and top chunks to a remote AI chat model such as ChatGPT to complete the RAG pipeline

\set query 'what is [...]? how does it work?'

with ranked as (
  select
    id, doc_id, chunk, embedding <=> rag_bge_small_en_v15.embedding_for_query(:'query') as cosine_distance
  from embeddings
  order by cosine_distance
  limit 10
),
reranked as (
  select *, rag_jina_reranker_v1_tiny_en.rerank_distance(:'query', chunk)
  from ranked
  order by rerank_distance limit 5
)
select rag.openai_chat_completion(json_object(
  'model': 'gpt-4o-mini',
  'messages': json_array(
    json_object(
      'role': 'system',
      'content': E'The user is [...].\n\nTry to answer the user''s QUESTION using only the provided CONTEXT.\n\nThe CONTEXT represents extracts from [...] which have been selected as most relevant to this question.\n\nIf the context is not relevant or complete enough to confidently answer the question, your best response is: "I''m afraid I don''t have the information to answer that question".'
    ),
    json_object(
      'role': 'user',
      'content': E'# CONTEXT\n\n```\n' || string_agg(chunk, E'\n\n') || E'\n```\n\n# QUESTION\n\n```\n' || :'query' || E'```'
    )
  )
)) -> 'choices' -> 0 -> 'message' -> 'content' as answer
from reranked;