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Added support for Nebula Chat model (#21925)
Description: Added support for Nebula Chat model in addition to Nebula Instruct Dependencies: N/A Twitter handle: @Symbldotai --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Sharmistha S. Gupta
GitHub
parent
080741d336
commit
90439b12f6
297
docs/docs/integrations/chat/symblai_nebula.ipynb
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297
docs/docs/integrations/chat/symblai_nebula.ipynb
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@ -0,0 +1,297 @@
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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": "53fbf15f",
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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: Nebula (Symbl.ai)\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": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# Nebula (Symbl.ai)\n",
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"\n",
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"## Overview\n",
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"This notebook covers how to get started with [Nebula](https://docs.symbl.ai/docs/nebula-llm) - Symbl.ai's chat model.\n",
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"\n",
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"### Integration details\n",
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"Head to the [API reference](https://docs.symbl.ai/reference/nebula-chat) for detailed documentation.\n",
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"\n",
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"### Model features: TODO"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3607d67e-e56c-4102-bbba-df2edc0e109e",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"### Credentials\n",
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"To get started, request a [Nebula API key](https://platform.symbl.ai/#/login) and set the `NEBULA_API_KEY` environment variable:"
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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": 2,
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"id": "2108b517-1e8d-473d-92fa-4f930e8072a7",
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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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"os.environ[\"NEBULA_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "68b44357",
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"metadata": {},
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"source": [
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"### Installation\n",
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"The integration is set up in the `langchain-community` package."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c26754b-b3c9-4d93-8f36-43049bd943bf",
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"metadata": {},
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"source": [
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"## Instantiation"
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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": "0fdd26e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.chat_models.symblai_nebula import ChatNebula\n",
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"from langchain_core.messages import AIMessage, HumanMessage, SystemMessage"
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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": 4,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatNebula(max_tokens=1024, temperature=0.5)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a915547",
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"metadata": {},
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"source": [
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"## Invocation"
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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": 5,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant that answers general knowledge questions.\"\n",
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" ),\n",
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" HumanMessage(content=\"What is the capital of France?\"),\n",
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"]\n",
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"chat.invoke(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9723913f",
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"metadata": {},
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"source": [
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"### Async"
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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": 6,
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"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chat.ainvoke(messages)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0a1d3b4",
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"metadata": {},
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"source": [
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"### Streaming"
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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": 9,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" The capital of France is Paris."
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]
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}
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],
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"source": [
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"for chunk in chat.stream(messages):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9f91b7c7",
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"metadata": {},
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"source": [
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"### Batch"
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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": 12,
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"id": "054dc648",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat.batch([messages])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e59a5519",
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"metadata": {},
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"source": [
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"## Chaining"
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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": 18,
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"id": "6455f67b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
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"chain = prompt | chat"
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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": 19,
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"id": "deb1e2a1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=[{'role': 'human', 'text': 'Tell me a joke about cows'}, {'role': 'assistant', 'text': \"Sure, here's a joke about cows:\\n\\nWhy did the cow cross the road?\\n\\nTo get to the udder side!\"}])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke({\"topic\": \"cows\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb9d4755",
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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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"Check out the [API reference](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.symblai_nebula.ChatNebula.html) for more detail."
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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": "3.11.8"
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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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@ -153,6 +153,7 @@ if TYPE_CHECKING:
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from langchain_community.chat_models.sparkllm import (
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ChatSparkLLM,
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)
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from langchain_community.chat_models.symblai_nebula import ChatNebula
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from langchain_community.chat_models.tongyi import (
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ChatTongyi,
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)
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@ -201,6 +202,7 @@ __all__ = [
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"ChatMLflowAIGateway",
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"ChatMaritalk",
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"ChatMlflow",
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"ChatNebula",
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"ChatOCIGenAI",
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"ChatOllama",
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"ChatOpenAI",
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@ -257,6 +259,7 @@ _module_lookup = {
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"ChatMLX": "langchain_community.chat_models.mlx",
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"ChatMaritalk": "langchain_community.chat_models.maritalk",
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"ChatMlflow": "langchain_community.chat_models.mlflow",
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"ChatNebula": "langchain_community.chat_models.symblai_nebula",
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"ChatOctoAI": "langchain_community.chat_models.octoai",
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"ChatOCIGenAI": "langchain_community.chat_models.oci_generative_ai",
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"ChatOllama": "langchain_community.chat_models.ollama",
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271
libs/community/langchain_community/chat_models/symblai_nebula.py
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271
libs/community/langchain_community/chat_models/symblai_nebula.py
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@ -0,0 +1,271 @@
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import json
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import os
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from json import JSONDecodeError
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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import requests
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from aiohttp import ClientSession
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, SecretStr
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from langchain_core.utils import convert_to_secret_str
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def _convert_role(role: str) -> str:
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map = {"ai": "assistant", "human": "human", "chat": "human"}
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if role in map:
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return map[role]
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else:
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raise ValueError(f"Unknown role type: {role}")
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def _format_nebula_messages(messages: List[BaseMessage]) -> Dict[str, Any]:
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system = ""
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formatted_messages = []
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for message in messages[:-1]:
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if message.type == "system":
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if isinstance(message.content, str):
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system = message.content
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else:
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raise ValueError("System prompt must be a string")
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else:
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formatted_messages.append(
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{
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"role": _convert_role(message.type),
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"text": message.content,
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}
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)
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text = messages[-1].content
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formatted_messages.append({"role": "human", "text": text})
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return {"system_prompt": system, "messages": formatted_messages}
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class ChatNebula(BaseChatModel):
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"""`Nebula` chat large language model - https://docs.symbl.ai/docs/nebula-llm
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API Reference: https://docs.symbl.ai/reference/nebula-chat
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To use, set the environment variable ``NEBULA_API_KEY``,
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or pass it as a named parameter to the constructor.
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To request an API key, visit https://platform.symbl.ai/#/login
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatNebula
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from langchain_core.messages import SystemMessage, HumanMessage
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chat = ChatNebula(max_new_tokens=1024, temperature=0.5)
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messages = [
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SystemMessage(
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content="You are a helpful assistant."
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),
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HumanMessage(
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"Answer the following question. How can I help save the world."
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),
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]
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chat.invoke(messages)
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"""
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max_new_tokens: int = 1024
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"""Denotes the number of tokens to predict per generation."""
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temperature: Optional[float] = 0
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"""A non-negative float that tunes the degree of randomness in generation."""
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streaming: bool = False
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nebula_api_url: str = "https://api-nebula.symbl.ai"
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nebula_api_key: Optional[SecretStr] = Field(None, description="Nebula API Token")
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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def __init__(self, **kwargs: Any) -> None:
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if "nebula_api_key" in kwargs:
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api_key = convert_to_secret_str(kwargs.pop("nebula_api_key"))
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elif "NEBULA_API_KEY" in os.environ:
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api_key = convert_to_secret_str(os.environ["NEBULA_API_KEY"])
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else:
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api_key = None
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super().__init__(nebula_api_key=api_key, **kwargs) # type: ignore[call-arg]
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "nebula-chat"
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@property
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def _api_key(self) -> str:
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if self.nebula_api_key:
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return self.nebula_api_key.get_secret_value()
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return ""
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
|
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) -> Iterator[ChatGenerationChunk]:
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"""Call out to Nebula's chat endpoint."""
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url = f"{self.nebula_api_url}/v1/model/chat/streaming"
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headers = {
|
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"ApiKey": self._api_key,
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"Content-Type": "application/json",
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}
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formatted_data = _format_nebula_messages(messages=messages)
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payload: Dict[str, Any] = {
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"max_new_tokens": self.max_new_tokens,
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"temperature": self.temperature,
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**formatted_data,
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**kwargs,
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}
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payload = {k: v for k, v in payload.items() if v is not None}
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json_payload = json.dumps(payload)
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||||
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||||
response = requests.request(
|
||||
"POST", url, headers=headers, data=json_payload, stream=True
|
||||
)
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response.raise_for_status()
|
||||
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||||
for chunk_response in response.iter_lines():
|
||||
chunk_decoded = chunk_response.decode()[6:]
|
||||
try:
|
||||
chunk = json.loads(chunk_decoded)
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||||
except JSONDecodeError:
|
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continue
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token = chunk["delta"]
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cg_chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))
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if run_manager:
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run_manager.on_llm_new_token(token, chunk=cg_chunk)
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yield cg_chunk
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async def _astream(
|
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self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
url = f"{self.nebula_api_url}/v1/model/chat/streaming"
|
||||
headers = {"ApiKey": self._api_key, "Content-Type": "application/json"}
|
||||
formatted_data = _format_nebula_messages(messages=messages)
|
||||
payload: Dict[str, Any] = {
|
||||
"max_new_tokens": self.max_new_tokens,
|
||||
"temperature": self.temperature,
|
||||
**formatted_data,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
payload = {k: v for k, v in payload.items() if v is not None}
|
||||
json_payload = json.dumps(payload)
|
||||
|
||||
async with ClientSession() as session:
|
||||
async with session.post( # type: ignore[call-arg]
|
||||
url, data=json_payload, headers=headers, stream=True
|
||||
) as response:
|
||||
response.raise_for_status()
|
||||
async for chunk_response in response.content:
|
||||
chunk_decoded = chunk_response.decode()[6:]
|
||||
try:
|
||||
chunk = json.loads(chunk_decoded)
|
||||
except JSONDecodeError:
|
||||
continue
|
||||
token = chunk["delta"]
|
||||
cg_chunk = ChatGenerationChunk(
|
||||
message=AIMessageChunk(content=token)
|
||||
)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(token, chunk=cg_chunk)
|
||||
yield cg_chunk
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if self.streaming:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
|
||||
url = f"{self.nebula_api_url}/v1/model/chat"
|
||||
headers = {"ApiKey": self._api_key, "Content-Type": "application/json"}
|
||||
formatted_data = _format_nebula_messages(messages=messages)
|
||||
payload: Dict[str, Any] = {
|
||||
"max_new_tokens": self.max_new_tokens,
|
||||
"temperature": self.temperature,
|
||||
**formatted_data,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
payload = {k: v for k, v in payload.items() if v is not None}
|
||||
json_payload = json.dumps(payload)
|
||||
|
||||
response = requests.request("POST", url, headers=headers, data=json_payload)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
return ChatResult(
|
||||
generations=[ChatGeneration(message=AIMessage(content=data["messages"]))],
|
||||
llm_output=data,
|
||||
)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if self.streaming:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
|
||||
url = f"{self.nebula_api_url}/v1/model/chat"
|
||||
headers = {"ApiKey": self._api_key, "Content-Type": "application/json"}
|
||||
formatted_data = _format_nebula_messages(messages=messages)
|
||||
payload: Dict[str, Any] = {
|
||||
"max_new_tokens": self.max_new_tokens,
|
||||
"temperature": self.temperature,
|
||||
**formatted_data,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
payload = {k: v for k, v in payload.items() if v is not None}
|
||||
json_payload = json.dumps(payload)
|
||||
|
||||
async with ClientSession() as session:
|
||||
async with session.post(
|
||||
url, data=json_payload, headers=headers
|
||||
) as response:
|
||||
response.raise_for_status()
|
||||
data = await response.json()
|
||||
|
||||
return ChatResult(
|
||||
generations=[
|
||||
ChatGeneration(message=AIMessage(content=data["messages"]))
|
||||
],
|
||||
llm_output=data,
|
||||
)
|
||||
@ -28,6 +28,7 @@ EXPECTED_ALL = [
|
||||
"ChatMlflow",
|
||||
"ChatMLflowAIGateway",
|
||||
"ChatMLX",
|
||||
"ChatNebula",
|
||||
"ChatOCIGenAI",
|
||||
"ChatOllama",
|
||||
"ChatOpenAI",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user