TramAI

0.3.0 — Memory, Multimodal, RAG, and Provider Expansion

0.3.0 is the repository milestone that expanded TramAI beyond the original typed service, provider, and orchestration surfaces into memory, multimodal input, RAG, vector stores, and a broader provider matrix.

New Modules

tramai-memory

Production-ready chat memory primitives for multi-turn AI services.

  • MessageWindowChatMemory for bounded recent-message windows
  • TokenAwareChatMemory for token-budget-aware conversation history
  • PersistentChatMemory for memory backed by an external store
  • system-message deduplication and deterministic eviction behavior

tramai-memory-store

External persistence SPI support for chat memory.

  • durable conversation storage boundary
  • JDBC-backed store support
  • table DDL helpers for operational setup

tramai-rag

Retrieval-augmented generation pipeline support.

  • document loading
  • chunking
  • retrieval
  • context injection into AI operations

tramai-embedding

Embedding model SPI and provider-backed embedding implementations.

  • OpenAI embedding model support
  • Ollama embedding model support
  • registry support for embedding use cases

tramai-vectorstore-spi

Vector store abstractions for semantic retrieval.

  • text embedding storage contracts
  • metadata-aware query models
  • in-memory implementation for tests and simple local flows

tramai-vectorstore-chroma

ChromaDB vector store adapter.

  • collection creation and lookup
  • document upsert
  • similarity query support

tramai-vectorstore-pgvector

PostgreSQL pgvector adapter.

  • relational vector storage
  • pgvector-backed similarity queries
  • fit for applications already using PostgreSQL

tramai-azure-openai

Azure OpenAI provider integration.

  • Azure endpoint support
  • API-key authentication
  • model request and response mapping
  • usage metric extraction

tramai-bedrock

AWS Bedrock provider integration.

  • Bedrock runtime request mapping
  • provider error normalization
  • image-capable request support where supported by the target model

tramai-gemini

Google Gemini provider integration.

  • Gemini request and response mapping
  • multimodal content support
  • provider error normalization

tramai-deepseek

DeepSeek provider integration.

  • DeepSeek chat API support
  • provider error normalization
  • OpenAI-style request semantics where compatible

Core Features

Multimodal Content

Core message modeling now supports additive content parts instead of only plain text.

  • ContentPart
  • TextPart
  • ImagePart
  • ImageUrlContent
  • ImageDetail with LOW, HIGH, and AUTO

The engine validates provider capabilities before execution so image input sent to a non-vision provider fails explicitly.

Image Downloading

TramAI includes built-in image download support for URL-backed image content.

  • 20MB response-size limit
  • 10s connect timeout
  • 30s request timeout
  • MIME detection from URL extension

Usage Metrics

Usage reporting was expanded for multimodal workloads.

  • image count tracking
  • estimated image token tracking
  • provider-specific usage normalization

Provider Updates

The existing providers were updated for multimodal serialization and capability reporting.

  • OpenAI
  • Azure OpenAI
  • Anthropic
  • Bedrock
  • Gemini
  • Ollama
  • DeepSeek

Observability Updates

Worker and distributed execution observability gained broader event coverage.

  • shutdown started
  • drain progress
  • lease renewed
  • worker heartbeat
  • workflow abandoned
  • graceful shutdown bounds

Notes

  • TramAI 0.3.x targets Java 21+.
  • Structured output remains the default contract for non-String return types.
  • Provider routing remains explicit and registry-based.
  • Runtime and platform modules remain optional.
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