mirror of
https://github.com/hwchase17/langchain
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40079d4936
## Description This PR adds Nebula to the available LLMs in LangChain. Nebula is an LLM focused on conversation understanding and enables users to extract conversation insights from video, audio, text, and chat-based conversations. These conversations can occur between any mix of human or AI participants. Examples of some questions you could ask Nebula from a given conversation are: - What could be the customer’s pain points based on the conversation? - What sales opportunities can be identified from this conversation? - What best practices can be derived from this conversation for future customer interactions? You can read more about Nebula here: https://symbl.ai/blog/extract-insights-symbl-ai-generative-ai-recall-ai-meetings/ #### Integration Test An integration test is added, but it requires network access. Since Nebula is fully managed like OpenAI, network access is required to exercise the integration test. #### Linting - [x] make lint - [x] make test (TODO: there seems to be a failure in another non-related test??? Need to check on this.) - [x] make format ### Dependencies No new dependencies were introduced. ### Twitter handle [@symbldotai](https://twitter.com/symbldotai) [@dvonthenen](https://twitter.com/dvonthenen) If you have any questions, please let me know. cc: @hwchase17, @baskaryan --------- Co-authored-by: dvonthenen <david.vonthenen@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
107 lines
2.7 KiB
Plaintext
107 lines
2.7 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# Nebula\n",
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"\n",
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"[Nebula](https://symbl.ai/nebula/) is a fully-managed Conversation platform, on which you can build, deploy, and manage scalable AI applications.\n",
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"\n",
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"This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm-overview). \n",
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"\n",
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"It will send the requests to Nebula Service endpoint, which concatenates `SYMBLAI_NEBULA_SERVICE_URL` and `SYMBLAI_NEBULA_SERVICE_PATH`, with a token defined in `SYMBLAI_NEBULA_SERVICE_TOKEN`"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f15ebe0d",
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"metadata": {},
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"source": [
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"### Integrate with a LLMChain"
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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": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
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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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"import os\n",
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"\n",
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"os.environ[\"SYMBLAI_NEBULA_SERVICE_URL\"] = SYMBLAI_NEBULA_SERVICE_URL\n",
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"os.environ[\"SYMBLAI_NEBULA_SERVICE_PATH\"] = SYMBLAI_NEBULA_SERVICE_PATH\n",
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"os.environ[\"SYMBLAI_NEBULA_SERVICE_TOKEN\"] = SYMBLAI_NEBULA_SERVICE_TOKEN"
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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": "6fb585dd",
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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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"from langchain.llms import OpenLLM\n",
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"\n",
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"llm = OpenLLM(\n",
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" conversation=\"<Drop your text conversation that you want to ask Nebula to analyze 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": "035dea0f",
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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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"from langchain import PromptTemplate, LLMChain\n",
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"\n",
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"template = \"Identify the {count} main objectives or goals mentioned in this context concisely in less points. Emphasize on key intents.\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"count\"])\n",
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"\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"\n",
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"generated = llm_chain.run(count=\"five\")\n",
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"print(generated)"
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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": "3.10.8"
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},
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"vscode": {
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"interpreter": {
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"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
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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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