langchain/docs/modules/indexes/retrievers/examples/chatgpt-plugin.ipynb
Leonid Ganeline b96ab4b763
docs retriever improvements (#4430)
# Docs: improvements in the `retrievers/examples/` notebooks

Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
  - docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
  - docs/modules/indexes/vectorstores/examples/pinecone.ipynb
  - docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )

## Who can review
@dev2049
2023-05-17 15:29:22 -07:00

179 lines
5.5 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"id": "1edb9e6b",
"metadata": {},
"source": [
"# ChatGPT Plugin\n",
"\n",
">[OpenAI plugins](https://platform.openai.com/docs/plugins/introduction) connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.\n",
"\n",
">Plugins can allow ChatGPT to do things like:\n",
">- Retrieve real-time information; e.g., sports scores, stock prices, the latest news, etc.\n",
">- Retrieve knowledge-base information; e.g., company docs, personal notes, etc.\n",
">- Perform actions on behalf of the user; e.g., booking a flight, ordering food, etc.\n",
"\n",
"This notebook shows how to use the ChatGPT Retriever Plugin within LangChain."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bbe89ca0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# STEP 1: Load\n",
"\n",
"# Load documents using LangChain's DocumentLoaders\n",
"# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html\n",
"\n",
"from langchain.document_loaders.csv_loader import CSVLoader\n",
"loader = CSVLoader(file_path='../../document_loaders/examples/example_data/mlb_teams_2012.csv')\n",
"data = loader.load()\n",
"\n",
"\n",
"# STEP 2: Convert\n",
"\n",
"# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-plugin\n",
"from typing import List\n",
"from langchain.docstore.document import Document\n",
"import json\n",
"\n",
"def write_json(path: str, documents: List[Document])-> None:\n",
" results = [{\"text\": doc.page_content} for doc in documents]\n",
" with open(path, \"w\") as f:\n",
" json.dump(results, f, indent=2)\n",
"\n",
"write_json(\"foo.json\", data)\n",
"\n",
"# STEP 3: Use\n",
"\n",
"# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json\n"
]
},
{
"cell_type": "markdown",
"id": "0474661d",
"metadata": {},
"source": [
"## Using the ChatGPT Retriever Plugin\n",
"\n",
"Okay, so we've created the ChatGPT Retriever Plugin, but how do we actually use it?\n",
"\n",
"The below code walks through how to do that."
]
},
{
"cell_type": "markdown",
"id": "fb27da9f-d574-425d-8fab-92b03b997568",
"metadata": {},
"source": [
"We want to use `ChatGPTPluginRetriever` so we have to get the OpenAI API Key."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b5d8c9e9-839f-42e9-933a-08195797dd4c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"OpenAI API Key: ········\n"
]
}
],
"source": [
"import os\n",
"import getpass\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "39d6074e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.retrievers import ChatGPTPluginRetriever"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "33fd23d1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = ChatGPTPluginRetriever(url=\"http://0.0.0.0:8000\", bearer_token=\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "16250bdf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content=\"This is Alice's phone number: 123-456-7890\", lookup_str='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0),\n",
" Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0),\n",
" Document(page_content='Team: Angels \"Payroll (millions)\": 154.49 \"Wins\": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': None, 'score': 0.697888613}, lookup_index=0)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.get_relevant_documents(\"alice's phone number\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8b5794b",
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}