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fdba711d28
Updated `integrations/embeddings`: fixed titles; added links, descriptions Updated `integrations/providers`.
112 lines
2.4 KiB
Plaintext
112 lines
2.4 KiB
Plaintext
{
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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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"# AwaDB\n",
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"\n",
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">[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
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"\n",
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"This notebook explains how to use `AwaEmbeddings` 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": "3.10.12"
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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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