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langchain/docs/extras/integrations/document_loaders/github.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GitHub\n",
"\n",
"This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup access token"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To access the GitHub API, you need a personal access token - you can set up yours here: https://github.com/settings/tokens?type=beta. You can either set this token as the environment variable ``GITHUB_PERSONAL_ACCESS_TOKEN`` and it will be automatically pulled in, or you can pass it in directly at initializaiton as the ``access_token`` named parameter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# If you haven't set your access token as an environment variable, pass it in here.\n",
"from getpass import getpass\n",
"\n",
"ACCESS_TOKEN = getpass()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Issues and PRs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.document_loaders import GitHubIssuesLoader"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"loader = GitHubIssuesLoader(\n",
" repo=\"langchain-ai/langchain\",\n",
" access_token=ACCESS_TOKEN, # delete/comment out this argument if you've set the access token as an env var.\n",
" creator=\"UmerHA\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load all issues and PRs created by \"UmerHA\".\n",
"\n",
"Here's a list of all filters you can use:\n",
"- include_prs\n",
"- milestone\n",
"- state\n",
"- assignee\n",
"- creator\n",
"- mentioned\n",
"- labels\n",
"- sort\n",
"- direction\n",
"- since\n",
"\n",
"For more info, see https://docs.github.com/en/rest/issues/issues?apiVersion=2022-11-28#list-repository-issues."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"docs = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Creates GitHubLoader (#5257)\r\n",
"\r\n",
"GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.\r\n",
"\r\n",
"Fixes #5257\r\n",
"\r\n",
"Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:\r\n",
"DataLoaders\r\n",
"- @eyurtsev\r\n",
"\n",
"{'url': 'https://github.com/langchain-ai/langchain/pull/5408', 'title': 'DocumentLoader for GitHub', 'creator': 'UmerHA', 'created_at': '2023-05-29T14:50:53Z', 'comments': 0, 'state': 'open', 'labels': ['enhancement', 'lgtm', 'doc loader'], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5408, 'is_pull_request': True}\n"
]
}
],
"source": [
"print(docs[0].page_content)\n",
"print(docs[0].metadata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Only load issues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the GitHub API returns considers pull requests to also be issues. To only get 'pure' issues (i.e., no pull requests), use `include_prs=False`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"loader = GitHubIssuesLoader(\n",
" repo=\"langchain-ai/langchain\",\n",
" access_token=ACCESS_TOKEN, # delete/comment out this argument if you've set the access token as an env var.\n",
" creator=\"UmerHA\",\n",
" include_prs=False,\n",
")\n",
"docs = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"### System Info\n",
"\n",
"LangChain version = 0.0.167\r\n",
"Python version = 3.11.0\r\n",
"System = Windows 11 (using Jupyter)\n",
"\n",
"### Who can help?\n",
"\n",
"- @hwchase17\r\n",
"- @agola11\r\n",
"- @UmerHA (I have a fix ready, will submit a PR)\n",
"\n",
"### Information\n",
"\n",
"- [ ] The official example notebooks/scripts\n",
"- [X] My own modified scripts\n",
"\n",
"### Related Components\n",
"\n",
"- [X] LLMs/Chat Models\n",
"- [ ] Embedding Models\n",
"- [X] Prompts / Prompt Templates / Prompt Selectors\n",
"- [ ] Output Parsers\n",
"- [ ] Document Loaders\n",
"- [ ] Vector Stores / Retrievers\n",
"- [ ] Memory\n",
"- [ ] Agents / Agent Executors\n",
"- [ ] Tools / Toolkits\n",
"- [ ] Chains\n",
"- [ ] Callbacks/Tracing\n",
"- [ ] Async\n",
"\n",
"### Reproduction\n",
"\n",
"```\r\n",
"import os\r\n",
"os.environ[\"OPENAI_API_KEY\"] = \"...\"\r\n",
"\r\n",
"from langchain.chains import LLMChain\r\n",
"from langchain.chat_models import ChatOpenAI\r\n",
"from langchain.prompts import PromptTemplate\r\n",
"from langchain.prompts.chat import ChatPromptTemplate\r\n",
"from langchain.schema import messages_from_dict\r\n",
"\r\n",
"role_strings = [\r\n",
" (\"system\", \"you are a bird expert\"), \r\n",
" (\"human\", \"which bird has a point beak?\")\r\n",
"]\r\n",
"prompt = ChatPromptTemplate.from_role_strings(role_strings)\r\n",
"chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\r\n",
"chain.run({})\r\n",
"```\n",
"\n",
"### Expected behavior\n",
"\n",
"Chain should run\n",
"{'url': 'https://github.com/langchain-ai/langchain/issues/5027', 'title': \"ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings\", 'creator': 'UmerHA', 'created_at': '2023-05-20T10:39:18Z', 'comments': 1, 'state': 'open', 'labels': [], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5027, 'is_pull_request': False}\n"
]
}
],
"source": [
"print(docs[0].page_content)\n",
"print(docs[0].metadata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}