Knowledge Bases (KBs) organize data across data sources, including databases, files, documents, webpages, enabling efficient search capabilities. Here is what happens to data when it is inserted into the knowledge base.

Upon inserting data into the knowledge base, it is split into chunks, transformed into the embedding representation to enhance the search capabilities, and stored in a vector database.
Learn more about features of knowledge bases available via SQL API.

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insert() Function

Here is the syntax for inserting data into a knowledge base:
  • Inserting raw data: 
    my_kb.insert([
    {'type': 'apartment', 'price': 100000},
    {'type': 'villa', 'price': 500000}
])
  • Inserting data from data sources connected to MindsDB: 
    my_kb.insert_query(
    server.databases.my_database.tables.my_table.filter(type='my_type')
)
  • Inserting data from files uploaded to MindsDB: 
    my_kb.insert_files(['my_pdf_file', 'my_txt_file'])
  • Inserting data from webpages: 
    kb.insert_webpages(
    ['https://example.com'],
    crawl_depth=2,
    filters=[r'.*\/blog\/.*'],
    limit=10
)
    Where:
    • urls: Base URLs to crawl.
    • crawl_depth: Depth for recursive crawling. Default is 1.
    • filters: Regex patterns to include.
    • limit: Max number of pages.
Upon execution, it inserts data into a knowledge base, using the embedding model to embed it into vectors before inserting into an underlying vector database.
The status of the insert operations is logged in the information_schema.queries table with the timestamp when it was ran.
Handling duplicate data while inserting into the knowledge baseKnowledge bases uniquely identify data rows using an ID column, which prevents from inserting duplicate data, as follows.
  • Case 1: Inserting data into the knowledge base without the id_column defined. When users do not define the id_column during the creation of a knowledge base, MindsDB generates the ID for each row using a hash of the content columns, as explained here. Example: If two rows have exactly the same content in the content columns, their hash (and thus their generated ID) will be the same. Note that duplicate rows are skipped and not inserted. Since both rows in the below table have the same content, only one row will be inserted.
    nameage
    Alice25
    Alice25
  • Case 2: Inserting data into the knowledge base with the id_column defined. When users define the id_column during the creation of a knowledge base, then the knowledge base uses that column’s values as the row ID. Example: If the id_column has duplicate values, the knowledge base skips the duplicate row(s) during the insert. The second row in the below table has the same id as the first row, so only one of these rows is inserted.
    idnameage
    1Alice25
    1Bob30
Best practiceEnsure the id_column uniquely identifies each row to avoid unintentional data loss due to duplicate ID skipping.

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Update Existing Data

In order to update existing data in the knowledge base, insert data with the column ID that you want to update and the updated content. Here is an example of usage. A knowledge base stores the following data. 
+----------+-------------------+------------------------+
| order_id | product           | notes                  |
+----------+-------------------+------------------------+
| A1B      | Wireless Mouse    | Request color: black   |
| 3XZ      | Bluetooth Speaker | Gift wrap requested    |
| Q7P      | Laptop Stand      | Prefer aluminum finish |
+----------+-------------------+------------------------+
A user updated Laptop Stand to Aluminum Laptop Stand. 
+----------+-----------------------+------------------------+
| order_id | product               | notes                  |
+----------+-----------------------+------------------------+
| A1B      | Wireless Mouse        | Request color: black   |
| 3XZ      | Bluetooth Speaker     | Gift wrap requested    |
| Q7P      | Aluminum Laptop Stand | Prefer aluminum finish |
+----------+-----------------------+------------------------+
Go to the Complete Example section below to find out how to access this sample data.
Here is how to propagate this change into the knowledge base. 
my_kb.insert_query(
    server.databases.sample_data.tables.orders.filter(order_id='Q7P')
)
The knowledge base matches the ID value to the existing one and updates the data if required.

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Insert Data using Partitions

In order to optimize the performance of data insertion into the knowledge base, users can set up partitions and threads to insert batches of data in parallel. This also enables tracking the progress of data insertion process including cancelling and resuming it if required. Here is an example. 
project.query(
    '''
    INSERT INTO my_kb
    SELECT order_id, product, notes
    FROM sample_data.orders
    USING
        batch_size = 200,
        track_column = order_id,
        threads = 10,
        error = 'skip';
    '''
)
The parameters include the following:
  • batch_size defines the number of rows fetched per iteration to optimize data extraction from the source. It defaults to 1000.
  • threads defines threads for running partitions. Note that if the ML task queue is enabled, threads are used automatically. The available values for threads are:
    • a number of threads to be used, for example, threads = 10,
    • a boolean value that defines whether to enable threads, setting threads = true, or disable threads, setting threads = false.
  • track_column defines the column used for sorting data before partitioning.
  • error defines the error processing options. The available values include raise, used to raise errors as they come, or skip, used to subside errors. It defaults to raise if not provided.
After executing the INSERT INTO statement with the above parameters, users can view the data insertion progress by querying the information_schema.queries table. 
project.query(
    '''
    SELECT * FROM information_schema.queries;
    '''
)
Users can cancel the data insertion process using the process ID from the information_schema.queries table. 
project.query(
    '''
    SELECT query_cancel(1);
    '''
)
If you want to cancel the data insertion process, look up the process ID value from the information_schema.queries table and pass it as an argument to the query_cancel() function. Note that canceling the query will not remove the already inserted data. Users can resume the data insertion process using the process ID from the information_schema.queries table. 
project.query(
    '''
    SELECT query_resume(1);
    '''
)
If you want to resume the data insertion process (which may have been interrupted by an error or cancelled by a user), look up the process ID value from the information_schema.queries table and pass it as an argument to the query_resume() function. Note that resuming the query will not remove the already inserted data and will start appending the remaining data.

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Chunking Data

Upon inserting data into the knowledge base, the data chunking is performed in order to optimize the storage and search of data. Each chunk is identified by its chunk ID of the following format: <id>:<chunk_number>of<total_chunks>:<start_char_number>to<end_char_number>.

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Text

Users can opt for defining the chunking parameters when creating a knowledge base. 
my_kb = project.knowledge_bases.create(
    ...
    params={
        "preprocessing": {
            "text_chunking_config" : {
                "chunk_size": 2000,
                "chunk_overlap": 200
            }
        }
    }
)
The chunk_size parameter defines the size of the chunk as the number of characters. And the chunk_overlap parameter defines the number of characters that should overlap between subsequent chunks.

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JSON

Users can opt for defining the chunking parameters specifically for JSON data. 
my_kb = project.knowledge_bases.create(
    ...
    params={
        "preprocessing": {
            "type": "json_chunking",
            "json_chunking_config" : {
                ...
            }
        }
    }
)
When the type of chunking is set to json_chunking, users can configure it by setting the following parameter values in the json_chunking_config parameter:
  • flatten_nested
    It is of the bool data type with the default value of True.
    It defines whether to flatten nested JSON structures.
  • include_metadata
    It is of the bool data type with the default value of True.
    It defines whether to include original metadata in chunks.
  • chunk_by_object
    It is of the bool data type with the default value of True.
    It defines whether to chunk by top-level objects (True) or create a single document (False).
  • exclude_fields
    It is of the List[str] data type with the default value of an empty list.
    It defines the list of fields to exclude from chunking.
  • include_fields
    It is of the List[str] data type with the default value of an empty list.
    It defines the list of fields to include in chunking (if empty, all fields except excluded ones are included).
  • metadata_fields
    It is of the List[str] data type with the default value of an empty list.
    It defines the list of fields to extract into metadata for filtering (can include nested fields using dot notation). If empty, all primitive fields will be extracted (top-level fields if available, otherwise all primitive fields in the flattened structure).
  • extract_all_primitives
    It is of the bool data type with the default value of False.
    It defines whether to extract all primitive values (strings, numbers, booleans) into metadata.
  • nested_delimiter
    It is of the str data type with the default value of ".".
    It defines the delimiter for flattened nested field names.
  • content_column
    It is of the str data type with the default value of "content".
    It defines the name of the content column for chunk ID generation.

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Underlying Vector Store

Each knowledge base has its underlying vector store that stores data inserted into the knowledge base in the form of embeddings. Users can query the underlying vector store as follows.
  • KB with the default ChromaDB vector store:
project.query(
    '''
    SELECT id, content, metadata, embeddings
    FROM <kb_name>_chromadb.storage_table;
    '''
)

project.query(
    '''
    SELECT id, content, metadata, embeddings
    FROM <vector_store_connection_name>.<table_name>;
    '''
)