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* docs[minor],community[patch]: Update fireworks embeddings doc, add model param

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344 changes: 344 additions & 0 deletions docs/core_docs/docs/integrations/text_embedding/fireworks.ipynb
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{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"sidebar_label: Fireworks\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# FireworksEmbeddings\n",
"\n",
"This will help you get started with FireworksEmbeddings [embedding models](/docs/concepts#embedding-models) using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/fireworks/) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: |\n",
"| [FireworksEmbeddings](https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_embeddings_fireworks.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 Fireworks embedding models you'll need to create a Fireworks account, get an API key, and install the `@langchain/community` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"Head to [fireworks.ai](https://fireworks.ai/) to sign up to `Fireworks` and generate an API key. Once you've done this set the `FIREWORKS_API_KEY` environment variable:\n",
"\n",
"```bash\n",
"export FIREWORKS_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 `FireworksEmbeddings` 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 { FireworksEmbeddings } from \"@langchain/community/embeddings/fireworks\";\n",
"\n",
"const embeddings = new FireworksEmbeddings({\n",
" modelName: \"nomic-ai/nomic-embed-text-v1.5\",\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": [
{
"data": {
"text/plain": [
"\u001b[32m\"LangChain is the framework for building context-aware reasoning applications\"\u001b[39m"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
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"]\n"
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}
],
"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": [
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}
],
"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 FireworksEmbeddings features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_embeddings_fireworks.FireworksEmbeddings.html"
]
}
],
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29 changes: 0 additions & 29 deletions docs/core_docs/docs/integrations/text_embedding/fireworks.mdx
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