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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) | ❌ | ✅ |  |  |\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": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "1:37 - Cannot find module '@langchain/community/embeddings/fireworks' or its corresponding type declarations.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "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": null, | ||
| "id": "d817716b", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "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": null, | ||
| "id": "0d2befcd", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "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": null, | ||
| "id": "2f4d6e97", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "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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docs/core_docs/docs/integrations/text_embedding/fireworks.mdx
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