Snowflake Cortex Vector Search¶

Vector Data Type¶

With the VECTOR data type, Snowflake encodes and processes vectors efficiently. This new type supports semantic vector search and retrieval applications, such as for large language models (LLMs), and common operations on vectors in vector processing applications.

A value of type VECTOR is defined by two parameters:

• The type of the elements (either 32-bit integers or 32-bit floating point numbers).

• The dimension (length) of the vector.

VECTOR({INT | FLOAT}, <dimension>)

-- valid
VECTOR(FLOAT, 256) -- a vector of 256 32-bit float values
VECTOR(INT, 16) -- a vector of 16 32-bit integer values

-- invalid
VECTOR(STRING, 256) -- not a valid type
VECTOR(INT, -1) -- not a valid vector size

Creating an Embedding¶

To create an embedding from a piece of text, use the EMBED_TEXT function. This function returns the vector embeddings for a given English-language text. This vector can be used with the vector comparison functions to determine the semantic similarity of two documents.

SNOWFLAKE.CORTEX.EMBED_TEXT(model, text) -- returns VECTOR

• model: The language model to be used to generate the embeddings. Currently this must be ‚e5-base-v2‘.

• text: The text for which an embedding should be calculated.

Vector Search and Distance Functions¶

The calculation of distances between vectors is a fundamental operation in semantic comparison. For example, you need this operation to find the top K closest vectors to a query vector, which can be used for a semantic search. Vector search also enables developers to improve the accuracy of their generative AI responses by providing related documents to a large language model.

Snowflake Cortex provides three vector distance functions: inner product, L2 distance, and cosine similarity. Each function takes two vector arguments of equal element type and dimension and computes the specified metric over them.

VECTOR_INNER_PRODUCT(<vector>, <vector>)

VECTOR_L2_DISTANCE(<vector>, <vector>)

VECTOR_COSINE_DISTANCE(<vector>, <vector>)

Examples¶

CREATE TABLE vectors (a VECTOR(float, 3), b VECTOR(float, 3));
INSERT INTO vectors SELECT [1.1,2.2,3]::VECTOR(FLOAT,3), [1,1,1]::VECTOR(FLOAT,3);
INSERT INTO vectors SELECT [1,2.2,3]::VECTOR(FLOAT,3), [4,6,8]::VECTOR(FLOAT,3);

-- Compute the pairwise inner product between columns a and b
SELECT VECTOR_INNER_PRODUCT(a, b) FROM vectors;

+------+
| 6.3  |
|------|
| 41.2 |
+------+

-- Find the closest vector to [1,2,3]
SELECT a, VECTOR_COSINE_DISTANCE(a, [1,2,3]::VECTOR(FLOAT, 3)) AS similarity
    FROM vectors
ORDER BY similarity DESC
LIMIT 1;

+-------------------------+
| [1, 2.2, 3] | 0.9990... |
+-------------------------+

import snowflake.connector

conn = ... # Set up connection
cur = conn.cursor()

cur.execute("CREATE or replace TABLE vectors (a VECTOR(float, 3), b VECTOR(float, 3))")
values = [([1.1, 2.2, 3], [1, 1, 1]), ([1, 2.2, 3], [4, 6, 8])]
for row in values:
        cur.execute(f"""
            INSERT INTO vectors(a, b)
                SELECT {row[0]}::VECTOR(float,3), {row[1]}::VECTOR(FLOAT,3)
        """)

# Compute the pairwise inner product between columns a and b
cur.execute("SELECT VECTOR_INNER_PRODUCT(a, b) FROM vectors")
print(cur.fetchall())

[(6.30...,), (41.2...,)]

# Find the closest vector to [1,2,3]
cur.execute(f"""
    SELECT a, VECTOR_COSINE_DISTANCE(a, {[1,2,3]}::VECTOR(FLOAT, 3))
        AS similarity
        FROM vectors
        ORDER BY similarity DESC
        LIMIT 1;
""")
print(cur.fetchall())

[([1.0, 2.2..., 3.0], 0.9990...)]

from snowflake.snowpark import Session, Row
session = ... # Set up session
from snowflake.snowpark.types import VectorType, StructType, StructField
from snowflake.snowpark.functions import col, lit, vector_l2_distance
schema = StructType([StructField("vec", VectorType(int, 3))])
data = [Row([1, 2, 3]), Row([4, 5, 6]), Row([7, 8, 9])]
df = session.create_dataframe(data, schema)
df.select(
    "vec",
    vector_l2_distance(df.vec, lit([1, 2, 2]).cast(VectorType(int, 3))).as_("dist"),
).sort("dist").limit(1).show()

----------------------
|"VEC"      |"DIST"  |
----------------------
|[1, 2, 3]  |1.0     |
----------------------

Vector Search¶

To implement a search for semantically similar documents, first store the embeddings for the documents to be searched. (Keep the embeddings up to date when documents are added or edited.)

In this example, the documents are call center issues logged by support representatives. The issue is stored in a column called issue_text in the table issues. The following SQL creates a new vector column to hold the embeddings of the issues.

ALTER TABLE issues ADD COLUMN issue_vec VECTOR(FLOAT, 768);

UPDATE TABLE issues
    SET issue_vec = SNOWFLAKE.CORTEX.EMBED_TEXT('e5-base-v2', issue_text);

To perform a search, create an embedding of the search term or target document, then use a vector distance function to locate documents with similar embeddings. Use ORDER BY and LIMIT clauses to select the top k matching documents, and optionally use a WHERE condition to specify a minimum similarity.

Generally, the call to the vector distance function should appear in the SELECT clause, not in the WHERE clause. This way, the function is called only for the rows specified by the WHERE clause, which may restrict the query based on some other criteria, instead of operating over all rows in the table. To test a similarity value in the WHERE clause, define a column alias for the VECTOR_SIMILARITY call in the SELECT clause, and use that alias in a condition in the WHERE clause.

This example finds up to five issues matching the search term from the last 90 days, assuming the cosine similarity with the search term is at least 0.7.

SET search_term = "User could not install Facebook app on his phone"
SET search_wec = SNOWFLAKE.VECTOR.EMBED_TEXT('e5-base-v2', $search_term)

SELECT
    issue,
    SNOWFLAKE.CORTEX.VECTOR_COSINE_DISTANCE(issue_vec, $search_vec) AS similarity
FROM issues
ORDER BY similarity DESC
LIMIT 5
WHERE DATEDIFF(day, CURRENT_DATE(), issue_date) < 90 AND similarity > 0.7;

Retrieval Augmented Generation¶

In retrieval augmented generation (RAG), a user’s query is used to find similar documents using vector search. The top document is then passed to a large language model (LLM) along with the user’s query, providing context for the generative response (completion). This can improve the appropriateness of the response significantly.

In the following example, wiki is a table with a text column content, and query is a single-row table with a text column :text.

-- Create embedding vectors for wiki articles (only do once)
ALTER TABLE wiki ADD COLUMN vec VECTOR(FLOAT, 768);
UPDATE wiki SET vec = SNOWFLAKE.CORTEX.EMBED_TEXT('e5-base-v2', content);

-- Embed incoming query
ALTER TABLE query ADD COLUMN vec VECTOR(FLOAT, 768);
UPDATE query SET vec = SNOWFLAKE.CORTEX.EMBED_TEXT('e5-base-v2', text);

-- Do a semantic search to find the relevant wiki for the query
WITH result AS (
    SELECT
        content,
        text AS query_text,
        SNOWFLAKE.CORTEX.VECTOR_COSINE_DISTANCE(wiki.vec, query.vec) AS similarity
    FROM wiki, query
    ORDER BY similarity DESC
    LIMIT 1
)

-- Pass to large language model as context
SELECT SNOWFLAKE.CORTEX.COMPLETE('mistral-7b',
    CONCAT('Answer this question: ', query_text, ' using this text: ', content)) FROM result;

Cost Considerations¶

During this preview, the use of EMBED_TEXT is free but limited. You may embed 100,000 strings daily. We will announce changes to this polity well in advance. The use of the vector similarity functions is not limited.

Snowflake Cortex functions run in the warehouse that runs the query that calls them. The cost of running the function is part of the cost of running the query. The cost of vector embedding functions grows linearly with the size of the text. The cost of vector distance queries like the example grows linearly with the number of vectors and dimensions.

Legal Notices¶

The Snowflake Cortex EMBED_VECTOR function is powered by machine learning technology. Machine learning technology and results provided may be inaccurate, inappropriate, or biased. Decisions based on machine learning outputs, including those built into automatic pipelines, should have human oversight and review processes to ensure model-generated content is accurate.

LLM function queries will be treated as any other SQL query and may be considered metadata.