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docs: Update nomic AI embeddings integration docs #25308

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docs: Update nomic AI embeddings integration docs #25308

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Merge branch 'master' into eugene/add_integraiton_odcs_nomic
isahers1 Aug 13, 2024
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235 changes: 184 additions & 51 deletions docs/docs/integrations/text_embedding/nomic.ipynb
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Expand Up @@ -12,121 +12,254 @@
},
{
"cell_type": "markdown",
"id": "e49f1e0d",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# NomicEmbeddings\n",
"\n",
"This notebook covers how to get started with Nomic embedding models.\n",
"This will help you get started with Nomic embedding models using LangChain. For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n",
"\n",
"## Installation"
"## Overview\n",
"### Integration details\n",
"\n",
"import { ItemTable } from \"@theme/FeatureTables\";\n",
"\n",
"<ItemTable category=\"text_embedding\" item=\"Nomic\" />\n",
"\n",
"## Setup\n",
"\n",
"To access Nomic embedding models you'll need to create a/an Nomic account, get an API key, and install the `langchain-nomic` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"Head to [https://atlas.nomic.ai/](https://atlas.nomic.ai/) to sign up to Nomic and generate an API key. Once you've done this set the `NOMIC_API_KEY` environment variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"execution_count": 2,
"id": "36521c2a",
"metadata": {},
"outputs": [],
"source": [
"# install package\n",
"!pip install -U langchain-nomic"
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"NOMIC_API_KEY\"):\n",
" os.environ[\"NOMIC_API_KEY\"] = getpass.getpass(\"Enter your Nomic API key: \")"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"id": "c84fb993",
"metadata": {},
"source": [
"## Environment Setup\n",
"\n",
"Make sure to set the following environment variables:\n",
"\n",
"- `NOMIC_API_KEY`\n",
"\n",
"## Usage"
"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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"execution_count": 3,
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_nomic.embeddings import NomicEmbeddings\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "d9664366",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\")"
"The LangChain Nomic integration lives in the `langchain-nomic` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12fcfb4b",
"execution_count": 2,
"id": "64853226",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"embeddings.embed_query(\"My query to look up\")"
"%pip install -qU langchain-nomic"
]
},
{
"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": null,
"id": "1f2e6104",
"execution_count": 10,
"id": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"embeddings.embed_documents(\n",
" [\"This is a content of the document\", \"This is another document\"]\n",
"from langchain_nomic import NomicEmbeddings\n",
"\n",
"embeddings = NomicEmbeddings(\n",
" model=\"nomic-embed-text-v1.5\",\n",
" # dimensionality=256,\n",
" # Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka)\n",
" # to enable variable-length embeddings with a single model.\n",
" # This means that you can specify the dimensionality of the embeddings at inference time.\n",
" # The model supports dimensionality from 64 to 768.\n",
" # inference_mode=\"remote\",\n",
" # One of `remote`, `local` (Embed4All), or `dynamic` (automatic). Defaults to `remote`.\n",
" # api_key=... , # if using remote inference,\n",
" # device=\"cpu\",\n",
" # The device to use for local embeddings. Choices include\n",
" # `cpu`, `gpu`, `nvidia`, `amd`, or a specific device name. See\n",
" # the docstring for `GPT4All.__init__` for more info. Typically\n",
" # defaults to CPU. Do not use on macOS.\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 in the `InMemoryVectorStore`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46739f68",
"execution_count": 5,
"id": "d817716b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'LangChain is the framework for building context-aware reasoning applications'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n",
"\n",
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
"\n",
"vectorstore = InMemoryVectorStore.from_texts(\n",
" [text],\n",
" embedding=embeddings,\n",
")\n",
"\n",
"# Use the vectorstore as a retriever\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_documents[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"outputs": [],
"source": [
"# async embed query\n",
"await embeddings.aembed_query(\"My query to look up\")"
"## Direct Usage\n",
"\n",
"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",
"\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 single texts or documents with `embed_query`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e48632ea",
"execution_count": 6,
"id": "0d2befcd",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.024642944, 0.029083252, -0.14013672, -0.09082031, 0.058898926, -0.07489014, -0.0138168335, 0.0037\n"
]
}
],
"source": [
"# async embed documents\n",
"await embeddings.aembed_documents(\n",
" [\"This is a content of the document\", \"This is another document\"]\n",
")"
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "7a331dc3",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Custom Dimensionality\n",
"### Embed multiple texts\n",
"\n",
"Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka) to enable variable-length embeddings with a single model. This means that you can specify the dimensionality of the embeddings at inference time. The model supports dimensionality from 64 to 768."
"You can embed multiple texts with `embed_documents`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "993f65c8",
"execution_count": 7,
"id": "2f4d6e97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.012771606, 0.023727417, -0.12365723, -0.083740234, 0.06530762, -0.07110596, -0.021896362, -0.0068\n",
"[-0.019058228, 0.04058838, -0.15222168, -0.06842041, -0.012130737, -0.07128906, -0.04534912, 0.00522\n"
]
}
],
"source": [
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_documents([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "98785c12",
"metadata": {},
"outputs": [],
"source": [
"embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\", dimensionality=256)\n",
"## API Reference\n",
"\n",
"embeddings.embed_query(\"My query to look up\")"
"For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n"
]
}
],
Expand All @@ -146,7 +279,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
"version": "3.9.6"
}
},
"nbformat": 4,
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6 changes: 6 additions & 0 deletions docs/src/theme/FeatureTables.js
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Expand Up @@ -340,6 +340,12 @@ const FEATURE_TABLES = {
package: "langchain-cohere",
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html#langchain_cohere.embeddings.CohereEmbeddings"
},
{
name: "Nomic",
link: "cohere",
package: "langchain-nomic",
apiLink: "https://api.python.langchain.com/en/latest/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html#langchain_nomic.embeddings.NomicEmbeddings"
},
]
},
document_retrievers: {
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