A knowledge base is an advanced system that organizes information based on semantic meaning rather than simple keyword matching. It integrates embedding models, reranking models, and vector stores to enable context-aware data retrieval. By performing semantic reasoning across multiple data points, a knowledge base delivers deeper insights and more accurate responses, making it a powerful tool for intelligent data access. Before diving into the syntax, here is a quick walkthrough showing how knowledge bases work in MindsDB. We start by creating a knowledge base and inserting data. Next we can run semantic search queries with metadata filtering.
1

Create a knowledge base

Use the CREATE KNOWLEDGE_BASE command to create a knowledge base, specifying all its components.
CREATE KNOWLEDGE_BASE my_kb
USING
    embedding_model = {
        "provider": "openai",
        "model_name" : "text-embedding-3-large",
        "api_key": "sk-abc123"
    },
    reranking_model = {
        "provider": "openai",
        "model_name": "gpt-4o",
        "api_key": "sk-abc123"
    },
    metadata_columns = ['product'],
    content_columns = ['notes'],
    id_column = 'order_id';
2

Insert data into the knowledge base

In this example, we use a simple dataset containing customer notes for product orders which will be inserted into the knowledge base.
+----------+-----------------------+------------------------+
| order_id | product               | notes                  |
+----------+-----------------------+------------------------+
| A1B      | Wireless Mouse        | Request color: black   |
| 3XZ      | Bluetooth Speaker     | Gift wrap requested    |
| Q7P      | Aluminum Laptop Stand | Prefer aluminum finish |
+----------+-----------------------+------------------------+
Use the INSERT INTO command to ingest data into the knowledge base.
INSERT INTO my_kb
    SELECT order_id, product, notes
    FROM sample_data.orders;
3

Run semantic search on the knowledge base

Query the knowledge base using semantic search.
SELECT *
FROM my_kb
WHERE content = 'color'
This query returns:
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
| id  | chunk_id             | chunk_content           | metadata                                                                                                                                                                                            | distance           | relevance          |
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
| A1B | A1B_notes:1of1:0to20 | Request color: black    | {"chunk_index":0,"content_column":"notes","end_char":20,"original_doc_id":"A1B_notes","original_row_id":"A1B","product":"Wireless Mouse","source":"TextChunkingPreprocessor","start_char":0}        | 0.5743341242061104 | 0.5093188026135379 |
| Q7P | Q7P_notes:1of1:0to22 | Prefer aluminum finish  | {"chunk_index":0,"content_column":"notes","end_char":22,"original_doc_id":"Q7P_notes","original_row_id":"Q7P","product":"Aluminum Laptop Stand","source":"TextChunkingPreprocessor","start_char":0} | 0.7744703514692067 | 0.2502580835880018 |
| 3XZ | 3XZ_notes:1of1:0to19 | Gift wrap requested     | {"chunk_index":0,"content_column":"notes","end_char":19,"original_doc_id":"3XZ_notes","original_row_id":"3XZ","product":"Bluetooth Speaker","source":"TextChunkingPreprocessor","start_char":0}     | 0.8010851611432231 | 0.2500003885558766 |
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
4

Get the most relevant search results

Query the knowledge base using semantic search and define the relevance parameter to receive only the best matching data for your use case.
SELECT *
FROM my_kb
WHERE content = 'color'
AND relevance >= 0.2502;
This query returns:
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
| id  | chunk_id             | chunk_content           | metadata                                                                                                                                                                                            | distance           | relevance          |
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
| A1B | A1B_notes:1of1:0to20 | Request color: black    | {"chunk_index":0,"content_column":"notes","end_char":20,"original_doc_id":"A1B_notes","original_row_id":"A1B","product":"Wireless Mouse","source":"TextChunkingPreprocessor","start_char":0}        | 0.5743341242061104 | 0.5093188026135379 |
| Q7P | Q7P_notes:1of1:0to22 | Prefer aluminum finish  | {"chunk_index":0,"content_column":"notes","end_char":22,"original_doc_id":"Q7P_notes","original_row_id":"Q7P","product":"Aluminum Laptop Stand","source":"TextChunkingPreprocessor","start_char":0} | 0.7744703514692067 | 0.2502580835880018 |
+-----+----------------------+-------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+--------------------+
5

Filter results by metadata

Add metadata filtering to focus your search.
SELECT *
FROM my_kb
WHERE product = 'Wireless Mouse'
AND content = 'color'
AND relevance >= 0.2502;
This query returns:
+-----+----------------------+------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+-------------------+
| id  | chunk_id             | chunk_content          | metadata                                                                                                                                                                                     | distance           | relevance         |
+-----+----------------------+------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+-------------------+
| A1B | A1B_notes:1of1:0to20 | Request color: black   | {"chunk_index":0,"content_column":"notes","end_char":20,"original_doc_id":"A1B_notes","original_row_id":"A1B","product":"Wireless Mouse","source":"TextChunkingPreprocessor","start_char":0} | 0.5743341242061104 | 0.504396172197583 |
+-----+----------------------+------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------+-------------------+
The following sections explain the syntax and other features of knowledge bases.