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Reworded README.md around LLM compatibility

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(Based on discussion with Sid)
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Eric Lammertsma 2026-01-23 10:48:43 -05:00
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README.md
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@ -52,8 +52,10 @@ https://github.com/user-attachments/assets/a0a16566-6967-4374-ac51-9b3e07fbecd7
- Interact in Natural Language and get cited answers.
### 📄 **Cited Answers**
- Get Cited answers just like Perplexity.
### 🧩 **Universal Compatibility**
- Connect virtually any inference provider via the OpenAI spec and LiteLLM.
### 🔔 **Privacy & Local LLM Support**
- Works Flawlessly with Ollama local LLMs.
- Works Flawlessly with local LLMs like vLLM and Ollama.
### 🏠 **Self Hostable**
- Open source and easy to deploy locally.
### 👥 **Team Collaboration with RBAC**
@ -61,6 +63,7 @@ https://github.com/user-attachments/assets/a0a16566-6967-4374-ac51-9b3e07fbecd7
- Invite team members with customizable roles (Owner, Admin, Editor, Viewer)
- Granular permissions for documents, chats, connectors, and settings
- Share knowledge bases securely within your organization
- Team chats update in real-time and "Chat about the chat" in comment threads
### 🎙️ Podcasts
- Blazingly fast podcast generation agent. (Creates a 3-minute podcast in under 20 seconds.)
- Convert your chat conversations into engaging audio content
@ -237,6 +240,8 @@ Before self-hosting installation, make sure to complete the [prerequisite setup
### **BackEnd**
- **LiteLLM**: Universal LLM integration supporting 100+ models (OpenAI, Anthropic, Ollama, etc.)
- **FastAPI**: Modern, fast web framework for building APIs with Python
- **PostgreSQL with pgvector**: Database with vector search capabilities for similarity searches
@ -253,8 +258,6 @@ Before self-hosting installation, make sure to complete the [prerequisite setup
- **LangChain**: Framework for developing AI-powered applications.
- **LiteLLM**: Universal LLM integration supporting 100+ models (OpenAI, Anthropic, Ollama, etc.)
- **Rerankers**: Advanced result ranking for improved search relevance
- **Hybrid Search**: Combines vector similarity and full-text search for optimal results using Reciprocal Rank Fusion (RRF)