langchain/docs/extras/integrations/llms/symblai_nebula.ipynb
David vonThenen bf4a112aa6
Fixes to the Nebula LLM Integration (#8918)
This addresses some issues with introducing the Nebula LLM to LangChain
in this PR:
https://github.com/langchain-ai/langchain/pull/8876

This fixes the following:
- Removes `SYMBLAI` from variable names
- Fixes bug with `Bearer` for the API KEY


Thanks again in advance for your help!
cc: @hwchase17, @baskaryan

---------

Co-authored-by: dvonthenen <david.vonthenen@gmail.com>
2023-08-08 10:04:43 -07:00

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"source": [
"# Nebula\n",
"\n",
"[Nebula](https://symbl.ai/nebula/) is a fully-managed Conversation platform, on which you can build, deploy, and manage scalable AI applications.\n",
"\n",
"This example goes over how to use LangChain to interact with the [Nebula platform](https://docs.symbl.ai/docs/nebula-llm-overview). \n",
"\n",
"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`"
]
},
{
"cell_type": "markdown",
"id": "f15ebe0d",
"metadata": {},
"source": [
"### Integrate with a LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5472a7cd-af26-48ca-ae9b-5f6ae73c74d2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"NEBULA_SERVICE_URL\"] = NEBULA_SERVICE_URL\n",
"os.environ[\"NEBULA_SERVICE_PATH\"] = NEBULA_SERVICE_PATH\n",
"os.environ[\"NEBULA_SERVICE_API_KEY\"] = NEBULA_SERVICE_API_KEY"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fb585dd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenLLM\n",
"\n",
"llm = OpenLLM(\n",
" conversation=\"<Drop your text conversation that you want to ask Nebula to analyze here>\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "035dea0f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain import PromptTemplate, LLMChain\n",
"\n",
"template = \"Identify the {count} main objectives or goals mentioned in this context concisely in less points. Emphasize on key intents.\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"count\"])\n",
"\n",
"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
"\n",
"generated = llm_chain.run(count=\"five\")\n",
"print(generated)"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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