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ea8c728 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # Local and OpenRouter Models
Shinka supports dynamic LLM backend routing in `LLMClient` and `AsyncLLMClient`.
It also supports dynamic embedding backend routing in `EmbeddingClient` and
`AsyncEmbeddingClient`.
You can use:
- models listed in the provider pricing CSVs (existing behavior)
- dynamic OpenRouter model IDs
- local OpenAI-compatible servers via inline endpoint URIs
## Supported Model Name Formats
### 1) Known models (from `pricing.csv`)
```yaml
evo_config:
llm_models:
- gpt-5-mini
- claude-sonnet-4-6
```
### 2) Dynamic OpenRouter models
Prefix with `openrouter/`:
```yaml
evo_config:
llm_models:
- openrouter/qwen/qwen3-coder
- openrouter/deepseek/deepseek-r1
```
Set env var:
```bash
OPENROUTER_API_KEY=...
```
### 3) Local OpenAI-compatible models
Use `local/<model>@<http(s)://endpoint>`:
```yaml
evo_config:
llm_models:
- local/qwen2.5-coder@http://localhost:11434/v1
```
Set optional env var:
```bash
LOCAL_OPENAI_API_KEY=local
```
If not set, Shinka uses `"local"` as a default token.
## Local Embeddings
The same inline local format also works for `embedding_model`.
```yaml
evo_config:
embedding_model: local/text-embeddings-inference@http://localhost:8080/v1
```
Common local embedding backends:
- Hugging Face TEI:
`local/text-embeddings-inference@http://localhost:8080/v1`
- vLLM or another OpenAI-compatible embedding server:
`local/BAAI/bge-small-en-v1.5@http://localhost:8000/v1`
- Ollama OpenAI-compatible endpoint:
`local/embeddinggemma@http://localhost:11434/v1`
## Notes
- Dynamic OpenRouter/local model IDs are allowed even if not listed in `pricing.csv`.
- If a model has no pricing entry and the provider does not return cost metadata, Shinka records cost as `0.0`.
- Local OpenAI-compatible backend path currently uses chat-completions style calls.
- Local embedding backends use the OpenAI-compatible `/v1/embeddings` path.
- Structured output is not supported yet for `local/...@...` models.
## Applies to Which Clients
These formats work across all LLM consumers that use `LLMClient` / `AsyncLLMClient`, including:
- mutation LLMs (`llm_models`)
- meta LLMs (`meta_llm_models`)
- novelty judge LLMs (`novelty_llm_models`)
- prompt evolution LLMs (`prompt_llm_models`)
For embeddings, the same format applies to:
- code similarity embeddings (`embedding_model`)
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