mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
a70b7a688e
Added missed docstrings. Format docstrings to the consistent format (used in the API Reference)
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
from itertools import islice
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from typing import Any, Iterator, List, Optional
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from ai21.models import EmbedType
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from langchain_core.embeddings import Embeddings
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from langchain_ai21.ai21_base import AI21Base
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_DEFAULT_BATCH_SIZE = 128
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def _split_texts_into_batches(texts: List[str], batch_size: int) -> Iterator[List[str]]:
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texts_itr = iter(texts)
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return iter(lambda: list(islice(texts_itr, batch_size)), [])
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class AI21Embeddings(Embeddings, AI21Base):
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"""AI21 embedding model.
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To use, you should have the 'AI21_API_KEY' environment variable set
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or pass as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain_ai21 import AI21Embeddings
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embeddings = AI21Embeddings()
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query_result = embeddings.embed_query("Hello embeddings world!")
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"""
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batch_size: int = _DEFAULT_BATCH_SIZE
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"""Maximum number of texts to embed in each batch"""
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def embed_documents(
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self,
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texts: List[str],
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*,
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batch_size: Optional[int] = None,
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**kwargs: Any,
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) -> List[List[float]]:
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"""Embed search docs."""
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return self._send_embeddings(
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texts=texts,
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batch_size=batch_size or self.batch_size,
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embed_type=EmbedType.SEGMENT,
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**kwargs,
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)
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def embed_query(
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self,
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text: str,
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*,
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batch_size: Optional[int] = None,
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**kwargs: Any,
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) -> List[float]:
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"""Embed query text."""
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return self._send_embeddings(
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texts=[text],
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batch_size=batch_size or self.batch_size,
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embed_type=EmbedType.QUERY,
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**kwargs,
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)[0]
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def _send_embeddings(
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self, texts: List[str], *, batch_size: int, embed_type: EmbedType, **kwargs: Any
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) -> List[List[float]]:
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chunks = _split_texts_into_batches(texts, batch_size)
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responses = [
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self.client.embed.create(
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texts=chunk,
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type=embed_type,
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**kwargs,
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)
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for chunk in chunks
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]
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return [
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result.embedding for response in responses for result in response.results
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]
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