mirror of
https://github.com/hwchase17/langchain.git
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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>
124 lines
2.9 KiB
Plaintext
124 lines
2.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dd7ec7af",
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"metadata": {},
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"source": [
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"# LLMRequestsChain\n",
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"\n",
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"Using the request library to get HTML results from a URL and then an LLM to parse results"
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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": "dd8eae75",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.chains import LLMRequestsChain, 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": "65bf324e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
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"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
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"Use the format\n",
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"Extracted:<answer or \"not found\">\n",
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">>> {requests_result} <<<\n",
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"Extracted:\"\"\"\n",
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"\n",
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"PROMPT = PromptTemplate(\n",
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" input_variables=[\"query\", \"requests_result\"],\n",
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" template=template,\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": 3,
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"id": "f36ae0d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
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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": "b5d22d9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
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"inputs = {\n",
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" \"query\": question,\n",
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" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\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": 5,
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"id": "2ea81168",
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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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"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
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" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
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" 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
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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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"chain(inputs)"
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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": "db8f2b6d",
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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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