mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-28 23:07:11 +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()
```
128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
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 SVMRetriever(BaseRetriever):
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"""`SVM` 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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"""
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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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) -> SVMRetriever:
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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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) -> SVMRetriever:
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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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try:
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from sklearn import svm
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except ImportError:
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raise ImportError(
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"Could not import scikit-learn, please install with `pip install "
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"scikit-learn`."
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)
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query_embeds = np.array(self.embeddings.embed_query(query))
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x = np.concatenate([query_embeds[None, ...], self.index])
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y = np.zeros(x.shape[0])
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y[0] = 1
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clf = svm.LinearSVC(
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class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
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)
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clf.fit(x, y)
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similarities = clf.decision_function(x)
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sorted_ix = np.argsort(-similarities)
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# svm.LinearSVC in scikit-learn is non-deterministic.
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# if a text is the same as a query, there is no guarantee
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# the query will be in the first index.
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# this performs a simple swap, this works because anything
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# left of the 0 should be equivalent.
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zero_index = np.where(sorted_ix == 0)[0][0]
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if zero_index != 0:
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sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
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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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for row in sorted_ix[1 : self.k + 1]:
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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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metadata = self.metadatas[row - 1] if self.metadatas else {}
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doc = Document(page_content=self.texts[row - 1], metadata=metadata)
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top_k_results.append(doc)
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return top_k_results
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