HeatWave retrieves content from the vector store and provide that
as context to the LLM. This process is called as
retrieval-augmented generation or RAG. This helps the LLM to
produce more relevant and accurate results for your queries. The
ML_MODEL_LOAD
routine loads the LLM, and the
ML_RAG
routine runs RAG to generate accurate responses for your
queries.
If the vector store tables contain information in different
languages, then the ML_RAG
routine filters
the retrieved context using the embedding model name and the
language used for ingesting files into the vector store table.
Both these details are stored as metadata in the vector store
tables.
Complete the steps to set up a vector store.
For Running Batch Queries, add the natural-language queries to a column in a new or existing table.
To enter a natural-language query, retrieve the context, and generate accurate results using RAG, perform the following steps:
-
To load the LLM in HeatWave memory, use the
ML_MODEL_LOAD
routine:call sys.ML_MODEL_LOAD('LLM', NULL);
Replace
LLM
with the name of the LLM that you want to use. To view the lists of supported LLMs, see LLMs.For example:
call sys.ML_MODEL_LOAD('mistral-7b-instruct-v1', NULL);
This step is optional. The
ML_RAG
routine loads the specified LLM too. But it takes a bit longer to load the LLM and generate the output when you run it for the first time. -
To specify the table for retrieving the vector embeddings to use as context, set the
@options
session variable:set @options = JSON_OBJECT("vector_store", JSON_ARRAY("DBName.VectorStoreTableName"), "model_options", JSON_OBJECT("language", "Language"));
Replace the following:
DBName
: the name of the database that contains the vector store table.VectorStoreTableName
: the name of the vector store table.Language
: the two-letterISO 639-1
code for the language you want to use. Default language isen
, which is English. To view the list of supported languages, see Languages.
For example:
set @options = JSON_OBJECT("vector_store", JSON_ARRAY("demo_db.demo_embeddings"), "model_options", JSON_OBJECT("language", "en"));
To learn more about the available routine options, see ML_RAG Syntax.
-
To define your natural-language query, set the session
@query
variable:set @query="AddYourQuery";
Replace
AddYourQuery
with your natural-language query.For example:
set @query="What is AutoML?";
-
To retrieve the augmented prompt, use the
ML_RAG
routine:call sys.ML_RAG(@query,@output,@options);
-
Print the output:
select JSON_PRETTY(@output);
Text-based content that is generated by the LLM in response to your query is printed as output. The output generated by RAG is comprised of two parts:
The text section contains the text-based content generated by the LLM as a response for your query.
The citations section shows the segments and documents it referred to as context.
The output looks similar to the following:
"text": " AutoML is a machine learning technique that automates the process of selecting, training, and evaluating machine learning models. It involves using algorithms and techniques to automatically identify the best model for a given dataset and optimize its hyperparameters without requiring manual intervention from data analysts or ML practitioners. AutoML can be used in various stages of the machine learning pipeline, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation.", "citations": [ { "segment": "Oracle AutoML also produces high quality models very efficiently, which is achieved through a scalable design and intelligent choices that reduce trials at each stage in the pipeline.\n Scalable design: The Oracle AutoML pipeline is able to exploit both HeatWave internode and intranode parallelism, which improves scalability and reduces runtime.", "distance": 0.4262576103210449, "document_name": "https://objectstorage.Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" }, { "segment": "The HeatWave AutoML ML_TRAIN routine leverages Oracle AutoML technology to automate the process of training a machine learning model. Oracle AutoML replaces the laborious and time consuming tasks of the data analyst whose workflow is as follows:\n1. Selecting a model from a large number of viable candidate models.\n2.\n99", "distance": 0.4311879277229309, "document_name": " https://objectstorage. Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" }, { "segment": "3.1 HeatWave AutoML Features HeatWave AutoML makes it easy to use machine learning, whether you are a novice user or an experienced ML practitioner. You provide the data, and HeatWave AutoML analyzes the characteristics of the data and creates an optimized machine learning model that you can use to generate predictions and explanations.", "distance": 0.4441382884979248, "document_name": "https://objectstorage. Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" } ], "vector_store": [ "demo_db.demo_embeddings" ]
To continue running more queries in the same session, repeat steps 3 to 5.
To enter a natural-language query and retrieve the context without generating a response for the query, perform the following steps:
-
To load the LLM in HeatWave memory, use the
ML_MODEL_LOAD
routine:call sys.ML_MODEL_LOAD('LLM', NULL);
Replace
LLM
with the name of the LLM that you want to use. To view the lists of supported LLMs, see LLMs.For example:
call sys.ML_MODEL_LOAD('mistral-7b-instruct-v1', NULL);
This step is optional. The
ML_RAG
routine loads the specified LLM too. But it takes a bit longer to load the LLM and generate the output when you run it for the first time. -
To specify the table for retrieving the vector embeddings and to skip generation of content, set the
@options
session variable:set @options = JSON_OBJECT("vector_store", JSON_ARRAY("DBName.VectorStoreTableName"), "skip_generate", true);
For example:
set @options = JSON_OBJECT("vector_store", JSON_ARRAY("demo_db.demo_embeddings"), "skip_generate", true);
-
To define your natural-language query, set the session
@query
variable:set @query="AddYourQuery";
Replace
AddYourQuery
with your natural-language query.For example:
set @query="What is AutoML?";
-
To retrieve the augmented prompt, use the
ML_RAG
routine:call sys.ML_RAG(@query,@output,@options);
-
Print the output:
select JSON_PRETTY(@output);
Semantically similar text segments used as content for the query and the name of the documents they were found in are printed as output.
The output looks similar to the following:
{ "citations": [ { "segment": "Oracle AutoML also produces high quality models very efficiently, which is achieved through a scalable design and intelligent choices that reduce trials at each stage in the pipeline.\n Scalable design: The Oracle AutoML pipeline is able to exploit both HeatWave internode and intranode parallelism, which improves scalability and reduces runtime.", "distance": 0.4262576103210449, "document_name": "https://objectstorage.Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" }, { "segment": "The HeatWave AutoML ML_TRAIN routine leverages Oracle AutoML technology to automate the process of training a machine learning model. Oracle AutoML replaces the laborious and time consuming tasks of the data analyst whose workflow is as follows:\n1. Selecting a model from a large number of viable candidate models.\n2.\n99", "distance": 0.4311879277229309, "document_name": " https://objectstorage. Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" }, { "segment": "3.1 HeatWave AutoML Features HeatWave AutoML makes it easy to use machine learning, whether you are a novice user or an experienced ML practitioner. You provide the data, and HeatWave AutoML analyzes the characteristics of the data and creates an optimized machine learning model that you can use to generate predictions and explanations.", "distance": 0.4441382884979248, "document_name": "https://objectstorage. Region.oraclecloud.com/n/Namespace/b/BucketName/o/Path/Filename" } ], "vector_store": [ "demo_db.demo_embeddings" ] }
To continue running more queries in the same session, repeat steps 3 to 5.
To run multiple RAG queries in parallel, use the
ML_RAG_TABLE
routine. This method is faster than running the
ML_RAG
routine multiple times.
To run batch queries using ML_RAG_TABLE
,
perform the following steps:
-
To load the LLM in HeatWave memory, use the
ML_MODEL_LOAD
routine:call sys.ML_MODEL_LOAD('LLM', NULL);
Replace
LLM
with the name of the LLM that you want to use. To view the lists of supported LLMs, see LLMs.For example:
call sys.ML_MODEL_LOAD('mistral-7b-instruct-v1', NULL);
This step is optional. The
ML_RAG_TABLE
routine loads the specified LLM too. But it takes a bit longer to load the LLM and generate the output when you run it for the first time. -
To specify the table for retrieving the vector embeddings to use as context, set the
@options
session variable:set @options = JSON_OBJECT("vector_store", JSON_ARRAY("DBName.VectorStoreTableName"), "model_options", JSON_OBJECT("language", "Language"));
Replace the following:
DBName
: the name of the database that contains the vector store table.VectorStoreTableName
: the name of the vector store table.Language
: the two-letterISO 639-1
code for the language you want to use. Default language isen
, which is English. To view the list of supported languages, see Languages.
For example:
set @options = JSON_OBJECT("vector_store", JSON_ARRAY("demo_db.demo_embeddings"), "model_options", JSON_OBJECT("language", "en"));
To learn more about the available routine options, see ML_RAG_TABLE Syntax.
-
In the
ML_RAG_TABLE
routine, specify the table columns containing the input queries and for storing the generated outputs:select sys.ML_RAG_TABLE(InputDBName.InputTableName.InputColumn, OutputDBName.OutputTableName.OutputColumn, @options);
Replace the following:
InputDBName
: the name of the database that contains the table column where your input queries are stored.InputTableName
: the name of the table that contains the column where your input queries are stored.InputColumn
: the name of the column that contains input queries.OutputDBName
: the name of the database that contains the table where you want to store the generated outputs. This can be the same as the input database.OutputTableName
: the name of the table where you want to create a new column to store the generated outputs. This can be the same as the input table. If the specified table doesn't exist, a new table is created.OutputColumn
: the name for the new column where you want to store the output generated for the input queries.
For example:
select sys.ML_GENERATE_TABLE(demo_db.input_table.Input, demo_db.output_table.Output, @options);