mirror of
https://github.com/hwchase17/langchain
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985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
72 lines
1.6 KiB
Plaintext
72 lines
1.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "959300d4",
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"metadata": {},
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"source": [
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"# HuggingFace Hub\n",
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"\n",
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"This example showcases how to connect to the HuggingFace Hub."
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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": "3acf0069",
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"metadata": {},
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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 Seattle Seahawks won the Super Bowl in 2010. Justin Beiber was born in 2010. The\n"
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]
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}
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],
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"source": [
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"from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
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"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":1e-10}))\n",
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"\n",
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"print(llm_chain.run(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": null,
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"id": "ae4559c7",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.10.9"
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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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