mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-28 06:48:50 +00:00
844955d6e1
Assigning missed defaults in various classes. Most clients were being
assigned during the `model_validator(mode="before")` step, so this
change should amount to a no-op in those cases.
---
This PR was autogenerated using gritql
```shell
grit apply 'class_definition(name=$C, $body, superclasses=$S) where {
$C <: ! "Config", // Does not work in this scope, but works after class_definition
$body <: block($statements),
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
or {
$y <: `Field($z)`,
$x <: "model_config"
}
},
// And has either Any or Optional fields without a default
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
$t <: or {
r"Optional.*",
r"Any",
r"Union[None, .*]",
r"Union[.*, None, .*]",
r"Union[.*, None]",
},
$y <: ., // Match empty node
$t => `$t = None`,
},
}
' --language python .
```
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import Any, Callable, Dict, Iterable, List, Optional
|
|
|
|
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
|
from langchain_core.documents import Document
|
|
from langchain_core.retrievers import BaseRetriever
|
|
from pydantic import ConfigDict, Field
|
|
|
|
|
|
def default_preprocessing_func(text: str) -> List[str]:
|
|
return text.split()
|
|
|
|
|
|
class BM25Retriever(BaseRetriever):
|
|
"""`BM25` retriever without Elasticsearch."""
|
|
|
|
vectorizer: Any = None
|
|
""" BM25 vectorizer."""
|
|
docs: List[Document] = Field(repr=False)
|
|
""" List of documents."""
|
|
k: int = 4
|
|
""" Number of documents to return."""
|
|
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func
|
|
""" Preprocessing function to use on the text before BM25 vectorization."""
|
|
|
|
model_config = ConfigDict(
|
|
arbitrary_types_allowed=True,
|
|
)
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls,
|
|
texts: Iterable[str],
|
|
metadatas: Optional[Iterable[dict]] = None,
|
|
bm25_params: Optional[Dict[str, Any]] = None,
|
|
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
|
|
**kwargs: Any,
|
|
) -> BM25Retriever:
|
|
"""
|
|
Create a BM25Retriever from a list of texts.
|
|
Args:
|
|
texts: A list of texts to vectorize.
|
|
metadatas: A list of metadata dicts to associate with each text.
|
|
bm25_params: Parameters to pass to the BM25 vectorizer.
|
|
preprocess_func: A function to preprocess each text before vectorization.
|
|
**kwargs: Any other arguments to pass to the retriever.
|
|
|
|
Returns:
|
|
A BM25Retriever instance.
|
|
"""
|
|
try:
|
|
from rank_bm25 import BM25Okapi
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import rank_bm25, please install with `pip install "
|
|
"rank_bm25`."
|
|
)
|
|
|
|
texts_processed = [preprocess_func(t) for t in texts]
|
|
bm25_params = bm25_params or {}
|
|
vectorizer = BM25Okapi(texts_processed, **bm25_params)
|
|
metadatas = metadatas or ({} for _ in texts)
|
|
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
|
|
return cls(
|
|
vectorizer=vectorizer, docs=docs, preprocess_func=preprocess_func, **kwargs
|
|
)
|
|
|
|
@classmethod
|
|
def from_documents(
|
|
cls,
|
|
documents: Iterable[Document],
|
|
*,
|
|
bm25_params: Optional[Dict[str, Any]] = None,
|
|
preprocess_func: Callable[[str], List[str]] = default_preprocessing_func,
|
|
**kwargs: Any,
|
|
) -> BM25Retriever:
|
|
"""
|
|
Create a BM25Retriever from a list of Documents.
|
|
Args:
|
|
documents: A list of Documents to vectorize.
|
|
bm25_params: Parameters to pass to the BM25 vectorizer.
|
|
preprocess_func: A function to preprocess each text before vectorization.
|
|
**kwargs: Any other arguments to pass to the retriever.
|
|
|
|
Returns:
|
|
A BM25Retriever instance.
|
|
"""
|
|
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
|
|
return cls.from_texts(
|
|
texts=texts,
|
|
bm25_params=bm25_params,
|
|
metadatas=metadatas,
|
|
preprocess_func=preprocess_func,
|
|
**kwargs,
|
|
)
|
|
|
|
def _get_relevant_documents(
|
|
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
|
) -> List[Document]:
|
|
processed_query = self.preprocess_func(query)
|
|
return_docs = self.vectorizer.get_top_n(processed_query, self.docs, n=self.k)
|
|
return return_docs
|