Skip to main content
Production use of this feature is available for specific editions only. Contact our sales team for more information.
The OpenAI Prompt component uses a large language model (LLM) to provide responses to user-composed prompts. The component takes one or more inputs from your source table, combines the inputs with user prompts, and sends this data to the LLM for processing. You can configure the component to output the results as either a text value or a JSON object. The results will be stored—along with other metadata—in a destination table on your cloud data warehouse. We recommend that you read Best practices for prompt engineering with OpenAI API if you are new to prompt engineering.

​
Video example

​
Properties

Reference material is provided below for the Connect, Source, Configure, Destination, and RAG properties.
​
string
required
A human-readable name for the component.

​
Connect

​
drop-down
required
Specify the OpenAI model. Select [Custom] if you wish to specify a model not available in the drop-down list.
​
string
required
The name of your custom model. Only required if you selected [Custom] in Model. Use the full name—for example, gpt-3.5-turbo-instruct.
​
drop-down
required
Use the drop-down menu to select the corresponding secret definition that denotes the value of your OpenAI API key.Read Secrets and secret definitions to learn how to create a new secret definition.To create a new OpenAI API key:
  1. Log in to OpenAI.
  2. Click your avatar in the top-right of the UI.
  3. Click View API keys.
  4. Click + Create new secret key.
  5. Give a name for your new secret key and click Create secret key.
  6. Copy your new secret key and save it. Then click Done.
​
floating point number
Set the input temperature. Accepts decimal values between 0 and 2.To quote OpenAI:
Lower values for temperature result in more consistent outputs, while higher values generate more diverse and creative results. Select a temperature value based on the desired trade-off between coherence and creativity for your specific application.
​
floating point number
An alternative method to sampling with “temperature” called “nucleus sampling”. The model “considers” the results of the tokens, with top_p probability mass. Therefore, 0.1 means that only tokens comprising the top 10% of probability mass would be considered.OpenAI recommends altering Top P or Temperature, but not both.
​
integer
You can choose to generate more than one response for your input data when the output is JSON. This parameter can quickly consume your token quota—use carefully. Default is 1.
​
drop-down
Select the effort level used for reasoning. This parameter is only visible when you select a reasoning model. Supported effort levels are low, medium, and high. The default setting is Medium.Setting this parameter to Low may lead to faster responses and fewer tokens used on reasoning in the response.
​
integer
The maximum number of completion tokens per prompt request. Each model has its own maximum token allowance, and this must be considered.Visit the OpenAI tokenizer tool to learn more about language model tokenization.

​
Source

Select your cloud data warehouse.
​
drop-down
required
The Snowflake source database. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
​
drop-down
required
The Snowflake source schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
​
drop-down
required
An existing Snowflake table to use as the input.
​
drop-down
required
Set a column as the primary key. Join the results back to the input table.
​
integer
required
Set a limit for the numbers of rows from the table to load. The default is 1000.

​
Configure

​
text editor
Provide your prompt. When Output Format is TEXT, this property is where you must specify all of the questions that you wish the LLM to provide answers to.Prompts should define the following information:
  • A persona. Who or what should the model impersonate when contextualizing their generative responses?
  • A context. Contextualize the situation for the model to enhance its responses.
  • A tone. What kind of language do you want the model to use?
Providing an example output may improve performance.
​
column editor
required
Select the source columns to feed as input to the prompt component.
  • Column Name: A column from the input table.
  • Descriptive Name: An alternate descriptive name to better contextualize the column. Recommended if your column names are low-context.
  • Type: Choose Text or Image. Image is currently only supported for model GPT-4o.
Images should be in either Base64 format or a direct, public URL of the image. If you’re feeding images in Base64 format, you will need to add data:image/{format_of_your_image};base64, before the encoded image.For example if your image is .jpeg, the encoded image would look like this:data:image/jpeg;base64, iVBORw0KGgoAAAANSUhEUgAAAyAAAAPoCAIAAACpqQ3mAAAy7klEQVR4nO3deZgj...
​
drop-down
required
Choose TEXT or JSON. Choosing JSON will activate an additional property, Outputs.
​
column editor
required
JSON only.Define the output columns the prompt component will generate.
  • Output: Key of a key:value JSON pair. For example, an output might be “review_score”.
  • Context: Text that defines the output you expect the model to provide—that is, some task for the model to perform. For example, “Give a score between 0 and 10 on the level of satisfaction you feel in the user’s review where 0 is completely dissatisfied and 10 is extremely satisfied.” You may wish to use this parameter to configure the tone of the model (where applicable).

​
Destination

Select your cloud data warehouse.
​
drop-down
required
The Snowflake destination database. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
​
drop-down
required
The Snowflake destination schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
​
string
required
The new Snowflake table to load your prompt output into. Will create a new table if one does not exist. Otherwise, will replace any existing table of the same name.
​
required
  • Replace if Table Exists: The pipeline will run despite the table already existing. The table will be recreated.
  • Fail if Table Exists: If the table already exists, the pipeline will fail to run. This is the default setting.
  • Append: If the table already exists, any new rows (records) will be added to the table without modifying or deleting existing rows. If the table does not exist, it is created and any new rows are inserted.
Snowflake only:This component uses the CREATE OR REPLACE clause. When using the REPLACE clause, it also applies the COPY GRANTS clause. When you clone or create a new object (such as a table, view, schema, or database) from an existing one, the new object doesn’t automatically inherit the original’s grants (privileges). However, with the COPY GRANTS clause, you can seamlessly transfer object-level privileges from the source object to the new one. This helps maintain consistent access control and simplifies permission management when cloning or recreating objects. For more information, read Snowflake COPY GRANTS.
​
string
Optionally set a prefix for newly created columns.

​
RAG

​
boolean
required
Click Yes to enable Retrieval-Augmented Generation (RAG). Using RAG optimizes an LLM output by invoking an authoritative knowledge base outside of the LLM’s initial training data sources. By using RAG, you can extend an LLM’s capabilities to specific domains, such as your organization’s documentation, without needing to retrain the model.Defaults to No.
​
text editor
required
Add text to your LLM prompt before the RAG data is listed, thus instructing the LLM what to do with the RAG data. For example, you might wish to use RAG to search relevant documentation snippets to answer a question.Example:“Use any of the following documentation snippets in your response, if relevant:”
​
drop-down
required
Choose a column in the source table that contains a search term for the vector database. The value is then taken from that column and a vector search is performed. For example, a column value might be a user question such as “How do I log in?”. A search is then performed on the vector database using the value string, which will return N number of relevant data. N is defined by the Top K parameter, further down.If your vector database contained vectors created from chunks of text documentation, in this scenario the RAG data returned may include the chunk “to log in, click on the key button in the top right and enter your username and password”. This data is then inserted into the llm prompt to help provide relevant context.
​
drop-down
required
The embedding provider is the API service used to convert the search term into a vector. Choose either OpenAI or Amazon Bedrock. The embedding provider receives a search term (e.g. “How do I log in?”) and returns a vector.Choose your provider:
​
drop-down
required
Use the drop-down menu to select the corresponding secret definition that denotes the value of your OpenAI API key.Read Secrets and secret definitions to learn how to create a new secret definition.To create a new OpenAI API key:
  1. Log in to OpenAI.
  2. Click your avatar in the top-right of the UI.
  3. Click View API keys.
  4. Click + Create new secret key.
  5. Give a name for your new secret key and click Create secret key.
  6. Copy your new secret key and save it. Then click Done.
​
drop-down
required
Select an embedding model.Currently supports:
  • text-embedding-ada-002
  • text-embedding-3-small
  • text-embedding-3-large
​
drop-down
required
Select a vector database to use.Currently supports Pinecone and Postgres.
​
drop-down
required
Use the drop-down menu to select the corresponding secret definition that denotes the value of your Pinecone API key.Read Secrets and secret definitions to learn how to create a new secret definition.
​
string
required
The name of the Pinecone vector search index to connect to. To retrieve an index name:
  1. Log in to Pinecone.
  2. Click PROJECTS in the left sidebar.
  3. Click a project tile. This action will open the list of vector search indexes in your project.
​
string
required
The name of the Pinecone namespace. Pinecone lets you partition records in an index into namespaces. To retrieve a namespace name:
  1. Log in to Pinecone.
  2. Click PROJECTS in the left sidebar.
  3. Click a project tile. This action will open the list of vector search indexes in your project.
  4. Click on your vector search index tile.
  5. Click the NAMESPACES tab. Your namespaces will be listed.
​
integer
required
The number of results to return from the vector database query.Between 1-100.Default is 3.
​
drop-down
required
Select the data lookup strategy. Pinecone only stores the vector associated with text data, and a JSON metadata blob. While the text data can be stored in the metadata blob, size limitations can affect coverage—for example when a user has a larger blob of text to be converted to a vector.
  • Raw data in metadata: Choosing this option adds an additional property, Data Path, to provide the path to text data within the metadata JSON blob.
  • Table details in metadata: Database, schema, and table information is used in the metadata to look up the text data in your warehouse table.
​
string
required
Set the path to the data in the metadata JSON blob.Default is data.

​
Explanation of output