Server Configuration¶

You must configure an embedding model in the Server configuration. This defines the connection to the LLM or embedding service that the function EMBED_AI() uses during execution.

• When a query requires converting a string into a vector on-the-fly, the Virtual DataPort (VDP) server uses this configuration to call the external API.

Base View Configuration¶

When you introspect a table containing vector data (e.g., a PostgreSQL table with a vector column), you should configure the following in the Design Studio:

1. Source Type Property: Explicitly set which model was used to populate the column.

2. Column Description: Provide a meaningful description that details the information used to generate the vector (e.g., “Embedding generated from the customer review summary”).

To specify the Embedding model source type property of a vector column:

1. Open the Base View and go to the Edit tab.

2. Navigate to the column that holds the vector data.

3. Set the Source Type Property “Embedding Model”.

4. Save the changes.

Benefits of this Configuration

• Automated Vector Generation: It enables the VDP server to automatically identify the required model during query execution. When you run a query comparing text to this column, VDP identifies the model needed to vectorize your input text, eliminating the need to manually specify it in every query.

• Governance and Documentation: It serves as a metadata record, documenting exactly which model and version were used to generate the vectors (e.g., openai-text-embedding-3-small vs cohere-embed-english-v3.0). This prevents version mismatches and ensures developers know how to interact with the data.

• AI-Powered Features: The column descriptions allow the the Denodo Assistant for VQL Shell, the Denodo AI SDK, and the Denodo Assistant Chatbot tools to generate more accurate queries by understanding the semantic context of the vector data.

Basic Semantic Search (Ranking)¶

The following query searches for tickets related to “internet connectivity issues.”

SELECT
    ticket_id,
    ticket_description,
    vector_distance(description_vector, 'My laptop keeps losing connection to the Wi-Fi') AS distance_score
FROM
    support_tickets
ORDER BY
    distance_score
LIMIT 3;

Results:

Notice that the system returns results about “Wireless” and “Network” even though the word “Wi-Fi” might not be present in every row.

Hybrid Search (Semantic + Structured Filter)¶

Hybrid search combines semantic understanding with traditional structured filtering. This is useful when you need to find semantically relevant results that are strictly limited to a specific category, date range, or status.

In this example, we search for issues semantically similar to “login failure,” but we strictly filter to show only tickets marked as “Critical” priority.

SELECT
    ticket_id,
    ticket_description,
    priority,
    vector_distance(description_vector, 'Users cannot log into the portal') AS distance_score
FROM
    support_tickets
WHERE
    priority = 'Critical'
    AND vector_distance(description_vector, 'Users cannot log into the portal') < 0.4
ORDER BY
    distance_score;

Results:

The results exclude common password resets (Low priority) and focus only on critical outages, even if the phrasing varies (e.g., “SSO”, “Auth”).

Recommendation System (“More Like This”)¶

You can use semantic search to build a recommendation engine or a “find similar” feature. Instead of comparing a user’s text input to the database, you compare the vector of an existing record to the vectors of all other records using a distance function such as VECTOR_COSINE_DISTANCE().

In this example, we identify a specific ticket (ID 1042) regarding a complex server crash. We then search for other tickets in the system that describe a similar problem to identify if this is a recurring pattern.

SELECT
    t.ticket_id,
    t.ticket_description,
    vector_cosine_distance(t.description_vector, source.description_vector) AS distance_score
FROM
    support_tickets t,
    (SELECT description_vector FROM support_tickets WHERE ticket_id = 1042) source
WHERE
    t.ticket_id <> 1042 -- Exclude the source ticket itself
    AND vector_cosine_distance(t.description_vector, source.description_vector) < 0.2
ORDER BY
    distance_score;

Results:

Assuming Ticket 1042 was “Server crash due to memory leak,” the query returns other tickets describing memory issues.