Amazon Bedrock Prompt
The Amazon Bedrock 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 General guidelines for Amazon Bedrock LLM users if you are new to prompt engineering.
Note
- You'll need to create a cloud provider credential to access the regions in your AWS account.
- You'll need to set permissions to allow access to Amazon Bedrock.
Alternative components include OpenAI Prompt and Azure OpenAI Prompt.
Properties
Name = string
A human-readable name for the component.
Region = drop-down
Select the AWS region that Amazon Bedrock is configured for on your AWS account.
Model Category = drop-down
Select the model category. Currently supports:
- Anthropic
- Mistral
- Amazon
- Meta
- Cohere
Model = drop-down
Specify the model.
Temperature = floating point number
Set the input temperature. Accepts decimal values between 0 and 2.
Read Temperature to learn more.
Top P = 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.
It is recommended to alter Top P or Temperature, but not both.
Number of Generations = integer
Cohere models only. The maximum number of generations that the model should return.
Default is 1.
Max Tokens = integer
The maximum number of tokens to process. Each model has its own maximum token allowance, and this must be considered.
| Model category | Default |
|---|---|
| Anthropic | 200 |
| Mistral | 512 |
| Amazon | 512 |
| Meta | 512 |
| Cohere | 200 |
Guardrail ID = string
String ID of an AWS guardrail used to implement safeguards for your generative AI applications. Any guardrails that you wish to use should be created in the same AWS region as the region specified above for using Amazon Bedrock.
To learn more, read Guardrails for Amazon Bedrock.
Select your cloud data warehouse.
Database = drop-down
The Snowflake source database. The special value, [Environment Default], will use the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
Schema = drop-down
The Snowflake source schema. The special value, [Environment Default], will use the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
Table = drop-down
An existing Snowflake table to use as the input.
Catalog = drop-down
Select a source Databricks Unity Catalog. The special value, [Environment Default], will use the catalog specified in the Data Productivity Cloud environment setup. Selecting a catalog will determine which databases are available in the next parameter.
Schema (Database) = drop-down
The Databricks source schema. The special value, [Environment Default], will use the schema defined in the environment. Read Create and manage schemas to learn more.
Table = drop-down
An existing Databricks table to use as the input.
Schema = drop-down
The Redshift source schema. The special value, [Environment Default], will use the schema defined in the environment. Read Schemas to learn more.
Table = drop-down
An existing Redshift table to use as the input.
Key Column = drop-down
Set a column as the primary key. Join the results back to the input table.
Limit = integer
Set a limit for the numbers of rows from the table to load. The default is 1000.
User Context = 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.
Inputs = column editor
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.
Output Format
Choose TEXT or JSON. Choosing JSON will activate an additional property, Outputs.
Outputs = column editor
JSON only.
Define the output columns the prompt component will generate.
- Output: Key of a
key:valueJSON 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).
Select your cloud data warehouse.
Database = drop-down
The Snowflake destination database. The special value, [Environment Default], will use the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
Schema = drop-down
The Snowflake destination schema. The special value, [Environment Default], will use the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
Table = string
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.
Catalog = drop-down
Select a destination Databricks Unity Catalog. The special value, [Environment Default], will use the catalog specified in the Data Productivity Cloud environment setup. Selecting a catalog will determine which databases are available in the next parameter.
Schema (Database) = drop-down
The Databricks destination schema. The special value, [Environment Default], will use the schema defined in the environment. Read Create and manage schemas to learn more.
Table = string
The new Databricks 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.
Schema = drop-down
The Redshift destination schema. The special value, [Environment Default], will use the schema defined in the environment. Read Schemas to learn more.
Table = string_
The new Redshift 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.
Create Table Options
- 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.
Enable RAG = boolean
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.
To learn more, read What is Retrieval-Augmented Generation?.
Pretext = text editor
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:"
Search Column = drop-down
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.
Embedding Provider = drop-down
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:
OpenAI API Key = drop-down
Use the drop-down menu to select the corresponding secret definition that denotes the value of your OpenAI API key.
Read Secret definitions to learn how to create a new secret definition.
To create a new OpenAI API key:
- Log in to OpenAI.
- Click your avatar in the top-right of the UI.
- Click View API keys.
- Click + Create new secret key.
- Give a name for your new secret key and click Create secret key.
- Copy your new secret key and save it. Then click Done.
Embedding Model = drop-down
Select an embedding model.
Currently supports:
- text-embedding-ada-002
- text-embedding-3-small
- text-embedding-3-large
Embedding AWS Region = drop-down
Select your AWS region.
Embedding Model = drop-down
Select an embedding model.
Currently supports:
Vector Database = drop-down
Select a vector database to use.
Currently supports Pinecone and Postgres.
Pinecone API Key = drop-down
Use the drop-down menu to select the corresponding secret definition that denotes the value of your Pinecone API key.
Read Secret definitions to learn how to create a new secret definition.
Pinecone Index = string
The name of the Pinecone vector search index to connect to. To retrieve an index name:
- Log in to Pinecone.
- Click PROJECTS in the left sidebar.
- Click a project tile. This action will open the list of vector search indexes in your project.
Pinecone Namespace = string
The name of the Pinecone namespace. Pinecone lets you partition records in an index into namespaces. To retrieve a namespace name:
- Log in to Pinecone.
- Click PROJECTS in the left sidebar.
- Click a project tile. This action will open the list of vector search indexes in your project.
- Click on your vector search index tile.
- Click the NAMESPACES tab. Your namespaces will be listed.
Top K = integer
The number of results to return from the vector database query.
Between 1-100.
Default is 3.
Data Lookup Strategy = drop-down
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.
Data Path = string
Set the path to the data in the metadata JSON blob.
Default is data.
Host = string
Your Postgres hostname.
Port = string
The TCP port number the Postgres server listens on. The default is 5432.
Database = string
The name of your Postgres database.
Username = string
Your Postgres username.
Password = drop-down
Use the drop-down menu to select the corresponding secret definition that denotes the value of your Postgres password.
Read Secret definitions to learn how to create a new secret definition.
Schema = drop-down
The Postgres schema. The available schemas are determined by the Postgres database you have provided.
Table = drop-down
The table to load data from. The available tables are determined by the Postgres schema you have selected.
Key Column Name = drop-down
The column in your table to use as the key column.
Text Column Name = drop-down
The column in your table with your original text data.
Embedding Column Name = drop-down
The column in your table used to store your embeddings.
Similarity Function = drop-down
Select which similarity function (distance metrics) to use.
Top K = integer
The number of results to return from the vector database query.
Between 1-100.
Default is 3.
Connection Options = column editor
- Parameter: A JDBC Postgres parameter supported by the database driver.
- Value: A value for the given parameter.
Explanation of output
| Output column | Description |
|---|---|
| DATA | A JSON set of key:value pairs when using the JSON. |
| RAW_DATA | All responses received from the LLM. If you have set the parameter N to a number greater than 1, then you will see multiple responses for each record. |
| ERROR_METADATA | Contains information about errors that occurred when processing that row, such as if no valid JSON was found, or the request to the LLM timed out. Useful for debugging results where no selected result, and/or no raw data is available in the output. |
| SYSTEM_METADATA | This is a fundamental instruction from Matillion's component for the LLM, advising how it should process and respond to the information it receives. For example: "You are a data analysis and exploration tool. You receive a context prompt from the user, a data object, and an output format. Generate the output in a valid JSON format, including only the output variables without any headers or explanations. Escape any values that would not be valid in a JSON string, such as newlines and double quotes." |
| USER_PROMPT | Prompt defined by the user in the Context property of the OpenAI prompt, Amazon Bedrock Prompt, or Azure OpenAI Prompt component. |
| MODEL_NAME | The specific name of the model. |
| PROVIDER_NAME | Name of the LLM provider. |
| Snowflake | Databricks | Amazon Redshift |
|---|---|---|
| ✅ | ✅ | ✅ |