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(Deprecated) Query Engines + Pydantic Outputs#

Tip

This guide references a deprecated method of extracting structured outputs in a RAG workflow. Check out our structured output starter guide for more details.

Using index.as_query_engine() and it's underlying RetrieverQueryEngine, we can support structured pydantic outputs without an additional LLM calls (in contrast to a typical output parser.)

Every query engine has support for integrated structured responses using the following response_modes in RetrieverQueryEngine:

  • refine
  • compact
  • tree_summarize
  • accumulate (beta, requires extra parsing to convert to objects)
  • compact_accumulate (beta, requires extra parsing to convert to objects)

Under the hood, this uses OpenAIPydanitcProgam or LLMTextCompletionProgram depending on which LLM you've setup. If there are intermediate LLM responses (i.e. during refine or tree_summarize with multiple LLM calls), the pydantic object is injected into the next LLM prompt as a JSON object.

Usage Pattern#

First, you need to define the object you want to extract.

from typing import List
from pydantic import BaseModel


class Biography(BaseModel):
    """Data model for a biography."""

    name: str
    best_known_for: List[str]
    extra_info: str

Then, you create your query engine.

query_engine = index.as_query_engine(
    response_mode="tree_summarize", output_cls=Biography
)

Lastly, you can get a response and inspect the output.

response = query_engine.query("Who is Paul Graham?")

print(response.name)
# > 'Paul Graham'
print(response.best_known_for)
# > ['working on Bel', 'co-founding Viaweb', 'creating the programming language Arc']
print(response.extra_info)
# > "Paul Graham is a computer scientist, entrepreneur, and writer. He is best known      for ..."

Modules#

Detailed usage is available in the notebooks below: