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docs: update README files to include Google Drive and Circleback

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@ -15,7 +15,7 @@
</div>
# SurfSense
While tools like NotebookLM and Perplexity are impressive and highly effective for conducting research on any topic/query, SurfSense elevates this capability by integrating with your personal knowledge base. It is a highly customizable AI research agent, connected to external sources such as Search Engines (SearxNG, Tavily, LinkUp), Slack, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Elasticsearch and more to come.
While tools like NotebookLM and Perplexity are impressive and highly effective for conducting research on any topic/query, SurfSense elevates this capability by integrating with your personal knowledge base. It is a highly customizable AI research agent, connected to external sources such as Search Engines (SearxNG, Tavily, LinkUp), Google Drive, Slack, Linear, Jira, ClickUp, Confluence, BookStack, Gmail, Notion, YouTube, GitHub, Discord, Airtable, Google Calendar, Luma, Circleback, Elasticsearch and more to come.
<div align="center">
<a href="https://trendshift.io/repositories/13606" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13606" alt="MODSetter%2FSurfSense | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
@ -93,6 +93,7 @@ Contributors can easily add new tools via the registry pattern:
### ℹ️ **External Sources**
- Search Engines (Tavily, LinkUp)
- SearxNG (self-hosted instances)
- Google Drive
- Slack
- Linear
- Jira
@ -107,6 +108,8 @@ Contributors can easily add new tools via the registry pattern:
- Airtable
- Google Calendar
- Luma
- Circleback
- Circleback
- Elasticsearch
- and more to come.....
@ -295,9 +298,6 @@ Before self-hosting installation, make sure to complete the [prerequisite setup
- **Flower**: Real-time monitoring and administration tool for Celery task queues
- **Chonkie**: Advanced document chunking and embedding library
- Uses `AutoEmbeddings` for flexible embedding model selection
- `LateChunker` for optimized document chunking based on embedding model's max sequence length
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