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"""PaLM embeddings."""
from typing import TYPE_CHECKING, Iterable, cast
import numpy as np
from tenacity import retry, stop_after_attempt, wait_random_exponential
from typing_extensions import override
from ..config import env
from ..schema import Item, RichData
from ..signals.signal import TextEmbeddingSignal
from ..signals.splitters.chunk_splitter import split_text
from .embedding import compute_split_embeddings
if TYPE_CHECKING:
import google.generativeai as palm
PALM_BATCH_SIZE = 1 # PaLM API only supports batch size 1.
NUM_PARALLEL_REQUESTS = 256 # Because batch size is 1, we can send many requests in parallel.
EMBEDDING_MODEL = 'models/embedding-gecko-001'
class PaLM(TextEmbeddingSignal):
"""Computes embeddings using PaLM's embedding API.
<br>**Important**: This will send data to an external server!
<br>To use this signal, you must get a PaLM API key from
[makersuite.google.com](https://makersuite.google.com/app/apikey) and add it to your .env.local.
"""
name = 'palm'
display_name = 'PaLM Embeddings'
_model: 'palm'
@override
def setup(self) -> None:
api_key = env('PALM_API_KEY')
if not api_key:
raise ValueError('`PALM_API_KEY` environment variable not set.')
try:
import google.generativeai as palm
palm.configure(api_key=api_key)
self._model = palm
except ImportError:
raise ImportError('Could not import the "google.generativeai" python package. '
'Please install it with `pip install google-generativeai`.')
@override
def compute(self, docs: Iterable[RichData]) -> Iterable[Item]:
"""Compute embeddings for the given documents."""
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10))
def embed_fn(texts: list[str]) -> list[np.ndarray]:
assert len(texts) == 1, 'PaLM API only supports batch size 1.'
response = self._model.generate_embeddings(model=EMBEDDING_MODEL, text=texts[0])
return [np.array(response['embedding'], dtype=np.float32)]
docs = cast(Iterable[str], docs)
split_fn = split_text if self._split else None
yield from compute_split_embeddings(
docs, PALM_BATCH_SIZE, embed_fn, split_fn, num_parallel_requests=NUM_PARALLEL_REQUESTS)
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