mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-20 20:48:16 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""KNN Retriever.
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Largely based on
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https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
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from __future__ import annotations
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import concurrent.futures
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from typing import Any, Iterable, List, Optional
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import numpy as np
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.retrievers import BaseRetriever
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def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
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"""
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Create an index of embeddings for a list of contexts.
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Args:
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contexts: List of contexts to embed.
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embeddings: Embeddings model to use.
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Returns:
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Index of embeddings.
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"""
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with concurrent.futures.ThreadPoolExecutor() as executor:
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return np.array(list(executor.map(embeddings.embed_query, contexts)))
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class KNNRetriever(BaseRetriever):
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"""`KNN` retriever."""
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embeddings: Embeddings
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"""Embeddings model to use."""
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index: Any
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"""Index of embeddings."""
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texts: List[str]
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"""List of texts to index."""
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metadatas: Optional[List[dict]] = None
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"""List of metadatas corresponding with each text."""
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k: int = 4
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"""Number of results to return."""
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relevancy_threshold: Optional[float] = None
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"""Threshold for relevancy."""
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embeddings: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> KNNRetriever:
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index = create_index(texts, embeddings)
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return cls(
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embeddings=embeddings,
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index=index,
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texts=texts,
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metadatas=metadatas,
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**kwargs,
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)
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@classmethod
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def from_documents(
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cls,
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documents: Iterable[Document],
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embeddings: Embeddings,
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**kwargs: Any,
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) -> KNNRetriever:
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texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
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return cls.from_texts(
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texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
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)
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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query_embeds = np.array(self.embeddings.embed_query(query))
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# calc L2 norm
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index_embeds = self.index / np.sqrt((self.index**2).sum(1, keepdims=True))
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query_embeds = query_embeds / np.sqrt((query_embeds**2).sum())
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similarities = index_embeds.dot(query_embeds)
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sorted_ix = np.argsort(-similarities)
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denominator = np.max(similarities) - np.min(similarities) + 1e-6
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normalized_similarities = (similarities - np.min(similarities)) / denominator
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top_k_results = [
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Document(
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page_content=self.texts[row],
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metadata=self.metadatas[row] if self.metadatas else {},
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)
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for row in sorted_ix[0 : self.k]
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if (
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self.relevancy_threshold is None
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or normalized_similarities[row] >= self.relevancy_threshold
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)
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]
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return top_k_results
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