Vectors Overview
Vectors in Indite represent searchable knowledge bases that power your AI chatbots with contextual information. By creating vectors, you can ingest data from various sources and convert it into embeddings for efficient retrieval-augmented generation (RAG).
What are Vectors?
Vectors are collections of embedded data that enable your bots to retrieve relevant information during conversations. Indite's vector system allows you to:
- 📄 Ingest Multiple Sources: Add text, Q&A pairs, websites, or files.
- 🔍 Efficient Search: Use semantic search to find similar content.
- 🤖 Enhance AI Responses: Provide context to AI models for more accurate answers.
- ⚙️ Customize Chunking: Control how data is split and processed.
💡
Vectors are essential for building knowledgeable chatbots that reference your custom data.
Key Benefits
- Improved Accuracy: Bots can pull from your specific knowledge base.
- Easy Data Management: Update vectors without rebuilding bots.
- Scalable: Handle large datasets with advanced parameters.
- Multi-Source Support: Combine text, web content, and documents.
How Vectors Work
- Data Ingestion: Upload or crawl content sources.
- Processing: Content is chunked and embedded using AI models.
- Storage: Embeddings are stored in a vector database.
- Retrieval: Query the vector for relevant chunks during conversations.
- Generation: AI uses retrieved context to generate responses.
Quick Navigation
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Ensure your data sources are clean and relevant to optimize vector performance.