mirror of https://github.com/whit3rabbit/anyllm-proxy.git synced 2026-10-06 16:00:23 +00:00

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whit3rabbitandClaude Sonnet 4.6 545bdbbbd3 feat: add bedrock native passthrough, generic passthrough, provider docs, and managed backend fixes

Adds bedrock_native.rs (Converse/InvokeModel with SigV4) and
generic_passthrough.rs catch-all for Translate mode. Adds comprehensive
provider reference docs (docs/providers/, docs/ENDPOINTS.md). Fixes
managed backend admin UI (BackendForm, ManagedBackendsSection) and
admin route/model handler issues. Adds automated model pricing update
workflow (scripts/update_pricing.py, .github/workflows/update-pricing.yml).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-04-08 16:36:16 -05:00

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Voyage AI

Embeddings-only provider with models optimized for retrieval and semantic search.

LiteLLM prefix: voyage/
Status: Stub — routes through OpenAI-compatible client
Docs: https://docs.voyageai.com

Authentication

Variable	Required	Description
`VOYAGE_API_KEY`	Yes	API key from dash.voyageai.com

Quick Start

Single-Backend (env vars)

BACKEND=voyage VOYAGE_API_KEY=your-key cargo run -p anyllm_proxy
# Docker:
docker run -e BACKEND=voyage -e VOYAGE_API_KEY=your-key -e PROXY_OPEN_RELAY=true -p 3000:3000 followthewhit3rabbit/anyllm-proxy

LiteLLM YAML Config

model_list:
  - model_name: voyage-3
    litellm_params:
      model: voyage/voyage-3
      api_key: "env:VOYAGE_API_KEY"

Usage Examples

OpenAI Embeddings API

curl http://localhost:3000/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PROXY_API_KEYS" \
  -d '{"model": "voyage-3", "input": "The quick brown fox"}'

Capabilities

Feature	Supported
Chat Completions	—
Streaming	—
Tool Use	—
Embeddings	✓
Vision	—
Batch	—

Notable Models

Model ID	Context	Notes
`voyage-3`	32k	General-purpose, highest accuracy
`voyage-3-lite`	32k	Faster, lower cost
`voyage-code-3`	32k	Optimized for code retrieval
`voyage-multimodal-3`	32k	Text and image embeddings

Notes

Voyage AI is embeddings-only — chat completions are not supported. Use the /v1/embeddings endpoint. Do not set this as BACKEND for chat workloads. If you need both embeddings and chat in the same config, use a LiteLLM YAML with Voyage for embedding model entries and a separate provider for chat model entries.

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