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Construct Filters | Документация для разработчиков

Construct Filters

Обновлено 6 марта 2024

We may want to do query analysis to extract filters to pass into retrievers. One way we ask the LLM to represent these filters is as a Pydantic model. There is then the issue of converting that Pydantic model into a filter that can be passed into a retriever.

This can be done manually, but LangChain also provides some "Translators" that are able to translate from a common syntax into filters specific to each retriever. Here, we will cover how to use those translators.

from typing import Optional

from langchain.chains.query_constructor.ir import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
)
from langchain.retrievers.self_query.chroma import ChromaTranslator
from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
from langchain_core.pydantic_v1 import BaseModel

In this example, year and author are both attributes to filter on.

class Search(BaseModel):
query: str
start_year: Optional[int]
author: Optional[str]
search_query = Search(query="RAG", start_year=2022, author="LangChain")
def construct_comparisons(query: Search):
comparisons = []
if query.start_year is not None:
comparisons.append(
Comparison(
comparator=Comparator.GT,
attribute="start_year",
value=query.start_year,
)
)
if query.author is not None:
comparisons.append(
Comparison(
comparator=Comparator.EQ,
attribute="author",
value=query.author,
)
)
return comparisons
comparisons = construct_comparisons(search_query)
_filter = Operation(operator=Operator.AND, arguments=comparisons)
ElasticsearchTranslator().visit_operation(_filter)
    {'bool': {'must': [{'range': {'metadata.start_year': {'gt': 2022}}},
{'term': {'metadata.author.keyword': 'LangChain'}}]}}
ChromaTranslator().visit_operation(_filter)
    {'$and': [{'start_year': {'$gt': 2022}}, {'author': {'$eq': 'LangChain'}}]}
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