mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
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
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dd7ec7af",
|
|
"metadata": {},
|
|
"source": [
|
|
"# LLMRequestsChain\n",
|
|
"\n",
|
|
"Using the request library to get HTML results from a URL and then an LLM to parse results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "dd8eae75",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.llms import OpenAI\n",
|
|
"from langchain.chains import LLMRequestsChain, LLMChain"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "65bf324e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.prompts import PromptTemplate\n",
|
|
"\n",
|
|
"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
|
|
"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
|
|
"Use the format\n",
|
|
"Extracted:<answer or \"not found\">\n",
|
|
">>> {requests_result} <<<\n",
|
|
"Extracted:\"\"\"\n",
|
|
"\n",
|
|
"PROMPT = PromptTemplate(\n",
|
|
" input_variables=[\"query\", \"requests_result\"],\n",
|
|
" template=template,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "f36ae0d8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "b5d22d9d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
|
|
"inputs = {\n",
|
|
" \"query\": question,\n",
|
|
" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "2ea81168",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
|
|
" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
|
|
" 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain(inputs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "db8f2b6d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|