RAG
- Initializing a RAG table
  - vectorize.init_rag
- Query using RAG
  - vectorize.rag
  - Example

RAG

SQL API for Retrieval Augmented Generation projects.

Initializing a RAG table

Creates embeddings for specified data in a Postgres table. Creates index, and triggers to keep embeddings up to date.

`vectorize.init_rag`

vectorize.init_rag(
    "agent_name" TEXT,
    "table_name" TEXT,
    "unique_record_id" TEXT,
    "column" TEXT,
    "schema" TEXT DEFAULT 'public',
    "transformer" TEXT DEFAULT 'text-embedding-ada-002',
    "search_alg" vectorize.SimilarityAlg DEFAULT 'pgv_cosine_similarity',
    "table_method" vectorize.TableMethod DEFAULT 'append'
) RETURNS TEXT

Parameters:

Example:

select vectorize.init_rag(
    agent_name => 'tembo_chat',
    table_name => 'tembo_docs',
    unique_record_id => 'document_name',
    "column" => 'content',
    transformer => 'sentence-transformers/all-MiniLM-L12-v2'
);

Query using RAG

`vectorize.rag`

vectorize."rag"(
    "agent_name" TEXT,
    "query" TEXT,
    "chat_model" TEXT DEFAULT 'gpt-3.5-turbo',
    "task" TEXT DEFAULT 'question_answer',
    "api_key" TEXT DEFAULT NULL,
    "num_context" INT DEFAULT 2,
    "force_trim" bool DEFAULT false
) RETURNS TABLE (
    "chat_results" jsonb
)

Parameters:

Example

select vectorize.rag(
    agent_name => 'tembo_support',
    query => 'what are the major features from the tembo kubernetes operator?',
    chat_model => 'gpt-3.5-turbo',
    force_trim => 'true'
);

The response contains the contextual data used in the prompt in addition to the chat response.

{
  "context": [
    {
      "content": "\"Tembo Standard Stack\\n\\nThe Tembo Standard Stack is a tuned Postgres instance balance for general purpose computing. You have full control over compute, configuration, and extension installation.\"",
      "token_ct": 37,
      "record_id": "535"
    },
    {
      "content": "\"Why Stacks?\\n\\nAdopting a new database adds significant complexity and costs to an engineering organization. Organizations spend a huge amount of time evaluating, benchmarking or migrating databases and setting upcomplicated pipelines keeping those databases in sync.\\n\\nMost of these use cases can be served by Postgres, thanks to its stability, feature completeness and extensibility. However, optimizing Postgres for each use case is a non-trivial task and requires domain expertise, use case understanding and deep Postgres expertise, making it hard for most developers to adopt this.\\n\\nTembo Stacks solve that problem by providing pre-built, use case optimized Postgres deployments.\\n\\nA tembo stack is a pre-built, use case specific Postgres deployment which enables you to quickly deploy specialized data services that can replace external, non-Postgres data services. They help you avoid the pains associated with adopting, operationalizing, optimizing and managing new databases.\\n\\n|Name|Replacement for|\\n|----|---------------|\\n|Data Warehouse| Snowflake, Bigquery |\\n|Geospatial| ESRI, Oracle |\\n|OLTP| Amazon RDS |\\n|OLAP| Snowflake, Bigquery |\\n|Machine Learning| MindsDB |\\n|Message Queue| Amazon SQS, RabbitMQ, Redis |\\n|Mongo Alternative on Postgres| MongoDB |\\n|RAG| LangChain |\\n|Standard| Amazon RDS |\\n|Vector DB| Pinecone, Weaviate |\\n\\nWe are actively working on additional Stacks. Check out the Tembo Roadmap and upvote the stacks you''d like to see next.\"",
      "token_ct": 336,
      "record_id": "387"
    }
  ],
  "chat_response": "Tembo Stacks are pre-built, use case specific Postgres deployments that are optimized for various data services such as Data Warehouse, Geospatial, OLTP, OLAP, Machine Learning, Message Queue, and more. These Stacks aim to provide organizations with specialized data services that can replace external non-Postgres data services. Each Tembo Stack is designed to cater to specific use cases, enabling developers to quickly deploy and utilize Postgres instances tailored to their needs without the complexity of setting up and optimizing Postgres manually."
}

Filter the results to just the chat_response:

select vectorize.rag(
    agent_name => 'tembo_support',
    query => 'what are the major features from the tembo kubernetes operator?',
    chat_model => 'gpt-3.5-turbo',
    force_trim => 'true'
) -> 'chat_response';

 "Tembo Stacks are pre-built, use case specific Postgres deployments that are optimized for various data services such as Data Warehouse, Geospatial, OLTP, OLAP, Machine Learning, Message Queue, and more. These Stacks aim to provide organizations with specialized data services that can replace external non-Postgres data services. Each Tembo Stack is designed to cater to specific use cases, enabling developers to quickly deploy and utilize Postgres instances tailored to their needs without the complexity of setting up and optimizing Postgres manually."

PGXN

PostgreSQL Extension Network

Contents

RAG

Initializing a RAG table

`vectorize.init_rag`

Query using RAG

`vectorize.rag`

Example