mirror of
https://github.com/hwchase17/langchain.git
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132 lines
3.6 KiB
Plaintext
132 lines
3.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SageMakerEndpoint\n",
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"\n",
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"This notebooks goes over how to use an LLM hosted on a SageMaker endpoint."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"!pip3 install langchain boto3"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.docstore.document import Document"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"example_doc_1 = \"\"\"\n",
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"Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.\n",
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"Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.\n",
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"Therefore, Peter stayed with her at the hospital for 3 days without leaving.\n",
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"\"\"\"\n",
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"\n",
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"docs = [\n",
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" Document(\n",
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" page_content=example_doc_1,\n",
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" )\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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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Dict\n",
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"\n",
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"from langchain import PromptTemplate, SagemakerEndpoint\n",
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"from langchain.llms.sagemaker_endpoint import ContentHandlerBase\n",
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"from langchain.chains.question_answering import load_qa_chain\n",
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"import json\n",
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"\n",
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"query = \"\"\"How long was Elizabeth hospitalized?\n",
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"\"\"\"\n",
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"\n",
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"prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n",
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"\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"Answer:\"\"\"\n",
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"PROMPT = PromptTemplate(\n",
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" template=prompt_template, input_variables=[\"context\", \"question\"]\n",
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")\n",
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"\n",
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"class ContentHandler(ContentHandlerBase):\n",
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" content_type = \"application/json\"\n",
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" accepts = \"application/json\"\n",
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"\n",
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" def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n",
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" input_str = json.dumps({prompt: prompt, **model_kwargs})\n",
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" return input_str.encode('utf-8')\n",
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" \n",
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" def transform_output(self, output: bytes) -> str:\n",
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" response_json = json.loads(output.read().decode(\"utf-8\"))\n",
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" return response_json[0][\"generated_text\"]\n",
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"\n",
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"content_handler = ContentHandler()\n",
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"\n",
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"chain = load_qa_chain(\n",
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" llm=SagemakerEndpoint(\n",
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" endpoint_name=\"endpoint-name\", \n",
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" credentials_profile_name=\"credentials-profile-name\", \n",
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" region_name=\"us-west-2\", \n",
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" model_kwargs={\"temperature\":1e-10},\n",
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" content_handler=content_handler\n",
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" ),\n",
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" prompt=PROMPT\n",
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")\n",
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"\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)\n",
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"\n"
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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": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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