Retrieval Parameter Extractor

gllm-retrieval| Involves LM | Tutorial: Retrieval Parameter Extractor | API Reference

The retrieval parameter extractor attempts to turn a natural-language query (and optional context) into structured retrieval parameters (e.g., query text, filters, sort). It is useful when you want an LLM to infer filters/sort constraints from user intent before hitting your retriever.

Prerequisites

This example specifically requires completion of all setup steps listed on the Prerequisites page.

You should be familiar with these concepts:

  1. Language Model (LM) Invoker

  2. LM Request Processor (LMRP)

Installation

# you can use a Conda environment
pip install --extra-index-url https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/ "gllm-retrieval"

Quickstart

We will use GPT-4.1 nano to extract certain insights from user query.

import asyncio
import json

from gllm_inference.builder import build_lm_request_processor
from gllm_retrieval.retrieval_parameter_extractor.lm_based_retrieval_parameter_extractor import (
    LMBasedRetrievalParameterExtractor,
)

def main() -> None:
    SYSTEM_TEMPLATE = """\
        Role: You are an assistant that extracts retrieval parameters from a user query.
        Objective: Infer a structured set of retrieval parameters that a search engine can use.

        AllowedValues (JSON):
        {{
        "department": {allowed_departments_json},
        "content_type": {allowed_content_types_json},
        "sort_fields": {allowed_sort_fields_json},
        "operators": {allowed_operators_json}
        }}

        OutputFormat (JSON):
        {{
        "query": "<string>",
        "filters": [
            {{"field": "<string>", "operator": "<operator_from_allowed_values>", "value": "<string|number|bool>"}}
        ],
        "sort": [
            {{"field": "<sort_field_from_allowed_values>", "order": "asc" | "desc"}}
        ]
        }}

        Instructions:
        - Use only values listed in AllowedValues.
        - Return ONLY a compact JSON object conforming to OutputFormat. No extra text.
    """

    lmrp = build_lm_request_processor(
        model_id="openai/gpt-4.1-nano",
        credentials="<your-api-key>",  # Or use the OPENAI_API_KEY environment variable
        system_template=SYSTEM_TEMPLATE,
        user_template="{query}",
        output_parser_type="json",
    )

    extractor = LMBasedRetrievalParameterExtractor(lm_request_processor=lmrp)

    # Provide JSON-serialized allowed values so they render as JSON in the system template.
    result = asyncio.run(extractor.extract_parameters(
        "Find latest InfoSec security policy documents and prioritize recent updates",
        allowed_departments_json=json.dumps(["InfoSec", "Finance", "HR", "Engineering"]),
        allowed_content_types_json=json.dumps(["security_policies", "standards", "guidelines", "procedures"]),
        allowed_sort_fields_json=json.dumps(["date", "relevance", "title"]),
        allowed_operators_json=json.dumps(["eq", "neq", "gt", "gte", "lt", "lte", "in", "nin", "like"]),
    ))
    print(result)

if __name__ == "__main__":
    main()

The system or prompt template must contain the template {query}.

The output is expected to be JSON.

Passing a validator

You can optionally pass a validator to validate the result from the LM. You can pass a JSON schema dictionary or a Pydantic BaseModel.

The following example uses a validator.

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