Catalog

Modules concerning the catalog to manage and load prompt builders used in Gen AI applications.

`LMRequestProcessorCatalog`

Bases: BaseCatalog[LMRequestProcessor]

Loads multiple LM request processors from certain sources.

Attributes:

Name	Type	Description
`components`	`dict[str, LMRequestProcessor]`	Dictionary of the loaded LM request processors.

Initialization

Example 1: Load from Google Sheets using client email and private key

catalog = LMRequestProcessorCatalog.from_gsheets(
    sheet_id="...",
    worksheet_id="...",
    client_email="...",
    private_key="...",
)

lm_request_processor = catalog.name

Example 2: Load from Google Sheets using credential file

catalog = LMRequestProcessorCatalog.from_gsheets(
    sheet_id="...",
    worksheet_id="...",
    credential_file_path="...",
)

lm_request_processor = catalog.name

Example 3: Load from CSV

catalog = LMRequestProcessorCatalog.from_csv(csv_path="...")

lm_request_processor = catalog.name

Example 4: Load from record

records=[
    {
        "name": "answer_question",
        "system_template": (
            "You are helpful assistant.\n"
            "Answer the following question based on the provided context.\n"
            "```{context}```"
        ),
        "user_template": "{query}",
        "key_defaults": '{"context": "<default context>"}',
        "model_id": "openai/gpt-5-nano",
        "credentials": "OPENAI_API_KEY",
        "config": "",
        "output_parser_type": "none",
    },
]
catalog = LMRequestProcessorCatalog.from_records(records=records)
lm_request_processor = catalog.answer_question

Template Format Example

Example 1: Google Sheets

For an example of how a Google Sheets file can be formatted to be loaded using LMRequestProcessorCatalog, see: https://docs.google.com/spreadsheets/d/1CX9i45yEinv1UdB3s6uHNMj7mxr2-s1NFHfFDvMsq0E/edit?usp=drive_link

Example 2: CSV

For an example of how a CSV file can be formatted to be loaded using LMRequestProcessorCatalog, see: https://drive.google.com/file/d/1_2rSoxh3CR2KZxIyUmpowMrt0Lm0YqAb/view?usp=drive_link

Template Explanation

The required columns are: 1. name (str): The name of the LM request processor. 2. system_template (str): The system template of the prompt builder. 3. user_template (str): The user template of the prompt builder. 4. key_defaults (json_str): The default values for the prompt template keys. 5. model_id (str): The model ID of the LM invoker. 6. credentials (str | json_str): The credentials of the LM invoker. 7. config (json_str): The additional configuration of the LM invoker. 8. output_parser_type (str): The type of the output parser.

Important Notes: 1. At least one of system_template or user_template must be filled. 2. key_defaults is optional. If filled, must be a dictionary containing the default values for the prompt template keys. These default values will be applied when the corresponding keys are not provided in the runtime input. If it is empty, the prompt template keys will not have default values. 3. The model_id: 3.1. Must be filled with the model ID of the LM invoker, e.g. "openai/gpt-5-nano". 3.2. Can be partially loaded from the environment variable using the "${ENV_VAR_KEY}" syntax, e.g. "azure-openai/${AZURE_ENDPOINT}/${AZURE_DEPLOYMENT}". 3.3. For the available model ID formats, see: https://gdplabs.gitbook.io/sdk/resources/supported-models 4. credentials is optional. If it is filled, it can either be: 4.1. An environment variable name containing the API key (e.g. OPENAI_API_KEY). 4.2. An environment variable name containing the path to a credentials JSON file (e.g. GOOGLE_CREDENTIALS_FILE_PATH). Currently only supported for Google Vertex AI. 4.3. A dictionary of credentials, with each value being an environment variable name corresponding to the credential (e.g. {"api_key": "OPENAI_API_KEY"}). Currently supported for Bedrock and LangChain. If it is empty, the LM invoker will use the default credentials loaded from the environment variables. 5. config is optional. If filled, must be a dictionary containing the configuration for the LM invoker. If it is empty, the LM invoker will use the default configuration. 6. output_parser_type can either be: 6.1. none: No output parser will be used. 6.2. json: The JSONOutputParser will be used.

`PromptBuilderCatalog`

Bases: BaseCatalog[PromptBuilder]

Loads multiple prompt builders from certain sources.

Attributes:

Name	Type	Description
`components`	`dict[str, PromptBuilder]`	Dictionary of the loaded prompt builders.

Initialization

Example 1: Load from Google Sheets using client email and private key

catalog = PromptBuilderCatalog.from_gsheets(
    sheet_id="...",
    worksheet_id="...",
    client_email="...",
    private_key="...",
)
prompt_builder = catalog.name

Example 2: Load from Google Sheets using credential file

catalog = PromptBuilderCatalog.from_gsheets(
    sheet_id="...",
    worksheet_id="...",
    credential_file_path="...",
)
prompt_builder = catalog.name

Example 3: Load from CSV

catalog = PromptBuilderCatalog.from_csv(csv_path="...")
prompt_builder = catalog.name

Example 4: Load from records

records=[
    {
        "name": "answer_question",
        "system": (
            "You are helpful assistant.\n"
            "Answer the following question based on the provided context.\n"
            "```{context}```"
        ),
        "user": "{query}",
        "key_defaults": '{"context": "<default context>"}',
    },
]
catalog = PromptBuilderCatalog.from_records(records=records)
prompt_builder = catalog.answer_question

Template Example

Example 1: Google Sheets

For an example of how a Google Sheets file can be formatted to be loaded using PromptBuilderCatalog, see: https://docs.google.com/spreadsheets/d/12IwSKv8hMhyWXSQnLx9LgCj0cxaR1f9gOmbEDGleurE/edit?usp=drive_link

Example 2: CSV

For an example of how a CSV file can be formatted to be loaded using PromptBuilderCatalog, see: https://drive.google.com/file/d/1KQgddMdbcZBZmroQFtjSl-TKLohq84Fz/view?usp=drive_link

Template explanation

The required columns are: 1. name (str): The name of the prompt builder. 2. system (str): The system template of the prompt builder. 3. user (str): The user template of the prompt builder. 4. key_defaults (json_str): The default values for the prompt template keys.

Important Notes: 1. At least one of the system and user columns must be filled. 2. key_defaults is optional. If filled, must be a dictionary containing the default values for the prompt template keys. These default values will be applied when the corresponding keys are not provided in the runtime input. If it is empty, the prompt template keys will not have default values.