# TypeAgent

## Docs

- [Embedding Models](https://mintlify.wiki/microsoft/typeagent-py/api/aitools/embeddings.md): IEmbedder, IEmbeddingModel, and CachingEmbeddingModel interfaces
- [Model Adapters](https://mintlify.wiki/microsoft/typeagent-py/api/aitools/model-adapters.md): Provider-agnostic model configuration with pydantic-ai
- [ConversationBase](https://mintlify.wiki/microsoft/typeagent-py/api/conversation-base.md): Base class for conversations with incremental indexing support
- [create_conversation](https://mintlify.wiki/microsoft/typeagent-py/api/create-conversation.md): Factory function for creating conversation objects with automatic indexing
- [Message Classes](https://mintlify.wiki/microsoft/typeagent-py/api/messages.md): ConversationMessage, ConversationMessageMeta, and related message types
- [ConversationSettings](https://mintlify.wiki/microsoft/typeagent-py/api/settings/conversation-settings.md): Configuration class for conversation processing and indexing
- [Environment Variables](https://mintlify.wiki/microsoft/typeagent-py/api/settings/environment-variables.md): API keys, endpoints, and model configuration via environment variables
- [Collections](https://mintlify.wiki/microsoft/typeagent-py/api/storage/collections.md): IMessageCollection and ISemanticRefCollection interfaces
- [Index Types](https://mintlify.wiki/microsoft/typeagent-py/api/storage/indexes.md): All 6 index interfaces for knowledge lookup and search
- [Storage Providers](https://mintlify.wiki/microsoft/typeagent-py/api/storage/providers.md): IStorageProvider, MemoryStorageProvider, and SqliteStorageProvider
- [System Architecture](https://mintlify.wiki/microsoft/typeagent-py/concepts/architecture.md): TypeAgent's four-layer architecture with dual storage backends and structured retrieval
- [Specialized Indexes](https://mintlify.wiki/microsoft/typeagent-py/concepts/indexing.md): Deep dive into TypeAgent's six specialized indexes and their purposes
- [Knowledge Extraction](https://mintlify.wiki/microsoft/typeagent-py/concepts/knowledge-extraction.md): How AI models extract entities, topics, actions, and relationships from conversations
- [Structured RAG](https://mintlify.wiki/microsoft/typeagent-py/concepts/structured-rag.md): How TypeAgent's structured RAG approach differs from traditional RAG systems
- [Configuration](https://mintlify.wiki/microsoft/typeagent-py/guides/configuration.md): Configure ConversationSettings, extraction modes, and customization options for TypeAgent
- [Email Integration](https://mintlify.wiki/microsoft/typeagent-py/guides/email-integration.md): Ingest and query emails from Gmail, Outlook, and mbox archives
- [Message Ingestion](https://mintlify.wiki/microsoft/typeagent-py/guides/ingestion.md): Learn how to ingest messages into TypeAgent, including batch processing and message formatting
- [Podcast Integration](https://mintlify.wiki/microsoft/typeagent-py/guides/podcast-integration.md): Ingest and query podcast transcripts and VTT files
- [Querying Conversations](https://mintlify.wiki/microsoft/typeagent-py/guides/querying.md): Learn how to query conversations using natural language, search patterns, and retrieval strategies
- [Storage Providers](https://mintlify.wiki/microsoft/typeagent-py/guides/storage-providers.md): Understand Memory vs SQLite storage providers and how to switch between them
- [Installation](https://mintlify.wiki/microsoft/typeagent-py/installation.md): Install TypeAgent using pip, uv, or poetry with proper environment setup
- [Introduction](https://mintlify.wiki/microsoft/typeagent-py/introduction.md): Learn about TypeAgent, a Python library for AI-powered knowledge processing using Structured RAG
- [Quickstart](https://mintlify.wiki/microsoft/typeagent-py/quickstart.md): Build your first TypeAgent application from installation to querying in minutes