80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
"""Google PaLM embeddings file."""
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from typing import Any, List, Optional
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from llama_index.bridge.pydantic import PrivateAttr
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from llama_index.callbacks.base import CallbackManager
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from llama_index.core.embeddings.base import DEFAULT_EMBED_BATCH_SIZE, BaseEmbedding
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class GooglePaLMEmbedding(BaseEmbedding):
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"""Class for Google PaLM embeddings.
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Args:
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model_name (str): Model for embedding.
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Defaults to "models/embedding-gecko-001".
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api_key (Optional[str]): API key to access the model. Defaults to None.
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"""
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_model: Any = PrivateAttr()
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def __init__(
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self,
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model_name: str = "models/embedding-gecko-001",
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api_key: Optional[str] = None,
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embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
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callback_manager: Optional[CallbackManager] = None,
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**kwargs: Any,
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):
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try:
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import google.generativeai as palm
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except ImportError:
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raise ImportError(
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"google-generativeai package not found, install with"
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"'pip install google-generativeai'"
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)
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palm.configure(api_key=api_key)
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self._model = palm
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super().__init__(
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model_name=model_name,
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embed_batch_size=embed_batch_size,
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callback_manager=callback_manager,
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**kwargs,
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)
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@classmethod
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def class_name(cls) -> str:
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return "PaLMEmbedding"
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def _get_query_embedding(self, query: str) -> List[float]:
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"""Get query embedding."""
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return self._model.generate_embeddings(model=self.model_name, text=query)[
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"embedding"
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]
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async def _aget_query_embedding(self, query: str) -> List[float]:
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"""The asynchronous version of _get_query_embedding."""
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return await self._model.aget_embedding(query)
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def _get_text_embedding(self, text: str) -> List[float]:
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"""Get text embedding."""
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return self._model.generate_embeddings(model=self.model_name, text=text)[
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"embedding"
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]
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async def _aget_text_embedding(self, text: str) -> List[float]:
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"""Asynchronously get text embedding."""
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return self._model._get_text_embedding(text)
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def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
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"""Get text embeddings."""
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return self._model.generate_embeddings(model=self.model_name, text=texts)[
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"embedding"
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]
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async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
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"""Asynchronously get text embeddings."""
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return await self._model._get_embeddings(texts)
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