New release: 0.3.2 for Rhea: Latent Loom – self hosted LLM service

This update 0.3.2 introduces support for Text formats/Response formats support PyDantic JSON model schemas:

Refinements to endpoint: /v1/responses
- Adds parameters for test_format (Response formats) supporting PyDantic model JSON schemas.
- Adds response parameter: output_parsed.
Refinements to default parameters on endpoint: /v1/chat/completions
Additional schema models.

New release: 0.3.0 for Rhea: Latent Loom – self hosted LLM service

This update 0.3.0 introduces bi-directional function calling (client and server-side) tool calling:

Additions to schemas.
Refinements to endpoint: /v1/responses
- Adds parameters for tool/function calling.
- Tool calling supported on client and server side.

Unweaving the cloud: Local LLMs and Private RAG with Rhea: Latent Loom

Data privacy and mounting cloud costs are driving a shift toward localised AI. Enterprises want the power of Large Language Models (LLMs), but they cannot afford to send sensitive files over public networks.

Here’s where the Rhea: Latent Loom project comes in to play, a self-hosted LLM service designed to keep your data local, secure, and instantly searchable.

New release: 0.2.5 for Rhea: Latent Loom – self hosted LLM service

Following on our release for the Rhea: Latent Loom project this update 0.2.5 introduces:

Merges ingestion endpoints in to a single endpoint.
Additions to schemas.
New endpoint: /v1/rag/synthetic for generating synthetic questions used during knowledge ingestion.
Refinements to: /v1/search allowing vector distance threshold customisations.

New release Rhea: Latent Loom – self hosted LLM service

Announcing the Rhea: Latent Loom project a locally hosted LLM services that can be ran independently or with the Rhea Generative Framework. This post introduces the Rhea: Latent Loom API specification in OpenAPI (3.0.1) format. A Swagger UI Bundle is used to explore the API.

Rhea: Latent Loom can serve as drop-in replacement for AI cloud providers. Request responses are compatible with Chat Completion and Responses object types.