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https://github.com/hwchase17/langchain.git
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Docs: Add sambanova cloud embeddings docs (#30525)
- **Description:** Add samba nova cloud embeddings docs, only samabastudio embeddings were supported, now in the latest release of langchan_sambanova sambanova cloud embeddings is also available
This commit is contained in:
Jorge Piedrahita Ortiz
GitHub
parent
f4d1df1b2d
commit
b9e19c5f97
@ -114,6 +114,25 @@
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"## Embedding Models"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_sambanova import SambaNovaCloudEmbeddings\n",
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"\n",
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"embeddings = SambaNovaCloudEmbeddings(model=\"E5-Mistral-7B-Instruct\")\n",
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"embeddings.embed_query(\"What is the meaning of life?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambanova/)"
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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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@ -130,7 +149,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambanova/)"
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"For a more detailed walkthrough of the SambaStudioEmbeddings component, see [this notebook](https://python.langchain.com/docs/integrations/text_embedding/sambastudio/)"
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]
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},
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{
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@ -10,7 +10,7 @@
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},
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"source": [
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"---\n",
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"sidebar_label: SambaStudio\n",
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"sidebar_label: SambaNovaCloud\n",
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"---"
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]
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},
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@ -19,11 +19,11 @@
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# SambaStudioEmbeddings\n",
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"# SambaNovaCloudEmbeddings\n",
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"\n",
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"This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on `SambaStudioEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
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"This will help you getting started with SambaNovaCloud embedding models using LangChain. For detailed documentation on `SambaNovaCloudEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
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"\n",
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"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
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"**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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@ -34,7 +34,7 @@
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"\n",
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"## Setup\n",
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"\n",
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"To access ChatSambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
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"To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_sambanova` integration package.\n",
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"\n",
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"```bash\n",
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"pip install langchain-sambanova\n",
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@ -42,11 +42,10 @@
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"\n",
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"### Credentials\n",
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"\n",
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"Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
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"Get an API Key from [cloud.sambanova.ai](https://cloud.sambanova.ai/apis) and add it to your environment variables:\n",
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"\n",
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"``` bash\n",
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"export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
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"export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\n",
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"export SAMBANOVA_API_KEY=\"your-api-key-here\"\n",
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"```"
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]
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},
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@ -60,10 +59,8 @@
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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(\"SAMBASTUDIO_API_KEY\"):\n",
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" os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
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" \"Enter your SambaNova API key: \"\n",
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" )"
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"if not os.getenv(\"SAMBANOVA_API_KEY\"):\n",
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" os.environ[\"SAMBANOVA_API_KEY\"] = getpass.getpass(\"Enter your SambaNova API key: \")"
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]
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},
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{
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@ -122,10 +119,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_sambanova import SambaStudioEmbeddings\n",
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"from langchain_sambanova import SambaNovaCloudEmbeddings\n",
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"\n",
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"embeddings = SambaStudioEmbeddings(\n",
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" model=\"e5-mistral-7b-instruct\",\n",
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"embeddings = SambaNovaCloudEmbeddings(\n",
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" model=\"E5-Mistral-7B-Instruct\",\n",
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")"
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]
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},
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@ -227,7 +224,7 @@
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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 `SambaStudio` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html).\n"
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"For detailed documentation on `SambaNovaCloud` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n"
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]
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}
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],
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260
docs/docs/integrations/text_embedding/sambastudio.ipynb
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260
docs/docs/integrations/text_embedding/sambastudio.ipynb
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@ -0,0 +1,260 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"---\n",
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"sidebar_label: SambaStudio\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# SambaStudioEmbeddings\n",
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"\n",
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"This will help you get started with SambaNova's SambaStudio embedding models using LangChain. For detailed documentation on `SambaStudioEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
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"\n",
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"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform for running your own open-source models\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"| Provider | Package |\n",
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"|:--------:|:-------:|\n",
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"| [SambaNova](/docs/integrations/providers/sambanova/) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) |\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access SambaStudio models you will need to [deploy an endpoint](https://docs.sambanova.ai/sambastudio/latest/language-models.html) in your SambaStudio platform, install the `langchain_sambanova` integration package.\n",
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"\n",
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"```bash\n",
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"pip install langchain-sambanova\n",
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"```\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Get the URL and API Key from your SambaStudio deployed endpoint and add them to your environment variables:\n",
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"\n",
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"``` bash\n",
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"export SAMBASTUDIO_URL=\"sambastudio-url-key-here\"\n",
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"export SAMBASTUDIO_API_KEY=\"your-api-key-here\"\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": "36521c2a",
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"metadata": {},
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"outputs": [],
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"source": [
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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(\"SAMBASTUDIO_URL\"):\n",
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" os.environ[\"SAMBASTUDIO_URL\"] = getpass.getpass(\n",
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" \"Enter your SambaStudio endpoint URL: \"\n",
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" )\n",
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"\n",
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"if not os.getenv(\"SAMBASTUDIO_API_KEY\"):\n",
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" os.environ[\"SAMBASTUDIO_API_KEY\"] = getpass.getpass(\n",
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" \"Enter your SambaStudio API key: \"\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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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": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"The LangChain SambaNova integration lives in the `langchain-sambanova` 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": "64853226",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-sambanova"
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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": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_sambanova import SambaStudioEmbeddings\n",
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"\n",
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"embeddings = SambaStudioEmbeddings(\n",
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" model=\"e5-mistral-7b-instruct\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "77d271b6",
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"metadata": {},
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"source": [
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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](/docs/tutorials/).\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": "d817716b",
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"metadata": {},
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"outputs": [],
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"source": [
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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": null,
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [],
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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": null,
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [],
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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 `SambaStudio` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/sambastudio/latest/api-ref-landing.html).\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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