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cli nits and add together embeddings doc
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302 changes: 302 additions & 0 deletions docs/core_docs/docs/integrations/text_embedding/togetherai.ipynb
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{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"sidebar_label: TogetherAI\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# TogetherAIEmbeddings\n",
"\n",
"This will help you get started with TogetherAIEmbeddings [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `TogetherAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_community_embeddings_togetherai.TogetherAIEmbeddings.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/together/) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: |\n",
"| [TogetherAIEmbeddings](https://api.js.langchain.com/classes/langchain_community_embeddings_togetherai.TogetherAIEmbeddings.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_embeddings_togetherai.html) | ❌ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/community?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/community?style=flat-square&label=%20&) |\n",
"\n",
"## Setup\n",
"\n",
"To access TogetherAI embedding models you'll need to create a TogetherAI account, get an API key, and install the `@langchain/community` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"You can sign up for a Together account and create an API key [here](https://api.together.xyz/). Once you've done this set the `TOGETHER_AI_API_KEY` environment variable:\n",
"\n",
"```bash\n",
"export TOGETHER_AI_API_KEY=\"your-api-key\"\n",
"```\n",
"\n",
"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:\n",
"\n",
"```bash\n",
"# export LANGCHAIN_TRACING_V2=\"true\"\n",
"# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
"```\n",
"\n",
"### Installation\n",
"\n",
"The LangChain TogetherAIEmbeddings integration lives in the `@langchain/community` package:\n",
"\n",
"```{=mdx}\n",
"import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
"import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
"\n",
"<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
"\n",
"<Npm2Yarn>\n",
" @langchain/community\n",
"</Npm2Yarn>\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "45dd1724",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"Now we can instantiate our model object and generate chat completions:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"import { TogetherAIEmbeddings } from \"@langchain/community/embeddings/togetherai\";\n",
"\n",
"const embeddings = new TogetherAIEmbeddings({\n",
" model: \"togethercomputer/m2-bert-80M-8k-retrieval\", // Default value\n",
"});"
]
},
{
"cell_type": "markdown",
"id": "77d271b6",
"metadata": {},
"source": [
"## Indexing and Retrieval\n",
"\n",
"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",
"\n",
"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 using the demo [`MemoryVectorStore`](/docs/integrations/vectorstores/memory)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d817716b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LangChain is the framework for building context-aware reasoning applications\n"
]
}
],
"source": [
"// Create a vector store with a sample text\n",
"import { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n",
"\n",
"const text = \"LangChain is the framework for building context-aware reasoning applications\";\n",
"\n",
"const vectorstore = await MemoryVectorStore.fromDocuments(\n",
" [{ pageContent: text, metadata: {} }],\n",
" embeddings,\n",
");\n",
"\n",
"// Use the vector store as a retriever that returns a single document\n",
"const retriever = vectorstore.asRetriever(1);\n",
"\n",
"// Retrieve the most similar text\n",
"const retrievedDocuments = await retriever.invoke(\"What is LangChain?\");\n",
"\n",
"retrievedDocuments[0].pageContent;"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"source": [
"## Direct Usage\n",
"\n",
"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively.\n",
"\n",
"You can directly call these methods to get embeddings for your own use cases.\n",
"\n",
"### Embed single texts\n",
"\n",
"You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0d2befcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" 0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488,\n",
" 0.0084609175, 0.11605915, 0.05303011, 0.14711718, -0.14407106,\n",
" -0.29865336, -0.15807179, -0.068397366, -0.2708063, 0.056596708,\n",
" -0.07656515, 0.052995138, -0.11275427, 0.028096694, 0.123501234,\n",
" -0.039519835, 0.12148692, -0.12820457, 0.15691335, 0.033519063,\n",
" -0.27026987, -0.08460162, -0.23792154, -0.234982, -0.05786798,\n",
" 0.016467346, -0.17168592, -0.060787182, 0.038752213, -0.08169927,\n",
" 0.09327062, 0.29490772, 0.0167866, -0.32224452, -0.2037822,\n",
" -0.10284172, -0.124050565, 0.25344968, -0.06275548, -0.14180769,\n",
" 0.0046709594, 0.073105976, 0.12004031, 0.19224276, -0.022589967,\n",
" 0.102790825, 0.1138286, -0.057701062, -0.050010648, -0.1632584,\n",
" -0.18942119, -0.12018798, 0.15288158, 0.07941474, 0.10440051,\n",
" -0.13257962, -0.19282033, 0.044656333, 0.13560675, -0.068929024,\n",
" 0.028590716, 0.055663664, 0.04652713, 0.014936657, 0.120679885,\n",
" 0.053866718, -0.16296014, 0.119450666, -0.29559663, 0.008097747,\n",
" 0.07380408, -0.09010084, -0.0687739, -0.08575685, -0.07202606,\n",
" 0.18868081, -0.08392917, 0.014016109, 0.15435852, -0.030115498,\n",
" -0.16927013, 0.02836557, -0.050763763, 0.0840437, -0.22718845,\n",
" 0.111397505, 0.033395614, -0.123287566, -0.2111604, -0.1580479,\n",
" 0.05520573, -0.1422921, 0.08828953, 0.051058788, -0.13312188\n",
"]\n"
]
}
],
"source": [
"const singleVector = await embeddings.embedQuery(text);\n",
"\n",
"console.log(singleVector.slice(0, 100));"
]
},
{
"cell_type": "markdown",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Embed multiple texts\n",
"\n",
"You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2f4d6e97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" 0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488,\n",
" 0.0084609175, 0.11605915, 0.05303011, 0.14711718, -0.14407106,\n",
" -0.29865336, -0.15807179, -0.068397366, -0.2708063, 0.056596708,\n",
" -0.07656515, 0.052995138, -0.11275427, 0.028096694, 0.123501234,\n",
" -0.039519835, 0.12148692, -0.12820457, 0.15691335, 0.033519063,\n",
" -0.27026987, -0.08460162, -0.23792154, -0.234982, -0.05786798,\n",
" 0.016467346, -0.17168592, -0.060787182, 0.038752213, -0.08169927,\n",
" 0.09327062, 0.29490772, 0.0167866, -0.32224452, -0.2037822,\n",
" -0.10284172, -0.124050565, 0.25344968, -0.06275548, -0.14180769,\n",
" 0.0046709594, 0.073105976, 0.12004031, 0.19224276, -0.022589967,\n",
" 0.102790825, 0.1138286, -0.057701062, -0.050010648, -0.1632584,\n",
" -0.18942119, -0.12018798, 0.15288158, 0.07941474, 0.10440051,\n",
" -0.13257962, -0.19282033, 0.044656333, 0.13560675, -0.068929024,\n",
" 0.028590716, 0.055663664, 0.04652713, 0.014936657, 0.120679885,\n",
" 0.053866718, -0.16296014, 0.119450666, -0.29559663, 0.008097747,\n",
" 0.07380408, -0.09010084, -0.0687739, -0.08575685, -0.07202606,\n",
" 0.18868081, -0.08392917, 0.014016109, 0.15435852, -0.030115498,\n",
" -0.16927013, 0.02836557, -0.050763763, 0.0840437, -0.22718845,\n",
" 0.111397505, 0.033395614, -0.123287566, -0.2111604, -0.1580479,\n",
" 0.05520573, -0.1422921, 0.08828953, 0.051058788, -0.13312188\n",
"]\n",
"[\n",
" 0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017,\n",
" -0.26976448, -0.056340694, -0.26923394, 0.2548541, -0.27271318,\n",
" -0.2244126, 0.07949589, -0.27710953, -0.17993368, 0.09681616,\n",
" -0.08692256, 0.22127126, -0.14512022, -0.18016525, 0.14892976,\n",
" -0.0526347, -0.008140617, -0.2916987, 0.23706906, -0.38488507,\n",
" -0.35881752, 0.09276949, -0.07051063, -0.07778231, 0.12552947,\n",
" 0.06256748, -0.25832427, 0.025054429, -0.1451448, -0.2662871,\n",
" 0.13676351, -0.07413256, 0.14966589, -0.39968985, 0.15542287,\n",
" -0.13107607, 0.02761394, 0.108077586, -0.12076956, 0.128296,\n",
" -0.05625126, 0.15723586, -0.056932643, 0.23720805, 0.23993455,\n",
" -0.035553705, -0.053907514, -0.11852807, 0.07005695, -0.06317475,\n",
" 0.070009425, 0.284697, 0.2212059, 0.018890115, 0.16924675,\n",
" 0.21651487, 0.07259682, 0.1328156, 0.3261852, 0.1914124,\n",
" -0.10120423, 0.03450111, -0.22588971, -0.04458192, 0.24116798,\n",
" -0.021830376, -0.30731413, 0.08586451, -0.058835756, 0.0010347435,\n",
" 0.0031927782, -0.09403646, -0.22608931, 0.15865424, 0.15738021,\n",
" 0.23582733, 0.1714161, 0.1585189, -0.18085755, 0.019376995,\n",
" -0.026587496, -0.017079154, -0.04588549, -0.047336094, -0.082413346,\n",
" -0.1114185, -0.05403556, 0.12438637, -0.20476522, 0.073182,\n",
" -0.12210378, -0.010543863, -0.09767598, 0.1057683, -0.050204434\n",
"]\n"
]
}
],
"source": [
"const text2 = \"LangGraph is a library for building stateful, multi-actor applications with LLMs\";\n",
"\n",
"const vectors = await embeddings.embedDocuments([text, text2]);\n",
"\n",
"console.log(vectors[0].slice(0, 100));\n",
"console.log(vectors[1].slice(0, 100));"
]
},
{
"cell_type": "markdown",
"id": "8938e581",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all TogetherAIEmbeddings features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_embeddings_togetherai.TogetherAIEmbeddings.html"
]
}
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28 changes: 0 additions & 28 deletions docs/core_docs/docs/integrations/text_embedding/togetherai.mdx
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4 changes: 3 additions & 1 deletion libs/langchain-scripts/src/cli/docs/llms.ts
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Expand Up @@ -149,7 +149,9 @@ export async function fillLLMIntegrationDocTemplate(fields: {
)
.replace(PY_SUPPORT_PLACEHOLDER, extraFields?.pySupport ? "✅" : "❌");

const packageNameShortSnakeCase = fields.className.replace(/-/g, "_");
const packageNameShortSnakeCase = fields.className
.replace(/-/g, "_")
.toLowerCase();
const docPath = path.join(
INTEGRATIONS_DOCS_PATH,
`${packageNameShortSnakeCase}.ipynb`
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4 changes: 3 additions & 1 deletion libs/langchain-scripts/src/cli/docs/retrievers.ts
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Expand Up @@ -126,7 +126,9 @@ export async function fillRetrieverIntegrationDocTemplate(fields: {
.replaceAll(API_REF_MODULE_PLACEHOLDER, apiRefModuleUrl)
.replaceAll(PYTHON_DOC_URL_PLACEHOLDER, pyDocUrl);

const packageNameShortSnakeCase = fields.className.replace(/-/g, "_");
const packageNameShortSnakeCase = fields.className
.replace(/-/g, "_")
.toLowerCase();
const docPath = path.join(
INTEGRATIONS_DOCS_PATH,
`${packageNameShortSnakeCase}.ipynb`
Expand Down
2 changes: 1 addition & 1 deletion libs/langchain-scripts/src/cli/docs/templates/llms.ipynb
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Expand Up @@ -34,7 +34,7 @@
"\n",
"| Class | Package | Local | Serializable | [PY support](__python_doc_url__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [__module_name__](__api_ref_module__) | [__package_name__](__api_ref_package__) | __local__ | __serializable__ | __py_support__ | ![NPM - Downloads](https://img.shields.io/npm/dm/__package_name_pretty__?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/__package_name_pretty__?style=flat-square&label=%20&) |\n",
"| [__module_name__](__api_ref_module__) | [__package_name__](__api_ref_package__) | __local__ | __serializable__ | __py_support__ | ![NPM - Downloads](https://img.shields.io/npm/dm/__package_name__?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/__package_name__?style=flat-square&label=%20&) |\n",
"\n",
"## Setup\n",
"\n",
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6 changes: 3 additions & 3 deletions libs/langchain-scripts/src/cli/docs/templates/text_embedding.ipynb
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Expand Up @@ -40,7 +40,7 @@
"\n",
"- [ ] TODO: Update with relevant info.\n",
"\n",
"To access __sidebar_label__ embedding models you'll need to create a/an __module_name__ account, get an API key, and install the `__package_name__` integration package.\n",
"To access __sidebar_label__ embedding models you'll need to create a/an __sidebar_label__ account, get an API key, and install the `__package_name__` integration package.\n",
"\n",
"### Credentials\n",
"\n",
Expand Down Expand Up @@ -135,9 +135,9 @@
"const retriever = vectorstore.asRetriever(1);\n",
"\n",
"// Retrieve the most similar text\n",
"const retrievedDocument = await retriever.invoke(\"What is LangChain?\");\n",
"const retrievedDocuments = await retriever.invoke(\"What is LangChain?\");\n",
"\n",
"retrievedDocument.pageContent;"
"retrievedDocuments[0].pageContent;"
]
},
{
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