Harnessing Semantic Search in SQL Databases with Langchain and PGVector

By GptWriter

587 words

Harnessing Semantic Search in SQL Databases with Langchain and PGVector

In the ever-evolving world of data management, the ability to search and retrieve information based on semantic meaning rather than just keywords or exact matches is a game-changer. This is where the combination of Langchain and PGVector comes into play, offering a powerful way to perform semantic searches within SQL databases. In this blog, we’ll explore how to incorporate semantic similarity in tabular databases, a technique that can significantly enhance the way we interact with data.

The Workflow: From Embeddings to SQL Queries

Generating and Storing Semantic Embeddings

The process begins with generating embeddings for a specific column in our database. These embeddings are vector representations that capture the semantic meaning of the text. Here’s how we do it:

  1. Generating Embeddings: We use Langchain to create embeddings for each entry in our target column, such as song titles.
  2. Storing Embeddings: These embeddings are then stored in a new column or a separate table, depending on the data’s cardinality.

Querying with PGVector

With the PGVector extension, we can perform SQL queries using various distance and similarity measures:

  • L2 distance (<->)
  • Cosine distance (<=>)
  • Inner product (<#>)

This allows us to run standard SQL queries that consider the semantic meaning of the data.

Requirements

To implement this, we need a PostgreSQL database with the pgvector extension enabled. For demonstration purposes, we’ll use a Chinook database on a local PostgreSQL server.

Embedding the Song Titles: A Practical Example

Let’s take a closer look at how we can apply this to song titles:

  1. Adding a New Column: We alter our “Track” table to include a column for embeddings.
  2. Generating and Storing Embeddings: Using Langchain’s OpenAIEmbeddings, we generate embeddings for each song title and store them in our database.

Semantic Search in Action

To test our semantic search, we can run a query like this:

SELECT "Track"."Name" FROM "Track"
WHERE "Track"."embeddings" IS NOT NULL
ORDER BY "embeddings" <-> [search_vector] LIMIT 5

This query retrieves the top 5 song titles that are semantically closest to the concept of “hope about the future.”

Creating the SQL Chain

We define functions to interact with the database and build prompts using Langchain’s Expression Language. This allows us to create a chain that generates and executes SQL queries based on semantic meaning.

Using the Chain: Advanced Query Examples

Example 1: Genre-Based Filtering

Imagine we want to find rock songs that convey a “deep feeling of despair.” We can combine semantic search with traditional SQL filtering to achieve this.

Example 2: Album Insights

We can also discover albums with the most songs in the top 150 saddest songs list, a task that would be complex without hybrid querying.

Example 3: Dual Semantic Filters

An exciting aspect of this approach is the ability to combine two semantic searches. For instance, we can find sad songs from albums with “lovely” titles, which would be impossible with standard metadata filtering alone.

The integration of Langchain and PGVector opens up a new realm of possibilities for semantic search within SQL databases. By combining the power of embeddings with traditional SQL querying, we can uncover insights that were previously hidden or difficult to extract. This approach not only enhances data retrieval but also paves the way for more intuitive and meaningful interactions with our databases.

Ready to revolutionize your data search capabilities? Start by incorporating semantic embeddings into your SQL databases and experience the power of semantic search firsthand.