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feat(python): Add gemini text embedding function (#806)
Named it Gemini-text for now. Not sure how complicated it will be to support both text and multimodal embeddings under the same class "gemini"..But its not something to worry about for now I guess.
This commit is contained in:
Ayush Chaurasia
committed by
Weston Pace
Weston Pace
parent
f0a654036e
commit
2f72d5138e
@@ -118,6 +118,42 @@ texts = [{"text": "Capitalism has been dominant in the Western world since the e
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tbl.add(texts)
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```
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## Gemini Embedding Function
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With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide.
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The Gemini Embedding Model API supports various task 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 search/retrieval setting. |
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| "`retrieval_document`" | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API |
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| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS). |
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| "`classification`" | Specifies that the embeddings will be used for classification. |
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| "`clusering`" | Specifies that the embeddings will be used for clustering. |
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Usage Example:
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```python
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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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## Multi-modal embedding functions
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Multi-modal embedding functions allow you to query your table using both images and text.
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@@ -19,4 +19,5 @@ from .open_clip import OpenClipEmbeddings
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from .openai import OpenAIEmbeddings
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from .registry import EmbeddingFunctionRegistry, get_registry
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from .sentence_transformers import SentenceTransformerEmbeddings
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from .gemini_text import GeminiText
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from .utils import with_embeddings
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128
python/lancedb/embeddings/gemini_text.py
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128
python/lancedb/embeddings/gemini_text.py
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@@ -0,0 +1,128 @@
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# 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, Any
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import numpy as np
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from .base import TextEmbeddingFunction
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from .registry import register
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from .utils import api_key_not_found_help, TEXT
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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 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 search/retrieval setting. |
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| "`retrieval_document`" | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically proided by Embeddings API |
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| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS). |
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| "`classification`" | Specifies that the embeddings will be used for classification. |
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| "`clusering`" | Specifies that the embeddings will be used for clustering. |
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Note: The supported task types might change in the Gemini API, but as long as a supported task type and its argument set is provided,
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those will be delegated 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 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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class Config: # Pydantic 1.x compat
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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 = self.safe_import("google.generativeai", "google.generativeai")
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if not os.environ.get("GOOGLE_API_KEY"):
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raise ValueError(api_key_not_found_help("google"))
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return genai
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@@ -89,7 +89,7 @@ def test_openclip(tmp_path):
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db = lancedb.connect(tmp_path)
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registry = get_registry()
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func = registry.get("open-clip").create()
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func = registry.get("open-clip").create(max_retries=0)
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class Images(LanceModel):
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label: str
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@@ -170,7 +170,7 @@ def test_cohere_embedding_function():
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@pytest.mark.slow
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def test_instructor_embedding(tmp_path):
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model = get_registry().get("instructor").create()
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model = get_registry().get("instructor").create(max_retries=0)
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class TextModel(LanceModel):
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text: str = model.SourceField()
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@@ -182,3 +182,23 @@ def test_instructor_embedding(tmp_path):
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tbl.add(df)
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assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
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@pytest.mark.slow
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@pytest.mark.skipif(
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os.environ.get("GOOGLE_API_KEY") is None, reason="GOOGLE_API_KEY not set"
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)
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def test_gemini_embedding(tmp_path):
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model = get_registry().get("gemini-text").create(max_retries=0)
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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(tmp_path)
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tbl = db.create_table("test", schema=TextModel, mode="overwrite")
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tbl.add(df)
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assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
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assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
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