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/JSON file

import json

records=[
    {
        "name": "answer_question",
        "model_id": "openai/gpt-5-nano",
        "credentials": "OPENAI_API_KEY",
        "config": "",
        "system_template": (
            "You are helpful assistant.\n"
            "Answer the following question based on the provided context.\n"
            "```{context}```"
        ),
        "user_template": "{query}",
        "prompt_builder_kwargs": json.dumps({
            "key_defaults": {"context": "<default context>"},
            "use_jinja": True,
            "jinja_env": "restricted",
            "history_formatter": {
                "prefix_user_message": "Q: ",
                "suffix_user_message": "\n",
                "prefix_assistant_message": "A: ",
                "suffix_assistant_message": "\n",
            }
        }),
        "output_parser_type": "none",
    },
]

# or load the records from a JSON file
records = json.load(open("path/to/records.json"))

catalog = LMRequestProcessorCatalog.from_records(records=records)
lm_request_processor = catalog.answer_question

Template Example

For template examples compatible with LMRequestProcessorCatalog, refer to: 1. CSV: https://github.com/GDP-ADMIN/gl-sdk/tree/main/libs/gllm-inference/gllm_inference/resources/catalog/lm_request_processor_catalog_template.csv 2. JSON: https://github.com/GDP-ADMIN/gl-sdk/tree/main/libs/gllm-inference/gllm_inference/resources/catalog/lm_request_processor_catalog_template.json

Template Explanation

The required columns are: 1. name (str): The name of the LM request processor. 2. model_id (str): The model ID of the LM invoker. 3. credentials (str | json_str): The credentials of the LM invoker. 4. config (json_str): The additional configuration of the LM invoker. 5. system_template (str): The system template of the prompt builder. 6. user_template (str): The user template of the prompt builder. 7. prompt_builder_kwargs (json_str): Additional configuration for the prompt builder. 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. The model_id: 2.1. Must be filled with the model ID of the LM invoker, e.g. "openai/gpt-5-nano". 2.2. Can be partially loaded from the environment variable using the "${ENV_VAR_KEY}" syntax, e.g. "azure-openai/${AZURE_ENDPOINT}/${AZURE_DEPLOYMENT}". 2.3. For the available model ID formats, see: https://gdplabs.gitbook.io/sdk/resources/supported-models 3. credentials is optional. If it is filled, it can either be: 3.1. An environment variable name containing the API key (e.g. OPENAI_API_KEY). 3.2. 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. 3.3. 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. 4. 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. 5. output_parser_type can either be: 5.1. none: No output parser will be used. 5.2. 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/JSON file

records=[
    {
        "name": "answer_question",
        "system": (
            "You are helpful assistant.\n"
            "Answer the following question based on the provided context.\n"
            "```{context}```"
        ),
        "user": "{query}",
        "kwargs": json.dumps({
            "key_defaults": {"context": "<default context>"},
            "use_jinja": True,
            "jinja_env": "restricted"
        }),
    },
]

# or load the records from a JSON file
records = json.load(open("path/to/records.json"))

catalog = PromptBuilderCatalog.from_records(records=records)
prompt_builder = catalog.answer_question

Template Example:

For template examples compatible with PromptBuilderCatalog, refer to:

    1. CSV: https://github.com/GDP-ADMIN/gl-sdk/tree/main/libs/gllm-inference/gllm_inference/resources/catalog/prompt_builder_catalog_template.csv
    2. JSON: https://github.com/GDP-ADMIN/gl-sdk/tree/main/libs/gllm-inference/gllm_inference/resources/catalog/prompt_builder_catalog_template.json

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. kwargs (json_str): Additional configuration for the prompt builder.

Important Notes:

    1. At least one of the `system` or `user` columns must be filled.