mirror of https://github.com/hwchase17/langchain
Add embeddings for AwaEmbedding (#8353)
- Description: Adds AwaEmbeddings class for embeddings, which provides users with a convenient way to do fine-tuning, as well as the potential need for multimodality - Tag maintainer: @baskaryan Create `Awa.ipynb`: an example notebook for AwaEmbeddings class Modify `embeddings/__init__.py`: Import the class Create `embeddings/awa.py`: The embedding class Create `embeddings/test_awa.py`: The test file. --------- Co-authored-by: taozhiwang <taozhiwa@gmail.com>pull/8038/head
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b14a24db",
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"metadata": {},
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"source": [
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"# AwaEmbedding\n",
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"\n",
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"This notebook explains how to use AwaEmbedding, which is included in [awadb](https://github.com/awa-ai/awadb), to embedding texts in langchain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0ab948fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install awadb"
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]
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},
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{
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"cell_type": "markdown",
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"id": "67c637ca",
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"metadata": {},
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"source": [
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"## import the library"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5709b030",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import AwaEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1756b1ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"Embedding = AwaEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4a2a098d",
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"metadata": {},
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"source": [
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"# Set embedding model\n",
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"Users can use `Embedding.set_model()` to specify the embedding model. \\\n",
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"The input of this function is a string which represents the model's name. \\\n",
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"The list of currently supported models can be obtained [here](https://github.com/awa-ai/awadb) \\ \\ \n",
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"\n",
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"The **default model** is `all-mpnet-base-v2`, it can be used without setting."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "584b9af5",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"our embedding test\"\n",
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"\n",
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"Embedding.set_model(\"all-mpnet-base-v2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "be18b873",
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"metadata": {},
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"outputs": [],
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"source": [
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"res_query = Embedding.embed_query(\"The test information\")\n",
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"res_document = Embedding.embed_documents([\"test1\", \"another test\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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from typing import Any, Dict, List
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from pydantic import BaseModel, root_validator
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from langchain.embeddings.base import Embeddings
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class AwaEmbeddings(BaseModel, Embeddings):
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client: Any #: :meta private:
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model: str = "all-mpnet-base-v2"
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that awadb library is installed."""
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try:
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from awadb import AwaEmbedding
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except ImportError as exc:
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raise ImportError(
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"Could not import awadb library. "
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"Please install it with `pip install awadb`"
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) from exc
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values["client"] = AwaEmbedding()
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return values
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def set_model(self, model_name: str) -> None:
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"""Set the model used for embedding.
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The default model used is all-mpnet-base-v2
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Args:
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model_name: A string which represents the name of model.
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"""
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self.model = model_name
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self.client.model_name = model_name
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using AwaEmbedding.
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Args:
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texts: The list of texts need to be embedded
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Returns:
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List of embeddings, one for each text.
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"""
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return self.client.EmbeddingBatch(texts)
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using AwaEmbedding.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self.client.Embedding(text)
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"""Test Awa Embedding"""
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from langchain.embeddings.awa import AwaEmbeddings
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def test_awa_embedding_documents() -> None:
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"""Test Awa embeddings for documents."""
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documents = ["foo bar", "test document"]
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embedding = AwaEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 2
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assert len(output[0]) == 768
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def test_awa_embedding_query() -> None:
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"""Test Awa embeddings for query."""
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document = "foo bar"
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embedding = AwaEmbeddings()
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output = embedding.embed_query(document)
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assert len(output) == 768
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