forked from Archives/langchain
added Wikipedia
document loader (#4141)
- Added the `Wikipedia` document loader. It is based on the existing `unilities/WikipediaAPIWrapper` - Added a respective ut-s and example notebook - Sorted list of classes in __init__
This commit is contained in:
parent
423f497168
commit
9544b30821
@ -1,14 +1,13 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "245a954a",
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"metadata": {},
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"source": [
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"# Wikipedia API\n",
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"# Wikipedia\n",
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"\n",
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"This notebook goes over how to use the wikipedia component.\n",
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">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n",
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"\n",
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"First, you need to install `wikipedia` python package."
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]
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@ -24,7 +23,7 @@
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},
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"outputs": [],
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"source": [
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"pip install wikipedia"
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"!pip install wikipedia"
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]
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},
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{
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@ -71,7 +70,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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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@ -85,7 +84,7 @@
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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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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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130
docs/modules/indexes/document_loaders/examples/wikipedia.ipynb
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130
docs/modules/indexes/document_loaders/examples/wikipedia.ipynb
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@ -0,0 +1,130 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bda1f3f5",
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"metadata": {},
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"source": [
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"# Wikipedia\n",
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"\n",
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">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n",
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"\n",
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"This notebook shows how to load wiki pages from `wikipedia.org` into the Document format that we use downstream."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b7a1eef-7bf7-4e7d-8bfc-c4e27c9488cb",
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"metadata": {},
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"source": [
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"## Installation"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2abd5578-aa3d-46b9-99af-8b262f0b3df8",
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"metadata": {},
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"source": [
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"First, you need to install `wikipedia` python package."
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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": "b674aaea-ed3a-4541-8414-260a8f67f623",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#!pip install wikipedia"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95f05e1c-195e-4e2b-ae8e-8d6637f15be6",
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"metadata": {},
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"source": [
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"## Examples"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e29b954c-1407-4797-ae21-6ba8937156be",
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"metadata": {},
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"source": [
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"`WikipediaLoader` has these arguments:\n",
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"- `query`: free text which used to find documents in Wikipedia\n",
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"- optional `lang`: default=\"en\". Use it to search in a specific language part of Wikipedia\n",
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"- optional `load_max_docs`: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.\n",
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"- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `title`, `Summary`. If True, other fields also downloaded."
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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": "9bfd5e46",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import WikipediaLoader"
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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": "700e4ef2",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = WikipediaLoader(query='HUNTER X HUNTER', load_max_docs=2).load()\n",
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"len(docs)"
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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": "8977bac0-0042-4f23-9754-247dbd32439b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"docs[0].metadata # meta-information of the 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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"id": "46969806-45a9-4c4d-a61b-cfb9658fc9de",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"docs[0].page_content[:400] # a content of the Document \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": "3.10.6"
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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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@ -93,6 +93,7 @@ from langchain.document_loaders.url_playwright import PlaywrightURLLoader
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from langchain.document_loaders.url_selenium import SeleniumURLLoader
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from langchain.document_loaders.web_base import WebBaseLoader
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from langchain.document_loaders.whatsapp_chat import WhatsAppChatLoader
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from langchain.document_loaders.wikipedia import WikipediaLoader
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from langchain.document_loaders.word_document import (
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Docx2txtLoader,
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UnstructuredWordDocumentLoader,
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@ -111,8 +112,6 @@ __all__ = [
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"AirbyteJSONLoader",
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"ApifyDatasetLoader",
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"ArxivLoader",
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"StripeLoader",
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"SpreedlyLoader",
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"AzureBlobStorageContainerLoader",
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"AzureBlobStorageFileLoader",
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"BSHTMLLoader",
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"DiffbotLoader",
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"DirectoryLoader",
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"DiscordChatLoader",
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"Docx2txtLoader",
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"DuckDBLoader",
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"EverNoteLoader",
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"FacebookChatLoader",
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"GitLoader",
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"GitbookLoader",
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"GoogleApiClient",
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"RedditPostsLoader",
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"GoogleApiYoutubeLoader",
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"GoogleDriveLoader",
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"GutenbergLoader",
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"HNLoader",
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"HuggingFaceDatasetLoader",
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"HuggingFaceDatasetLoader",
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"IFixitLoader",
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"IMSDbLoader",
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"ImageCaptionLoader",
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"JSONLoader",
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"ModernTreasuryLoader",
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"MWDumpLoader",
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"MathpixPDFLoader",
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"ModernTreasuryLoader",
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"NotebookLoader",
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"NotionDBLoader",
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"NotionDirectoryLoader",
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"PagedPDFSplitter",
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"PlaywrightURLLoader",
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"PyMuPDFLoader",
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"PyPDFDirectoryLoader",
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"PyPDFLoader",
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"PyPDFium2Loader",
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"PythonLoader",
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"ReadTheDocsLoader",
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"RedditPostsLoader",
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"RoamLoader",
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"S3DirectoryLoader",
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"S3FileLoader",
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@ -172,15 +175,17 @@ __all__ = [
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"SeleniumURLLoader",
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"SitemapLoader",
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"SlackDirectoryLoader",
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"SpreedlyLoader",
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"StripeLoader",
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"TelegramChatLoader",
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"TextLoader",
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"TomlLoader",
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"TwitterTweetLoader",
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"UnstructuredAPIFileIOLoader",
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"UnstructuredAPIFileLoader",
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"UnstructuredEPubLoader",
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"UnstructuredEmailLoader",
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"UnstructuredAPIFileIOLoader",
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"UnstructuredFileIOLoader",
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"UnstructuredAPIFileLoader",
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"UnstructuredFileLoader",
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"UnstructuredHTMLLoader",
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"UnstructuredImageLoader",
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"UnstructuredWordDocumentLoader",
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"WebBaseLoader",
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"WhatsAppChatLoader",
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"WikipediaLoader",
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"YoutubeLoader",
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"PyPDFDirectoryLoader",
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"MathpixPDFLoader",
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"ChatGPTLoader",
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"HuggingFaceDatasetLoader",
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"Docx2txtLoader",
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]
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34
langchain/document_loaders/wikipedia.py
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34
langchain/document_loaders/wikipedia.py
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from typing import List, Optional
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from langchain.docstore.document import Document
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from langchain.document_loaders.base import BaseLoader
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from langchain.utilities.wikipedia import WikipediaAPIWrapper
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class WikipediaLoader(BaseLoader):
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"""Loads a query result from www.wikipedia.org into a list of Documents.
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The hard limit on the number of downloaded Documents is 300 for now.
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Each wiki page represents one Document.
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"""
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def __init__(
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self,
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query: str,
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lang: str = "en",
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load_max_docs: Optional[int] = 100,
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load_all_available_meta: Optional[bool] = False,
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):
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self.query = query
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self.lang = lang
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self.load_max_docs = load_max_docs
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self.load_all_available_meta = load_all_available_meta
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def load(self) -> List[Document]:
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client = WikipediaAPIWrapper(
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lang=self.lang,
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top_k_results=self.load_max_docs,
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load_all_available_meta=self.load_all_available_meta,
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)
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docs = client.load(self.query)
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return docs
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@ -17,7 +17,7 @@ class WikipediaQueryRun(BaseTool):
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description = (
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"A wrapper around Wikipedia. "
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"Useful for when you need to answer general questions about "
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"people, places, companies, historical events, or other subjects. "
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"people, places, companies, facts, historical events, or other subjects. "
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"Input should be a search query."
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)
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api_wrapper: WikipediaAPIWrapper
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@ -62,10 +62,10 @@ class ArxivAPIWrapper(BaseModel):
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def run(self, query: str) -> str:
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"""
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Run Arxiv search and get the document meta information.
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Run Arxiv search and get the article meta information.
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See https://lukasschwab.me/arxiv.py/index.html#Search
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See https://lukasschwab.me/arxiv.py/index.html#Result
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It uses only the most informative fields of document meta information.
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It uses only the most informative fields of article meta information.
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"""
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try:
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docs = [
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@ -82,10 +82,10 @@ class ArxivAPIWrapper(BaseModel):
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def load(self, query: str) -> List[Document]:
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"""
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Run Arxiv search and get the PDF documents plus the meta information.
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Run Arxiv search and get the article texts plus the article meta information.
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See https://lukasschwab.me/arxiv.py/index.html#Search
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Returns: a list of documents with the document.page_content in PDF format
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Returns: a list of documents with the document.page_content in text format
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"""
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try:
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"""Util that calls Wikipedia."""
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from typing import Any, Dict, Optional
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import logging
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra, root_validator
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from langchain.schema import Document
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logger = logging.getLogger(__name__)
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WIKIPEDIA_MAX_QUERY_LENGTH = 300
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@ -18,6 +23,7 @@ class WikipediaAPIWrapper(BaseModel):
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wiki_client: Any #: :meta private:
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top_k_results: int = 3
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lang: str = "en"
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load_all_available_meta: bool = False
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class Config:
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"""Configuration for this pydantic object."""
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@ -41,23 +47,70 @@ class WikipediaAPIWrapper(BaseModel):
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def run(self, query: str) -> str:
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"""Run Wikipedia search and get page summaries."""
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search_results = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
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page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
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summaries = []
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len_search_results = len(search_results)
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if len_search_results == 0:
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return "No good Wikipedia Search Result was found"
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for i in range(min(self.top_k_results, len_search_results)):
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summary = self.fetch_formatted_page_summary(search_results[i])
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if summary is not None:
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for page_title in page_titles[: self.top_k_results]:
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if wiki_page := self._fetch_page(page_title):
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if summary := self._formatted_page_summary(page_title, wiki_page):
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summaries.append(summary)
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if not summaries:
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return "No good Wikipedia Search Result was found"
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return "\n\n".join(summaries)
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def fetch_formatted_page_summary(self, page: str) -> Optional[str]:
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@staticmethod
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def _formatted_page_summary(page_title: str, wiki_page: Any) -> Optional[str]:
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return f"Page: {page_title}\nSummary: {wiki_page.summary}"
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def _page_to_document(self, page_title: str, wiki_page: Any) -> Document:
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main_meta = {
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"title": page_title,
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"summary": wiki_page.summary,
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}
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add_meta = (
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{
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"categories": wiki_page.categories,
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# "coordinates": wiki_page.coordinates,
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"page_url": wiki_page.url,
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"image_urls": wiki_page.images,
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"related_titles": wiki_page.links,
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"parent_id": wiki_page.parent_id,
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"references": wiki_page.references,
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"revision_id": wiki_page.revision_id,
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"sections": wiki_page.sections,
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}
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if self.load_all_available_meta
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else {}
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)
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doc = Document(
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page_content=wiki_page.content,
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metadata={
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**main_meta,
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**add_meta,
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},
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)
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return doc
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def _fetch_page(self, page: str) -> Optional[str]:
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try:
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wiki_page = self.wiki_client.page(title=page, auto_suggest=False)
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return f"Page: {page}\nSummary: {wiki_page.summary}"
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return self.wiki_client.page(title=page, auto_suggest=False)
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except (
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self.wiki_client.exceptions.PageError,
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self.wiki_client.exceptions.DisambiguationError,
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):
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return None
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def load(self, query: str) -> List[Document]:
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"""
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Run Wikipedia search and get the article text plus the meta information.
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See
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Returns: a list of documents with the document.page_content in PDF format
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"""
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page_titles = self.wiki_client.search(query[:WIKIPEDIA_MAX_QUERY_LENGTH])
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docs = []
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for page_title in page_titles[: self.top_k_results]:
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if wiki_page := self._fetch_page(page_title):
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if doc := self._page_to_document(page_title, wiki_page):
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docs.append(doc)
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return docs
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@ -1,19 +1,56 @@
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"""Integration test for Wikipedia API Wrapper."""
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from typing import List
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import pytest
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from langchain.schema import Document
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from langchain.utilities import WikipediaAPIWrapper
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def test_call() -> None:
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"""Test that WikipediaAPIWrapper returns correct answer"""
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@pytest.fixture
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def api_client() -> WikipediaAPIWrapper:
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return WikipediaAPIWrapper()
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wikipedia = WikipediaAPIWrapper()
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output = wikipedia.run("HUNTER X HUNTER")
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def test_run_success(api_client: WikipediaAPIWrapper) -> None:
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output = api_client.run("HUNTER X HUNTER")
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assert "Yoshihiro Togashi" in output
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def test_no_result_call() -> None:
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"""Test that call gives no result."""
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wikipedia = WikipediaAPIWrapper()
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output = wikipedia.run(
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def test_run_no_result(api_client: WikipediaAPIWrapper) -> None:
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output = api_client.run(
|
||||
"NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL"
|
||||
)
|
||||
assert "No good Wikipedia Search Result was found" == output
|
||||
|
||||
|
||||
def assert_docs(docs: List[Document], all_meta: bool = False) -> None:
|
||||
for doc in docs:
|
||||
assert doc.page_content
|
||||
assert doc.metadata
|
||||
main_meta = {"title", "summary"}
|
||||
assert set(doc.metadata).issuperset(main_meta)
|
||||
if all_meta:
|
||||
assert len(set(doc.metadata)) > len(main_meta)
|
||||
else:
|
||||
assert len(set(doc.metadata)) == len(main_meta)
|
||||
|
||||
|
||||
def test_load_success(api_client: WikipediaAPIWrapper) -> None:
|
||||
docs = api_client.load("HUNTER X HUNTER")
|
||||
assert len(docs) > 1
|
||||
assert_docs(docs, all_meta=False)
|
||||
|
||||
|
||||
def test_load_success_all_meta(api_client: WikipediaAPIWrapper) -> None:
|
||||
api_client.load_all_available_meta = True
|
||||
docs = api_client.load("HUNTER X HUNTER")
|
||||
assert len(docs) > 1
|
||||
assert_docs(docs, all_meta=True)
|
||||
|
||||
|
||||
def test_load_no_result(api_client: WikipediaAPIWrapper) -> None:
|
||||
docs = api_client.load(
|
||||
"NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL"
|
||||
)
|
||||
assert not docs
|
||||
|
Loading…
Reference in New Issue
Block a user