langchain/docs/docs/integrations/llms/sambanova.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sambanova\n",
    "\n",
    "**[Sambanova](https://sambanova.ai/)'s** [Sambaverse](https://sambaverse.sambanova.ai/) and [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) are platforms for running your own open source models\n",
    "\n",
    "This example goes over how to use LangChain to interact with Sambanova models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sambaverse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sambaverse** allows you to interact with multiple Open source models you can se the list of available models an interact with then in the [playground](https://sambaverse.sambanova.ai/playground)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An API key is required to access to Sambaverse models get one creating an account in [sambaverse.sambanova.ai](https://sambaverse.sambanova.ai/)\n",
    "\n",
    "The [sseclient-py](https://pypi.org/project/sseclient-py/) package is required to run streaming predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --quiet sseclient-py==1.8.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Register your API Key environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "sambaverse_api_key = \"<Your sambaverse API key>\"\n",
    "\n",
    "# Set the environment variables\n",
    "os.environ[\"SAMBAVERSE_API_KEY\"] = sambaverse_api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call Sambaverse models directly from langchain!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms.sambanova import Sambaverse\n",
    "\n",
    "llm = Sambaverse(\n",
    "    sambaverse_model_name=\"Meta/llama-2-7b-chat-hf\",\n",
    "    streaming=False,\n",
    "    model_kwargs={\n",
    "        \"do_sample\": True,\n",
    "        \"max_tokens_to_generate\": 1000,\n",
    "        \"temperature\": 0.01,\n",
    "        \"process_prompt\": True,\n",
    "        \"select_expert\": \"llama-2-7b-chat-hf\",\n",
    "        # \"repetition_penalty\": {\"type\": \"float\", \"value\": \"1\"},\n",
    "        # \"top_k\": {\"type\": \"int\", \"value\": \"50\"},\n",
    "        # \"top_p\": {\"type\": \"float\", \"value\": \"1\"}\n",
    "    },\n",
    ")\n",
    "\n",
    "print(llm.invoke(\"Why should I use open source models?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SambaStudio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SambaStudio** allows you to Train, run batch inference jous, and deploy online inference endpoints to run your own fine tunned open source models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A SambaStudio environment is required to deploy a model. Get more information in [sambanova.ai/products/enterprise-ai-platform-sambanova-suite](https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite)\n",
    "\n",
    "The [sseclient-py](https://pypi.org/project/sseclient-py/) package is required to run streaming predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --quiet sseclient-py==1.8.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Register your environment variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "sambastudio_base_url = \"<Your SambaStudio environment URL>\"\n",
    "sambastudio_project_id = \"<Your SambaStudio project id>\"\n",
    "sambastudio_endpoint_id = \"<Your SambaStudio endpoint id>\"\n",
    "sambastudio_api_key = \"<Your SambaStudio endpoint API key>\"\n",
    "\n",
    "# Set the environment variables\n",
    "os.environ[\"SAMBASTUDIO_BASE_URL\"] = sambastudio_base_url\n",
    "os.environ[\"SAMBASTUDIO_PROJECT_ID\"] = sambastudio_project_id\n",
    "os.environ[\"SAMBASTUDIO_ENDPOINT_ID\"] = sambastudio_endpoint_id\n",
    "os.environ[\"SAMBASTUDIO_API_KEY\"] = sambastudio_api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call SambaStudio models directly from langchain!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.llms.sambanova import SambaStudio\n",
    "\n",
    "llm = SambaStudio(\n",
    "    streaming=False,\n",
    "    model_kwargs={\n",
    "        \"do_sample\": True,\n",
    "        \"max_tokens_to_generate\": 1000,\n",
    "        \"temperature\": 0.01,\n",
    "        # \"repetition_penalty\": {\"type\": \"float\", \"value\": \"1\"},\n",
    "        # \"top_k\": {\"type\": \"int\", \"value\": \"50\"},\n",
    "        # \"top_logprobs\": {\"type\": \"int\", \"value\": \"0\"},\n",
    "        # \"top_p\": {\"type\": \"float\", \"value\": \"1\"}\n",
    "    },\n",
    ")\n",
    "\n",
    "print(llm.invoke(\"Why should I use open source models?\"))"
   ]
  }
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