Build high-performance RAG using just PostgreSQL (Full Tutorial)

Key Concepts

PG Vector scale, Vector database, Postgress SQL, Time scale DB, Docker, Embeddings, RAG (Retrieval-Augmented Generation), Similarity search, Cosine similarity, Metadata filtering, Predicates, Time-based filtering, LLM (Large Language Model), Instructor, Structured output, Data Lumina, Data freelancer.

Docker Environment Setup

The video begins by outlining the process of setting up a high-performance RAG solution using PG Vector scale and Python. The first step involves setting up a Docker environment using a docker-compose.yml file. This file defines a service called "time scale DB" which uses the Time scale DB image with Postgress version 16. The container is named "time scale DB," and the default Postgress database settings are used (database name: postgress, user: postgress, password: password). A volume is created to persist data even after the container is stopped. The command docker-compose up -d is used to run the Docker container in detached mode. The Docker desktop application is used to validate that the container is running correctly on the default Postgress port.

Connecting to the Database

After setting up the Docker environment, the next step is to connect to the Postgress SQL database. The video demonstrates using Tableplus, a GUI client, to connect to the database. The connection settings are configured with the default values specified in the docker-compose.yml file (host: Local Host, port: default, user: postgress, password: password, DB: postgress). The connection is tested to ensure it is successful.

Inserting Vectors

The video then walks through the process of inserting vectors into the database. The insert_vectors.py script is used to vectorize an FAQ data set and insert it into the database. The script loads data from the data folder, which contains an FAQ document with questions, answers, and categories. The data is vectorized using an embedding model, and the resulting vectors are stored in the database.

Requirements and Configuration

To run the insert_vectors.py script, the following steps are required:

1. Install the required Python packages from requirements.txt using a virtual environment.
2. Create an .env file with the following variables:
   • OPENAI_API_KEY: Your Open AI API key.
   • TIMESCALE_SERVICE_URL: The URL of your Postgress SQL database instance (e.g., postgresql://postgress:password@localhost:5432/postgress).
3. Review the settings.py file in the config folder to adjust settings such as the Open AI model, embedding model, and embedding dimension.

Vectorization Process

The insert_vectors.py script performs the following steps:

1. Loads the FAQ data from the data folder.
2. Uses the prepare_data function to combine the question and answer columns into a single string.
3. Uses the VectorStore class to create embeddings for the data using the Open AI API.
4. Creates a pandas data frame with the following columns: ID, metadata, content, and embedding.

Database Operations

The VectorStore class provides functions for interacting with the database:

1. create_tables(): Creates the embedding table in the database using the create_tables function from the time scale Vector library.
2. create_index(): Creates an index on the embedding column to speed up similarity searches. The disn index is recommended.
3. upsert(): Inserts the vectorized data into the embedding table.

The video notes that the create_tables function uses the default settings from the time scale Vector library. Custom table structures can be created using plain SQL.

Similarity Search

The video demonstrates how to perform similarity searches using the similarity_search.py script. The script initializes the VectorStore class and uses the search function to retrieve relevant context for a given question.

Basic Similarity Search

The search function takes the following parameters:

• query: The search query.
• limit: The number of results to retrieve.

The search function uses cosine similarity to find the most similar vectors in the database. The lower the distance metric, the more similar the vectors are.

Synthesizing Responses

The video demonstrates using a synthesizer to generate answers based on the retrieved context. The synthesizer uses the instructor library to generate structured output, including a thought process, an answer, and a Boolean value indicating whether the synthesizer had enough context to answer the question.

Handling Irrelevant Questions

The video addresses the challenge of handling irrelevant questions. Even if a question is irrelevant, the search function will still return results. To address this, the synthesizer can be used to determine whether the retrieved context is relevant to the question. If the synthesizer determines that the context is not relevant, it can return a message indicating that it cannot answer the question.

Metadata Filtering

The video demonstrates how to use metadata filtering to refine search results. The search function can take a metadata_filter parameter, which is a dictionary specifying the metadata values to filter by. For example, the metadata_filter can be used to retrieve only results where the category is "shipping."

Advanced Filtering Using Predicates

The video demonstrates how to use predicates to perform advanced filtering. The search function can take a predicate parameter, which is a string specifying a SQL predicate. The predicate can be used to specify complex filtering conditions, such as combining multiple conditions using Boolean operators.

Time-Based Filtering

The video demonstrates how to use time-based filtering to retrieve data from a specific time range. The search function can take a time_range parameter, which is a tuple of datetime objects specifying the start and end dates.

Conclusion

The video concludes by summarizing the key takeaways:

• PG Factor scale is a powerful tool for building high-performance RAG solutions using Postgress SQL.
• The time scale Vector library provides a convenient way to interact with PG Factor scale from Python.
• Metadata filtering, predicates, and time-based filtering can be used to refine search results.
• The instructor library can be used to generate structured output from language models.
• Building robust RAG systems requires careful consideration of the entire pipeline, including the language models, embeddings, synthesizer, and search capabilities.