How can I efficiently retrieve field-specific text chunks for a Pydantic BaseModel in RAG applications?

Below is one effective strategy that combines metadata on your Pydantic model with a customized retrieval process:

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1. Annotate Fields with Retrieval Metadata

Instead of relying on a single broad query, you can augment each field definition with extra metadata describing which context or query to use. For example, you can add a key (e.g., query_hint) to each field’s extra keyword arguments via the Field constructor:

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from pydantic import BaseModel, Field

class Report(BaseModel):
    title: str = Field(..., description="Title of the report", query_hint="report title")
    author: str = Field(..., description="Author of the report", query_hint="report author")
    introduction: str = Field(..., description="Introduction of the report", query_hint="report introduction")
    findings: str = Field(..., description="Findings of the report", query_hint="report findings")
    conclusion: str = Field(..., description="Conclusion of the report", query_hint="report conclusion")

Here, the extra query_hint value gives you a per-field token that could guide a retrieval system.

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2. Build Specialized Retrieval Logic

Create a function that iterates through your model’s fields. Using each field’s query_hint (or description) you can query your retrieval backend or embedding-based search engine. This approach sets up a field-specific context extraction pipeline.

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from typing import Dict, Any

def retrieve_field_chunk(field_name: str, query: str, large_document: str) -> str:
    """
    This function performs a targeted retrieval of relevant text chunks from a large document.
    You can integrate your semantic search or text splitting logic here.
    """
    # Replace with actual retrieval logic, e.g., using embeddings/similarity search.
    # For the sake of example, we simply return a dummy string.
    return f"Relevant content for {field_name} using query '{query}'"

def populate_report(large_document: str) -> Dict[str, Any]:
    """
    Iterate over Report fields and retrieve relevant text chunks.
    """
    report_data = {}
    # Access the model's field definitions:
    for field_name, field in Report.model_fields.items():
        # Use the field's extra metadata (e.g., query_hint or description) as context.
        query = field.field_info.extra.get("query_hint", field.field_info.description)
        content = retrieve_field_chunk(field_name, query, large_document)
        report_data[field_name] = content
    return report_data

This function uses the field’s metadata to perform specialized queries against your large report. You can substitute the dummy retrieval logic with your actual process (e.g., performing an embedding similarity search on a vector database).

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3. Validate and Structure Using Pydantic

After retrieving the relevant chunks, validate and structure the extracted data using your Pydantic model. This ensures that all fields conform to the expected format.

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# Let's assume large_document is your 100Mb report content.
large_document = "..."  # Your large report text here

# Populate Report data dictionary
report_data = populate_report(large_document)

# Create a report instance using the retrieved field content.
report_instance = Report(**report_data)

print(report_instance.json(indent=2))

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Best Practices

• Index Your Large Corpus: Preprocess your large report by chunking it into sections, and build an index if possible (e.g., using embedding-based methods). This will speed up per-field retrieval.
• Field-Specific Queries: Use the extra metadata (or even a custom field title generator) to determine optimal queries. This minimizes hallucination by ensuring a specific context is used for each field.
• Validation: Leveraging Pydantic’s model validation ensures that even if the retrieval logic returns unexpected types or formats, you can catch and handle these issues early.

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By splitting the retrieval into per-field operations and using the extra metadata attached to each field, you can effectively tailor the results from your large corpus and reduce ambiguity in the model’s output.