Semantic Operators: Run LLM Queries Directly in SQL

You have 50,000 support tickets in a database table. You need to classify each one by intent — billing issue, feature request, bug report, churn risk. The traditional approach: export the data, write a Python script to call an LLM API in a loop, handle rate limits and retries, parse the responses, then load the results back into your database. Three systems, two data movements, and a pipeline you'll have to maintain forever.

Semantic operators eliminate all of that. They let you call LLM reasoning directly from SQL — as a native database function. Classification, summarization, extraction, and generation happen inside the query engine, right where the data already lives.

This isn't a wrapper around an API. It's a database extension that makes LLM calls a first-class SQL operation, the same way `COUNT()` or `SUM()` are native functions you don't think twice about using.

What Are Semantic Operators?

Semantic operators are database extensions that integrate LLM providers (OpenAI, Amazon Bedrock Claude, or any OpenAI-compatible API) directly into the SQL execution engine. You enable the extension, configure your provider, and then use LLM-powered functions in SELECT statements, WHERE clauses, and INSERT pipelines.

Setup is minimal:

```sql -- Enable the extension CREATE EXTENSION llm; -- Configure provider SET llm.provider = 'openai'; SET llm.model = 'gpt-4o'; SET openai.api_key = 'sk-...'; ```

Once configured, you can invoke LLM reasoning anywhere you'd normally write a SQL expression. The database handles the API calls, batching, and result parsing internally — you just write queries.

This approach is fundamentally different from the export-process-reimport pattern that most teams use today. The data never leaves the database. There's no intermediate CSV, no Python glue code, no separate orchestration layer. The context lake becomes the place where data is both stored and enriched.

Text Classification in a Single Query

The most common use case for semantic operators is classifying unstructured text at scale. Instead of building a classification pipeline, you write a query:

```sql SELECT ticket_id, subject, llm_classify( message, ARRAY['billing', 'bug_report', 'feature_request', 'churn_risk', 'general'] ) AS category FROM support_tickets WHERE created_at > NOW() - INTERVAL '24 hours'; ```

This classifies every support ticket from the last 24 hours into one of five categories — directly in the query result set. No export, no script, no reimport. You can wrap this in a materialized view to keep classifications fresh as new tickets arrive.

The power here isn't just convenience. It's that classification now lives in the same layer as your structured data. You can immediately join classified tickets against customer records, filter by category in downstream queries, or feed the results into a feature store for agent decision-making.

Summarization Without a Pipeline

Long-form content — support conversations, product reviews, incident logs — often needs to be summarized before it's useful for analytics or AI agents. Traditionally, this requires a batch job that reads text, calls an LLM, and writes summaries back. With semantic operators, it's a query:

```sql INSERT INTO review_summaries (product_id, summary, sentiment) SELECT product_id, llm_summarize(review_text, 'Summarize in 2 sentences'), llm_classify(review_text, ARRAY['positive', 'negative', 'neutral']) FROM product_reviews WHERE processed = false; ```

This reads unprocessed reviews, generates a two-sentence summary and a sentiment classification for each, and writes the results directly into a summaries table. The entire enrichment pipeline is a single SQL statement.

For teams building real-time context layers, this means enrichment happens at the data layer — not in a separate service that introduces latency and freshness gaps. The summary is available the moment the source data is available.

Semantic Extraction: Structure from Unstructured

Unstructured data is only useful when you can query it. Semantic operators let you extract structured fields from free text — turning messy input into clean, queryable columns:

```sql SELECT log_id, raw_message, llm_extract(raw_message, 'error_code') AS error_code, llm_extract(raw_message, 'affected_component') AS component, llm_extract(raw_message, 'severity: low/medium/high/critical') AS severity FROM application_logs WHERE level = 'ERROR' AND timestamp > NOW() - INTERVAL '1 hour'; ```

This extracts error codes, affected components, and severity levels from raw log messages — fields that exist in the text but aren't in the schema. The LLM interprets natural language and returns structured values you can filter, group, and aggregate with standard SQL.

This is especially powerful for incident response. Instead of grep-and-eyeball, your on-call team can query structured error data that was unstructured seconds ago.

Intelligent Defaults and Data Enrichment

Missing data is a constant problem. Incomplete forms, optional fields left blank, imported records with gaps. Semantic operators can fill these intelligently based on the context of surrounding fields:

```sql UPDATE products SET description = llm_generate( 'Write a 1-sentence product description based on: ' || 'name=' || name || ', category=' || category || ', price=' || price::text ) WHERE description IS NULL; ```

This generates product descriptions for every product that's missing one, using the existing structured fields as context. The LLM reasons over the product name, category, and price to produce a relevant description — no manual writing, no separate enrichment service.

You can apply the same pattern to generate tags, infer categories, normalize messy input, or populate any field that can be reasonably derived from the data that's already there.

Semantic Operators vs Vector Search

Both semantic operators and vector search work with meaning rather than exact matches, but they solve different problems:

When to Use Semantic Operators (And When Not To)

Use semantic operators when:

You need to classify, summarize, extract, or generate data and the results should live in the same database as the source. When the alternative is a Python script that exports data, calls an API, and reimports results — semantic operators eliminate that entire workflow.

Be cautious when:

You're processing millions of rows. Each semantic operator call invokes an LLM API, which means per-token costs and rate limits. For high-volume classification, consider using semantic operators to label a training set, then train a lightweight model for bulk inference. Use semantic operators for the long tail — the complex cases that need real reasoning.

Don't use semantic operators for:

Simple pattern matching or keyword search. If a regex or full-text search index solves the problem, it's faster and cheaper. Semantic operators are for tasks that genuinely require language understanding — ambiguous classifications, nuanced summarization, context-dependent extraction.

Why In-Database AI Matters

The deeper implication of semantic operators is architectural. They collapse the gap between "where data lives" and "where AI reasoning happens."

In the traditional stack, data sits in a database, gets exported to a processing layer (Python, Spark, Airflow), passes through an LLM API, and the results flow back into the database — or worse, into a separate store. Every hop adds latency, introduces freshness decay, and creates another system to maintain.

Semantic operators eliminate the middle layer. The database becomes the AI reasoning engine. This is the context lake philosophy: all data, all reasoning, one system. Your data doesn't move to the AI — the AI comes to the data.

For teams building multi-agent systems, this means agents can query semantically enriched data through standard SQL without needing separate embedding pipelines, classification services, or enrichment jobs. The context lake handles storage, search, and semantic reasoning in a single layer.

Getting Started

Semantic operators are available as a Tacnode extension. To get started:

1. Enable the `llm` extension with `CREATE EXTENSION llm`

2. Configure your LLM provider (OpenAI, Bedrock Claude, or any OpenAI-compatible endpoint)

3. Start with a small classification task — take a table with unstructured text and classify it into categories

4. Once you're comfortable, explore summarization and extraction on more complex data

The key mental shift is treating LLM reasoning as a SQL function rather than an external service. Once that clicks, you'll find use cases everywhere — any column that could benefit from AI interpretation is a candidate for a semantic operator.