mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
f92006de3c
0.2rc migrations - [x] Move memory - [x] Move remaining retrievers - [x] graph_qa chains - [x] some dependency from evaluation code potentially on math utils - [x] Move openapi chain from `langchain.chains.api.openapi` to `langchain_community.chains.openapi` - [x] Migrate `langchain.chains.ernie_functions` to `langchain_community.chains.ernie_functions` - [x] migrate `langchain/chains/llm_requests.py` to `langchain_community.chains.llm_requests` - [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder` -> `langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder` (namespace not ideal, but it needs to be moved to `langchain` to avoid circular deps) - [x] unit tests langchain -- add pytest.mark.community to some unit tests that will stay in langchain - [x] unit tests community -- move unit tests that depend on community to community - [x] mv integration tests that depend on community to community - [x] mypy checks Other todo - [x] Make deprecation warnings not noisy (need to use warn deprecated and check that things are implemented properly) - [x] Update deprecation messages with timeline for code removal (likely we actually won't be removing things until 0.4 release) -- will give people more time to transition their code. - [ ] Add information to deprecation warning to show users how to migrate their code base using langchain-cli - [ ] Remove any unnecessary requirements in langchain (e.g., is SQLALchemy required?) --------- Co-authored-by: Erick Friis <erick@langchain.dev>
101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
from typing import Dict, Tuple, Union
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from langchain_core.structured_query import (
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Comparator,
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Comparison,
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Operation,
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Operator,
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StructuredQuery,
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Visitor,
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)
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class ElasticsearchTranslator(Visitor):
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"""Translate `Elasticsearch` internal query language elements to valid filters."""
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allowed_comparators = [
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Comparator.EQ,
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Comparator.GT,
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Comparator.GTE,
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Comparator.LT,
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Comparator.LTE,
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Comparator.CONTAIN,
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Comparator.LIKE,
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]
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"""Subset of allowed logical comparators."""
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allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
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"""Subset of allowed logical operators."""
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def _format_func(self, func: Union[Operator, Comparator]) -> str:
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self._validate_func(func)
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map_dict = {
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Operator.OR: "should",
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Operator.NOT: "must_not",
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Operator.AND: "must",
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Comparator.EQ: "term",
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Comparator.GT: "gt",
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Comparator.GTE: "gte",
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Comparator.LT: "lt",
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Comparator.LTE: "lte",
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Comparator.CONTAIN: "match",
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Comparator.LIKE: "match",
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}
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return map_dict[func]
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def visit_operation(self, operation: Operation) -> Dict:
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args = [arg.accept(self) for arg in operation.arguments]
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return {"bool": {self._format_func(operation.operator): args}}
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def visit_comparison(self, comparison: Comparison) -> Dict:
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# ElasticsearchStore filters require to target
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# the metadata object field
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field = f"metadata.{comparison.attribute}"
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is_range_comparator = comparison.comparator in [
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Comparator.GT,
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Comparator.GTE,
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Comparator.LT,
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Comparator.LTE,
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]
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if is_range_comparator:
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value = comparison.value
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if isinstance(comparison.value, dict) and "date" in comparison.value:
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value = comparison.value["date"]
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return {"range": {field: {self._format_func(comparison.comparator): value}}}
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if comparison.comparator == Comparator.CONTAIN:
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return {
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self._format_func(comparison.comparator): {
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field: {"query": comparison.value}
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}
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}
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if comparison.comparator == Comparator.LIKE:
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return {
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self._format_func(comparison.comparator): {
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field: {"query": comparison.value, "fuzziness": "AUTO"}
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}
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}
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# we assume that if the value is a string,
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# we want to use the keyword field
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field = f"{field}.keyword" if isinstance(comparison.value, str) else field
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if isinstance(comparison.value, dict):
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if "date" in comparison.value:
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comparison.value = comparison.value["date"]
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return {self._format_func(comparison.comparator): {field: comparison.value}}
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def visit_structured_query(
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self, structured_query: StructuredQuery
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) -> Tuple[str, dict]:
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if structured_query.filter is None:
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kwargs = {}
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else:
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kwargs = {"filter": [structured_query.filter.accept(self)]}
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return structured_query.query, kwargs
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