Feature/scaleway glm litellm dispatching

litellm_scaleway_dispatching/README.md

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+# Scaleway GLM dispatching with LiteLLM
+
+Small isolated integration to test a Scaleway GLM provider path inside a LiteLLM dispatching project.
+
+## What it adds
+
+- Scaleway GLM configuration from environment variables.
+- Validation for API key, model id, HTTPS base URL, `/v1` endpoint path and timeout.
+- LiteLLM completion wrapper using an OpenAI-compatible provider path.
+- Retry/backoff for transient errors such as rate limits, timeouts and server errors.
+- Error classification for auth, rate limit, timeout, server, bad request and unknown failures.
+- Fallback dispatching when the primary Scaleway GLM call fails.
+- Optional dispatch metrics with latency and per-attempt status.
+- Unit tests that do not call the real Scaleway API.
+
+## Environment variables
+
+Use these locally or as CI secrets:
+
+- `SCALEWAY_API_KEY`
+- `SCALEWAY_BASE_URL`, including the OpenAI-compatible `/v1` path
+- `SCALEWAY_GLM_MODEL`, for example `openai/<scaleway-model-id>`
+- `SCALEWAY_TIMEOUT_SECONDS`, optional positive integer
+
+Do not commit real secrets.
+
+## Example
+
+```python
+from litellm_scaleway_dispatching.scaleway_glm_dispatcher import (
+    RetryPolicy,
+    ScalewayGLMConfig,
+    dispatch_with_fallback,
+)
+
+result = dispatch_with_fallback(
+    "Return pong only.",
+    primary_config=ScalewayGLMConfig.from_env(),
+    fallback_models=["openai/gpt-4o-mini"],
+    retry_policy=RetryPolicy(max_retries=2, backoff_seconds=0.25),
+    return_metrics=True,
+    temperature=0,
+)
+
+print(result.selected_model)
+print(result.attempts)
+```
+
+## Run tests
+
+```bash
+python -m unittest discover -s scripts/python/tests -v
+```
+
+The tests mock the LiteLLM completion function, so they validate payload construction, retry, fallback and metrics behavior without consuming tokens.

litellm_scaleway_dispatching/__init__.py

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+"""LiteLLM Scaleway dispatching package."""