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embedding docs (#2200)
This commit is contained in:
Harrison Chase
GitHub
parent
feec4c61f4
commit
1c03205cc2
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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": "249b4058",
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"metadata": {},
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"source": [
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"# Embeddings\n",
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"\n",
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"This notebook goes over how to use the Embedding class in LangChain.\n",
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"\n",
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"The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.\n",
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"\n",
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"Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.\n",
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"\n",
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"The base Embedding class in LangChain exposes two methods: `embed_documents` and `embed_query`. The largest difference is that these two methods have different interfaces: one works over multiple documents, while the other works over a single document. Besides this, another reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "278b6c63",
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"metadata": {},
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"source": [
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"## OpenAI\n",
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"\n",
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"Let's load the OpenAI Embedding class."
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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": "0be1af71",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import OpenAIEmbeddings"
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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": "2c66e5da",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OpenAIEmbeddings()"
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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": "01370375",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": "bfb6142c",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": "0356c3b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb61bbeb",
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"metadata": {},
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"source": [
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"Let's load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see [here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)"
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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": null,
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"id": "c0b072cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings"
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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": null,
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"id": "a56b70f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OpenAIEmbeddings(model_name=\"ada\")"
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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": null,
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"id": "14aefb64",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": null,
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"id": "3c39ed33",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": null,
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"id": "e3221db6",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3852491",
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"metadata": {},
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"source": [
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"## AzureOpenAI\n",
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"\n",
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"Let's load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
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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": null,
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"id": "1b40f827",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set the environment variables needed for openai package to know to reach out to azure\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
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"os.environ[\"OPENAI_API_BASE\"] = \"https://<your-endpoint.openai.azure.com/\"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"your AzureOpenAI key\""
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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": null,
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"id": "bb36d16c",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = OpenAIEmbeddings(model=\"your-embeddings-deployment-name\")"
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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": null,
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"id": "228abcbb",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": null,
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"id": "60dd7fad",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": null,
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"id": "83bc1a72",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "42f76e43",
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"metadata": {},
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"source": [
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"## Cohere\n",
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"\n",
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"Let's load the Cohere Embedding class."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ca9e2b3a",
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"metadata": {},
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"source": []
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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": "6b82f59f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import CohereEmbeddings"
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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": "26895c60",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)"
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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": "eea52814",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": "fbe167bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": "38ad3b20",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed47bb62",
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"metadata": {},
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"source": [
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"## Hugging Face Hub\n",
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"Let's load the Hugging Face Embedding class."
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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": 7,
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"id": "861521a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceEmbeddings"
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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": 16,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = HuggingFaceEmbeddings()"
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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": 12,
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"id": "d0a98ae9",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": 13,
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"id": "5d6c682b",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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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": 14,
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"id": "bb5e74c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fff4734f",
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"metadata": {},
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"source": [
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"## TensorflowHub\n",
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"Let's load the TensorflowHub Embedding class."
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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": "f822104b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import TensorflowHubEmbeddings"
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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": "bac84e46",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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]
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}
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],
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"source": [
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"embeddings = TensorflowHubEmbeddings()"
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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": 6,
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"id": "4790d770",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": 7,
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"id": "f556dcdb",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59428e05",
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"metadata": {},
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"source": [
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"## InstructEmbeddings\n",
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"Let's load the HuggingFace instruct Embeddings class."
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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": 8,
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"id": "92c5b61e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceInstructEmbeddings"
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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": 9,
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"id": "062547b9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"load INSTRUCTOR_Transformer\n",
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"max_seq_length 512\n"
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]
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}
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],
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"source": [
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"embeddings = HuggingFaceInstructEmbeddings(\n",
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" query_instruction=\"Represent the query for retrieval: \"\n",
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")"
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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": 10,
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"id": "e1dcc4bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": 11,
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"id": "90f0db94",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eec4efda",
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"metadata": {},
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"source": [
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"## Self Hosted Embeddings\n",
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"Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes."
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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": null,
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"id": "d338722a",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import (\n",
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" SelfHostedEmbeddings,\n",
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" SelfHostedHuggingFaceEmbeddings,\n",
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" SelfHostedHuggingFaceInstructEmbeddings,\n",
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")\n",
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"import runhouse as rh"
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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": null,
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"id": "146559e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# For an on-demand A100 with GCP, Azure, or Lambda\n",
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"gpu = rh.cluster(name=\"rh-a10x\", instance_type=\"A100:1\", use_spot=False)\n",
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"\n",
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"# For an on-demand A10G with AWS (no single A100s on AWS)\n",
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"# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')\n",
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"\n",
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"# For an existing cluster\n",
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"# gpu = rh.cluster(ips=['<ip of the cluster>'],\n",
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"# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},\n",
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"# name='my-cluster')"
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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": null,
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"id": "1230f7df",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)"
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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": 6,
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"id": "2684e928",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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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": null,
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"id": "1dc5e606",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cef9cc54",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And similarly for SelfHostedHuggingFaceInstructEmbeddings:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "81a17ca3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5a33d1c8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now let's load an embedding model with a custom load function:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "c4af5679",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_pipeline():\n",
|
||||
" from transformers import (\n",
|
||||
" AutoModelForCausalLM,\n",
|
||||
" AutoTokenizer,\n",
|
||||
" pipeline,\n",
|
||||
" ) # Must be inside the function in notebooks\n",
|
||||
"\n",
|
||||
" model_id = \"facebook/bart-base\"\n",
|
||||
" tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
|
||||
" model = AutoModelForCausalLM.from_pretrained(model_id)\n",
|
||||
" return pipeline(\"feature-extraction\", model=model, tokenizer=tokenizer)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def inference_fn(pipeline, prompt):\n",
|
||||
" # Return last hidden state of the model\n",
|
||||
" if isinstance(prompt, list):\n",
|
||||
" return [emb[0][-1] for emb in pipeline(prompt)]\n",
|
||||
" return pipeline(prompt)[0][-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8654334b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = SelfHostedEmbeddings(\n",
|
||||
" model_load_fn=get_pipeline,\n",
|
||||
" hardware=gpu,\n",
|
||||
" model_reqs=[\"./\", \"torch\", \"transformers\"],\n",
|
||||
" inference_fn=inference_fn,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fc1bfd0f",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f9c02c78",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Fake Embeddings\n",
|
||||
"\n",
|
||||
"LangChain also provides a fake embedding class. You can use this to test your pipelines."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "2ffc2e4b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import FakeEmbeddings"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "80777571",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = FakeEmbeddings(size=1352)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "3ec9d8f0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(\"foo\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "3b9ae9e1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_results = embeddings.embed_documents([\"foo\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1f83f273",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## SageMaker Endpoint Embeddings\n",
|
||||
"\n",
|
||||
"Let's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.\n",
|
||||
"\n",
|
||||
"For instrucstions on how to do this, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "88d366bd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip3 install langchain boto3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "1e9b926a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Dict\n",
|
||||
"from langchain.embeddings import SagemakerEndpointEmbeddings\n",
|
||||
"from langchain.llms.sagemaker_endpoint import ContentHandlerBase\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class ContentHandler(ContentHandlerBase):\n",
|
||||
" content_type = \"application/json\"\n",
|
||||
" accepts = \"application/json\"\n",
|
||||
"\n",
|
||||
" def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
|
||||
" input_str = json.dumps({\"inputs\": prompt, **model_kwargs})\n",
|
||||
" return input_str.encode('utf-8')\n",
|
||||
" \n",
|
||||
" def transform_output(self, output: bytes) -> str:\n",
|
||||
" response_json = json.loads(output.read().decode(\"utf-8\"))\n",
|
||||
" return response_json[\"embeddings\"]\n",
|
||||
"\n",
|
||||
"content_handler = ContentHandler()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"embeddings = SagemakerEndpointEmbeddings(\n",
|
||||
" # endpoint_name=\"endpoint-name\", \n",
|
||||
" # credentials_profile_name=\"credentials-profile-name\", \n",
|
||||
" endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\", \n",
|
||||
" region_name=\"us-east-1\", \n",
|
||||
" content_handler=content_handler\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fe9797b8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(\"foo\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "76f1b752",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_results = embeddings.embed_documents([\"foo\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fff99b21",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "eb1c0ea9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Aleph Alpha\n",
|
||||
"\n",
|
||||
"There are two possible ways to use Aleph Alpha's semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric embeddings are the suggested approach."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9ecc84f9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Asymmetric"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8a920a89",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import AlephAlphaAsymmetricSemanticEmbedding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "f2d04da3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"document = \"This is a content of the document\"\n",
|
||||
"query = \"What is the contnt of the document?\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e6ecde96",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = AlephAlphaAsymmetricSemanticEmbedding()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "90e68411",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_result = embeddings.embed_documents([document])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "55903233",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(query)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8c00aab",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Symmetric"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "eabb763a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "0ad799f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"text = \"This is a test text\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "af86dc10",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = AlephAlphaSymmetricSemanticEmbedding()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d292536f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_result = embeddings.embed_documents([text])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c704a7cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33492471",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.13"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
165
docs/modules/models/text_embedding/examples/aleph_alpha.ipynb
Normal file
165
docs/modules/models/text_embedding/examples/aleph_alpha.ipynb
Normal file
@ -0,0 +1,165 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "eb1c0ea9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Aleph Alpha\n",
|
||||
"\n",
|
||||
"There are two possible ways to use Aleph Alpha's semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric embeddings are the suggested approach."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9ecc84f9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Asymmetric"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8a920a89",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import AlephAlphaAsymmetricSemanticEmbedding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "f2d04da3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"document = \"This is a content of the document\"\n",
|
||||
"query = \"What is the contnt of the document?\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e6ecde96",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = AlephAlphaAsymmetricSemanticEmbedding()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "90e68411",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_result = embeddings.embed_documents([document])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "55903233",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(query)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b8c00aab",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Symmetric"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "eabb763a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "0ad799f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"text = \"This is a test text\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "af86dc10",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = AlephAlphaSymmetricSemanticEmbedding()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d292536f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc_result = embeddings.embed_documents([text])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c704a7cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33492471",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user