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a6bcbd007b
This changes `lancedb` from a "pure python" setuptools project to a maturin project and adds a rust lancedb dependency. The async python client is extremely minimal (only `connect` and `Connection.table_names` are supported). The purpose of this PR is to get the infrastructure in place for building out the rest of the async client. Although this is not technically a breaking change (no APIs are changing) it is still a considerable change in the way the wheels are built because they now include the native shared library.
143 lines
5.3 KiB
Python
143 lines
5.3 KiB
Python
# Copyright (c) 2023. LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from functools import cached_property
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from typing import List, Union
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import numpy as np
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from lancedb.pydantic import PYDANTIC_VERSION
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from ..util import attempt_import_or_raise
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from .base import TextEmbeddingFunction
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from .registry import register
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from .utils import TEXT, api_key_not_found_help
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@register("gemini-text")
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class GeminiText(TextEmbeddingFunction):
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"""
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An embedding function that uses the Google's Gemini API. Requires GOOGLE_API_KEY to
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be set.
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https://ai.google.dev/docs/embeddings_guide
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Supports various tasks types:
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| Task Type | Description |
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|-------------------------|--------------------------------------------------------|
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| "`retrieval_query`" | Specifies the given text is a query in a |
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| | search/retrieval setting. |
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| "`retrieval_document`" | Specifies the given text is a document in a |
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| | search/retrieval setting. Using this task type |
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| | requires a title but is automatically provided by |
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| | Embeddings API |
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| "`semantic_similarity`" | Specifies the given text will be used for Semantic |
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| | Textual Similarity (STS). |
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| "`classification`" | Specifies that the embeddings will be used for |
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| | classification. |
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| "`clustering`" | Specifies that the embeddings will be used for |
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| | clustering. |
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Note: The supported task types might change in the Gemini API, but as long as a
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supported task type and its argument set is provided, those will be delegated
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to the API calls.
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Parameters
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----------
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name: str, default "models/embedding-001"
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The name of the model to use. See the Gemini documentation for a list of
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available models.
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query_task_type: str, default "retrieval_query"
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Sets the task type for the queries.
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source_task_type: str, default "retrieval_document"
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Sets the task type for ingestion.
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Examples
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--------
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import lancedb
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import pandas as pd
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from lancedb.pydantic import LanceModel, Vector
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from lancedb.embeddings import get_registry
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model = get_registry().get("gemini-text").create()
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class TextModel(LanceModel):
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text: str = model.SourceField()
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vector: Vector(model.ndims()) = model.VectorField()
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df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
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db = lancedb.connect("~/.lancedb")
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tbl = db.create_table("test", schema=TextModel, mode="overwrite")
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tbl.add(df)
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rs = tbl.search("hello").limit(1).to_pandas()
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"""
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name: str = "models/embedding-001"
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query_task_type: str = "retrieval_query"
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source_task_type: str = "retrieval_document"
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if PYDANTIC_VERSION < (2, 0): # Pydantic 1.x compat
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class Config:
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keep_untouched = (cached_property,)
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def ndims(self):
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# TODO: fix hardcoding
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return 768
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def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
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return self.compute_source_embeddings(query, task_type=self.query_task_type)
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def compute_source_embeddings(self, texts: TEXT, *args, **kwargs) -> List[np.array]:
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texts = self.sanitize_input(texts)
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task_type = (
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kwargs.get("task_type") or self.source_task_type
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) # assume source task type if not passed by `compute_query_embeddings`
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return self.generate_embeddings(texts, task_type=task_type)
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def generate_embeddings(
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self, texts: Union[List[str], np.ndarray], *args, **kwargs
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) -> List[np.array]:
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"""
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Get the embeddings for the given texts
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Parameters
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----------
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texts: list[str] or np.ndarray (of str)
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The texts to embed
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"""
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if (
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kwargs.get("task_type") == "retrieval_document"
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): # Provide a title to use existing API design
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title = "Embedding of a document"
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kwargs["title"] = title
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return [
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self.client.embed_content(model=self.name, content=text, **kwargs)[
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"embedding"
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]
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for text in texts
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]
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@cached_property
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def client(self):
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genai = attempt_import_or_raise("google.generativeai", "google.generativeai")
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if not os.environ.get("GOOGLE_API_KEY"):
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api_key_not_found_help("google")
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return genai
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