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 .
```
126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
from __future__ import annotations
|
|
|
|
import concurrent.futures
|
|
from typing import Any, Iterable, List, Optional
|
|
|
|
import numpy as np
|
|
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
|
from langchain_core.documents import Document
|
|
from langchain_core.embeddings import Embeddings
|
|
from langchain_core.retrievers import BaseRetriever
|
|
from pydantic import ConfigDict
|
|
|
|
|
|
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
|
|
"""
|
|
Create an index of embeddings for a list of contexts.
|
|
|
|
Args:
|
|
contexts: List of contexts to embed.
|
|
embeddings: Embeddings model to use.
|
|
|
|
Returns:
|
|
Index of embeddings.
|
|
"""
|
|
with concurrent.futures.ThreadPoolExecutor() as executor:
|
|
return np.array(list(executor.map(embeddings.embed_query, contexts)))
|
|
|
|
|
|
class NanoPQRetriever(BaseRetriever):
|
|
"""`NanoPQ retriever."""
|
|
|
|
embeddings: Embeddings
|
|
"""Embeddings model to use."""
|
|
index: Any = None
|
|
"""Index of embeddings."""
|
|
texts: List[str]
|
|
"""List of texts to index."""
|
|
metadatas: Optional[List[dict]] = None
|
|
"""List of metadatas corresponding with each text."""
|
|
k: int = 4
|
|
"""Number of results to return."""
|
|
relevancy_threshold: Optional[float] = None
|
|
"""Threshold for relevancy."""
|
|
subspace: int = 4
|
|
"""No of subspaces to be created, should be a multiple of embedding shape"""
|
|
clusters: int = 128
|
|
"""No of clusters to be created"""
|
|
|
|
model_config = ConfigDict(
|
|
arbitrary_types_allowed=True,
|
|
)
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls,
|
|
texts: List[str],
|
|
embeddings: Embeddings,
|
|
metadatas: Optional[List[dict]] = None,
|
|
**kwargs: Any,
|
|
) -> NanoPQRetriever:
|
|
index = create_index(texts, embeddings)
|
|
return cls(
|
|
embeddings=embeddings,
|
|
index=index,
|
|
texts=texts,
|
|
metadatas=metadatas,
|
|
**kwargs,
|
|
)
|
|
|
|
@classmethod
|
|
def from_documents(
|
|
cls,
|
|
documents: Iterable[Document],
|
|
embeddings: Embeddings,
|
|
**kwargs: Any,
|
|
) -> NanoPQRetriever:
|
|
texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
|
|
return cls.from_texts(
|
|
texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
|
|
)
|
|
|
|
def _get_relevant_documents(
|
|
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
|
) -> List[Document]:
|
|
try:
|
|
from nanopq import PQ
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import nanopq, please install with `pip install " "nanopq`."
|
|
)
|
|
|
|
query_embeds = np.array(self.embeddings.embed_query(query))
|
|
try:
|
|
pq = PQ(M=self.subspace, Ks=self.clusters, verbose=True).fit(
|
|
self.index.astype("float32")
|
|
)
|
|
except AssertionError:
|
|
error_message = (
|
|
"Received params: training_sample={training_sample}, "
|
|
"n_cluster={n_clusters}, subspace={subspace}, "
|
|
"embedding_shape={embedding_shape}. Issue with the combination. "
|
|
"Please retrace back to find the exact error"
|
|
).format(
|
|
training_sample=self.index.shape[0],
|
|
n_clusters=self.clusters,
|
|
subspace=self.subspace,
|
|
embedding_shape=self.index.shape[1],
|
|
)
|
|
raise RuntimeError(error_message)
|
|
|
|
index_code = pq.encode(vecs=self.index.astype("float32"))
|
|
dt = pq.dtable(query=query_embeds.astype("float32"))
|
|
dists = dt.adist(codes=index_code)
|
|
|
|
sorted_ix = np.argsort(dists)
|
|
|
|
top_k_results = [
|
|
Document(
|
|
page_content=self.texts[row],
|
|
metadata=self.metadatas[row] if self.metadatas else {},
|
|
)
|
|
for row in sorted_ix[0 : self.k]
|
|
]
|
|
|
|
return top_k_results
|