mirror of
https://github.com/hwchase17/langchain
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8f0cd91d57
This PR adds 8 new loaders: * `AirbyteCDKLoader` This reader can wrap and run all python-based Airbyte source connectors. * Separate loaders for the most commonly used APIs: * `AirbyteGongLoader` * `AirbyteHubspotLoader` * `AirbyteSalesforceLoader` * `AirbyteShopifyLoader` * `AirbyteStripeLoader` * `AirbyteTypeformLoader` * `AirbyteZendeskSupportLoader` ## Documentation and getting started I added the basic shape of the config to the notebooks. This increases the maintenance effort a bit, but I think it's worth it to make sure people can get started quickly with these important connectors. This is also why I linked the spec and the documentation page in the readme as these two contain all the information to configure a source correctly (e.g. it won't suggest using oauth if that's avoidable even if the connector supports it). ## Document generation The "documents" produced by these loaders won't have a text part (instead, all the record fields are put into the metadata). If a text is required by the use case, the caller needs to do custom transformation suitable for their use case. ## Incremental sync All loaders support incremental syncs if the underlying streams support it. By storing the `last_state` from the reader instance away and passing it in when loading, it will only load updated records. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
211 lines
5.9 KiB
Plaintext
211 lines
5.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1f3a5ebf",
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"metadata": {},
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"source": [
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"# Airbyte Zendesk Support"
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]
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},
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{
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"cell_type": "markdown",
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"id": "35ac77b1-449b-44f7-b8f3-3494d55c286e",
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"metadata": {},
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"source": [
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">[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.\n",
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"\n",
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"This loader exposes the Zendesk Support connector as a document loader, allowing you to load various objects as documents."
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]
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},
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{
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"cell_type": "markdown",
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"id": "6847a40c",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "3b06fbde",
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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": "e3e9dc79",
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"metadata": {},
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"source": [
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"First, you need to install the `airbyte-source-zendesk-support` 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": "4d35e4e0",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install airbyte-source-zendesk-support"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae855210",
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"metadata": {},
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"source": [
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"## Example"
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]
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},
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{
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"cell_type": "markdown",
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"id": "02208f52",
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"metadata": {},
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"source": [
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"Check out the [Airbyte documentation page](https://docs.airbyte.com/integrations/sources/zendesk-support/) for details about how to configure the reader.\n",
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"The JSON schema the config object should adhere to can be found on Github: [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-zendesk-support/source_zendesk_support/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-zendesk-support/source_zendesk_support/spec.json).\n",
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"\n",
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"The general shape looks like this:\n",
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"```python\n",
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"{\n",
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" \"subdomain\": \"<your zendesk subdomain>\",\n",
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" \"start_date\": \"<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>\",\n",
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" \"credentials\": {\n",
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" \"credentials\": \"api_token\",\n",
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" \"email\": \"<your email>\",\n",
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" \"api_token\": \"<your api token>\"\n",
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" }\n",
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"}\n",
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"```\n",
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"\n",
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"By default all fields are stored as metadata in the documents and the text is set to an empty string. Construct the text of the document by transforming the documents returned by the reader."
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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": "89a99e58",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from langchain.document_loaders.airbyte import AirbyteZendeskSupportLoader\n",
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"\n",
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"config = {\n",
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" # your zendesk-support configuration\n",
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"}\n",
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"\n",
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"loader = AirbyteZendeskSupportLoader(config=config, stream_name=\"tickets\") # check the documentation linked above for a list of all streams"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2cea23fc",
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"metadata": {},
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"source": [
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"Now you can load documents the usual way"
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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": "dae75cdb",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4a93dc2a",
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"metadata": {},
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"source": [
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"As `load` returns a list, it will block until all documents are loaded. To have better control over this process, you can also you the `lazy_load` method which returns an iterator instead:"
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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": "1782db09",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs_iterator = loader.lazy_load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a124086",
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"metadata": {},
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"source": [
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"Keep in mind that by default the page content is empty and the metadata object contains all the information from the record. To create documents in a different, pass in a record_handler function when creating the loader:"
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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": "5671395d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.docstore.document import Document\n",
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"\n",
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"def handle_record(record, id):\n",
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" return Document(page_content=record.data[\"title\"], metadata=record.data)\n",
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"\n",
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"loader = AirbyteZendeskSupportLoader(config=config, record_handler=handle_record, stream_name=\"tickets\")\n",
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"docs = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "223eb8bc",
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"metadata": {},
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"source": [
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"## Incremental loads\n",
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"\n",
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"Some streams allow incremental loading, this means the source keeps track of synced records and won't load them again. This is useful for sources that have a high volume of data and are updated frequently.\n",
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"\n",
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"To take advantage of this, store the `last_state` property of the loader and pass it in when creating the loader again. This will ensure that only new records are loaded."
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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": "7061e735",
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"metadata": {},
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"outputs": [],
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"source": [
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"last_state = loader.last_state # store safely\n",
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"\n",
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"incremental_loader = AirbyteZendeskSupportLoader(config=config, stream_name=\"tickets\", state=last_state)\n",
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"\n",
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"new_docs = incremental_loader.load()"
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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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