forked from Archives/langchain
53c67e04d4
Integrate Aleph Alpha's client into Langchain to provide access to the luminous models - more info on latest benchmarks here: https://www.aleph-alpha.com/luminous-performance-benchmarks
109 lines
2.2 KiB
Plaintext
109 lines
2.2 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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"# Aleph Alpha\n",
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"This example goes over how to use LangChain to interact with Aleph Alpha models"
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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": 1,
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"id": "6fb585dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import AlephAlpha\n",
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"from langchain import PromptTemplate, 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": 2,
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"id": "f81a230d",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Q: {question}\n",
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"\n",
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"A:\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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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": 3,
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"id": "f0d26e48",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = AlephAlpha(model=\"luminous-extended\", maximum_tokens=20, stop_sequences=[\"Q:\"])"
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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": "6811d621",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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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": "3058e63f",
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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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"' Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.\\n'"
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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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"question = \"What is AI?\"\n",
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"\n",
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"llm_chain.run(question)"
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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": ".venv",
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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": "3.10.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "2d002ec47225e662695b764370d7966aa11eeb4302edc2f497bbf96d49c8f899"
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}
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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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