mirror of
https://github.com/hwchase17/langchain
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bf4a112aa6
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>
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[\"NEBULA_SERVICE_URL\"] = NEBULA_SERVICE_URL\n",
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"os.environ[\"NEBULA_SERVICE_PATH\"] = NEBULA_SERVICE_PATH\n",
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"os.environ[\"NEBULA_SERVICE_API_KEY\"] = NEBULA_SERVICE_API_KEY"
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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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