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https://github.com/hwchase17/langchain
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docs: together ai embeddings integration docs (#25252)
Update together AI embedding integration docs Related issue: https://github.com/langchain-ai/langchain/issues/24856 ```json [ { "provider": "together", "js": true, "local": false, "serializable": false, } ] ``` --------- Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
This commit is contained in:
Eugene Yurtsev
GitHub
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8645a49f31
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# TogetherEmbeddings\n",
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"\n",
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"This notebook covers how to get started with open source embedding models hosted in the Together AI API.\n",
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"This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
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"\n",
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"## Installation"
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"import { ItemTable } from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"text_embedding\" item=\"Together\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Together embedding models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Head to [https://api.together.xyz/](https://api.together.xyz/) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
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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": "4c3bef91",
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"execution_count": 1,
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"id": "36521c2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# install package\n",
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"%pip install --upgrade --quiet langchain-together"
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"TOGETHER_API_KEY\"):\n",
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" os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"\n",
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"Make sure to set the following environment variables:\n",
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"\n",
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"- `TOGETHER_API_KEY`\n",
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"\n",
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"## Usage\n",
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"\n",
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"First, select a supported model from [this list](https://docs.together.ai/docs/embedding-models). In the following example, we will use `togethercomputer/m2-bert-80M-8k-retrieval`."
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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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": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"execution_count": 2,
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"id": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_together.embeddings import TogetherEmbeddings\n",
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")"
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"The LangChain Together integration lives in the `langchain-together` package:"
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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": "12fcfb4b",
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"execution_count": 3,
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"id": "64853226",
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"metadata": {},
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"outputs": [],
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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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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"embeddings.embed_query(\"My query to look up\")"
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"%pip install -qU langchain-together"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45dd1724",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:"
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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": "1f2e6104",
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"execution_count": 5,
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"id": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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"from langchain_together import TogetherEmbeddings\n",
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"\n",
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"embeddings = TogetherEmbeddings(\n",
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" model=\"togethercomputer/m2-bert-80M-8k-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": null,
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"id": "46739f68",
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"cell_type": "markdown",
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"id": "77d271b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# async embed query\n",
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"await embeddings.aembed_query(\"My query to look up\")"
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"## Indexing and Retrieval\n",
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"\n",
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"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
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"\n",
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"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
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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": "e48632ea",
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"execution_count": 6,
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"id": "d817716b",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# async embed documents\n",
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"await embeddings.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_documents[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `embed_query`:"
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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": "0d2befcd",
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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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"[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n"
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]
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}
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],
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"source": [
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `embed_documents`:"
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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": "2f4d6e97",
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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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"[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n",
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"[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394\n"
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]
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}
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],
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"source": [
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_documents([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98785c12",
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"metadata": {},
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"source": [
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"## API Reference\n",
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"\n",
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"For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
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]
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}
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],
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@ -126,7 +269,7 @@
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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": "3.10.5"
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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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