feat: add Eden AI as a first-class LLM provider and embedder

.env.example

@@ -16,6 +16,11 @@ OPENAI_API_KEY=your_openai_api_key_here
 DEEPSEEK_API_KEY=your_deepseek_api_key_here
 # DEEPSEEK_BASE_URL=https://api.deepseek.com
 
+# Eden AI (700+ models via one OpenAI-compatible key; EU-headquartered)
+EDENAI_API_KEY=your_edenai_api_key_here
+# Set the EU endpoint for data residency / GDPR-sensitive workloads:
+# EDENAI_BASE_URL=https://api.eu.edenai.run/v3
+
 # Ollama — no API key needed, but set base URL if non-default
 # OLLAMA_BASE_URL=http://localhost:11434
 

docs/CONFIG.md

@@ -216,6 +216,28 @@ export LITELLM_API_KEY="..."
 repowise init --provider litellm --model azure/gpt-4
 ```
 
+### Eden AI (700+ models, one key)
+
+[Eden AI](https://www.edenai.co/) is an EU-headquartered gateway exposing 700+
+models (Mistral, GPT, Claude, Gemini, Cohere, DeepSeek, Llama, …) through a
+single OpenAI-compatible endpoint. Models use `vendor/model` form.
+
+```bash
+export EDENAI_API_KEY="..."
+repowise init --provider edenai --model mistral/mistral-small-latest
+repowise init --provider edenai --model openai/gpt-5-mini --reasoning low
+```
+
+For data residency / GDPR-sensitive workloads, point at the EU endpoint:
+
+```bash
+export EDENAI_BASE_URL="https://api.eu.edenai.run/v3"
+repowise init --provider edenai --model mistral/mistral-small-latest
+```
+
+`reasoning` is forwarded as OpenAI `reasoning_effort` for OpenAI reasoning models
+routed through Eden (e.g. `openai/gpt-5*`); other models expose only `auto`.
+
 ### Provider auto-detection
 
 If you don't pass `--provider`, repowise detects your provider by checking, in
@@ -235,6 +257,8 @@ The embedder is separate from the LLM provider.
 |----------|---------|-------|
 | `gemini` | `GEMINI_API_KEY` | Default when key is present |
 | `openai` | `OPENAI_API_KEY` | OpenAI `text-embedding-3-small` |
+| `openrouter` | `OPENROUTER_API_KEY` | OpenRouter-hosted embeddings |
+| `edenai` | `EDENAI_API_KEY` | Eden AI gateway; `openai/text-embedding-3-small` (1536), `-3-large` (3072), `cohere/embed-multilingual-v3.0` (1024) |
 | `ollama` | `OLLAMA_EMBEDDING_MODEL` | Local Ollama embeddings, no API key |
 | `mock` | n/a | Dummy embeddings, no semantic search |
 
@@ -264,6 +288,8 @@ The `.repowise/.env` file is gitignored automatically.
 | `ANTHROPIC_API_KEY` | Anthropic API key |
 | `OPENAI_API_KEY` | OpenAI API key |
 | `GEMINI_API_KEY` / `GOOGLE_API_KEY` | Google Gemini API key |
+| `EDENAI_API_KEY` | Eden AI API key |
+| `EDENAI_BASE_URL` | Eden AI endpoint override (default: `https://api.edenai.run/v3`; EU: `https://api.eu.edenai.run/v3`) |
 | `OLLAMA_BASE_URL` | Ollama server URL (default: `http://localhost:11434`) |
 | `OLLAMA_EMBEDDING_MODEL` | Ollama embedding model (also selects the `ollama` embedder) |
 | `OLLAMA_EMBEDDING_DIMS` | Ollama embedding output dimensions (optional; inferred from the model otherwise) |

packages/cli/src/repowise/cli/helpers.py

@@ -689,6 +689,7 @@ def _resolve_base_url(name: str) -> str | None:
             "openai": ["OPENAI_BASE_URL"],
             "gemini": ["GEMINI_BASE_URL"],
             "deepseek": ["DEEPSEEK_BASE_URL"],
+            "edenai": ["EDENAI_BASE_URL"],
             "ollama": ["OLLAMA_BASE_URL"],
             "litellm": ["LITELLM_BASE_URL", "LITELLM_API_BASE"],
         }
@@ -736,6 +737,8 @@ def _resolve_base_url(name: str) -> str | None:
             kwargs["api_key"] = os.environ["OPENROUTER_API_KEY"]
         elif provider_name == "deepseek" and os.environ.get("DEEPSEEK_API_KEY"):
             kwargs["api_key"] = os.environ["DEEPSEEK_API_KEY"]
+        elif provider_name == "edenai" and os.environ.get("EDENAI_API_KEY"):
+            kwargs["api_key"] = os.environ["EDENAI_API_KEY"]
         elif provider_name == "litellm" and os.environ.get("LITELLM_API_KEY"):
             kwargs["api_key"] = os.environ["LITELLM_API_KEY"]
         elif provider_name == "ollama" and os.environ.get("OLLAMA_BASE_URL"):
@@ -797,11 +800,22 @@ def _resolve_base_url(name: str) -> str | None:
         if base_url:
             kwargs["base_url"] = base_url
         return get_provider("deepseek", **kwargs)
+    if os.environ.get("EDENAI_API_KEY") and os.environ["EDENAI_API_KEY"].strip():
+        kwargs = (
+            {"model": model, "api_key": os.environ["EDENAI_API_KEY"]}
+            if model
+            else {"api_key": os.environ["EDENAI_API_KEY"]}
+        )
+        base_url = _resolve_base_url("edenai")
+        if base_url:
+            kwargs["base_url"] = base_url
+        return get_provider("edenai", **kwargs)
 
     raise click.ClickException(
         "No provider configured. Use --provider, set REPOWISE_PROVIDER, "
         "or set ANTHROPIC_API_KEY / OPENAI_API_KEY / OPENROUTER_API_KEY / "
         "OLLAMA_BASE_URL / GEMINI_API_KEY / GOOGLE_API_KEY / DEEPSEEK_API_KEY / "
+        "EDENAI_API_KEY / "
         "LITELLM_API_KEY. Use REPOWISE_PROVIDER=codex_cli to use an authenticated "
         "Codex CLI subscription, or REPOWISE_PROVIDER=opencode to use opencode."
     )
@@ -839,6 +853,7 @@ def _is_env_var_exists(var_name: str) -> bool:
         "openai": ["OPENAI_API_KEY"],
         "openrouter": ["OPENROUTER_API_KEY"],
         "deepseek": ["DEEPSEEK_API_KEY"],
+        "edenai": ["EDENAI_API_KEY"],
         "gemini": ["GEMINI_API_KEY", "GOOGLE_API_KEY"],  # Either one
         "ollama": ["OLLAMA_BASE_URL"],
         "litellm": ["LITELLM_API_KEY"],  # May need others depending on backend

packages/cli/src/repowise/cli/providers/embedders.py

@@ -61,6 +61,8 @@ def resolve_embedder(embedder_flag: str | None) -> str:
         return "openai"
     if os.environ.get("OPENROUTER_API_KEY"):
         return "openrouter"
+    if os.environ.get("EDENAI_API_KEY"):
+        return "edenai"
     if os.environ.get("OLLAMA_EMBEDDING_MODEL"):
         return "ollama"
     return "mock"

packages/cli/src/repowise/cli/ui/provider_selection.py

@@ -25,6 +25,7 @@
     "openai": "gpt-5.4-nano",
     "anthropic": "claude-sonnet-4-6",
     "deepseek": "deepseek-v4-flash",
+    "edenai": "mistral/mistral-small-latest",
     "codex_cli": "codex_cli/default",
     "opencode": "opencode/default",
     "ollama": "llama3.2",
@@ -37,6 +38,7 @@
     "openai": "OPENAI_API_KEY",
     "anthropic": "ANTHROPIC_API_KEY",
     "deepseek": "DEEPSEEK_API_KEY",
+    "edenai": "EDENAI_API_KEY",
     "codex_cli": "__CODEX_CLI__",
     "opencode": "__OPENCODE_CLI__",
     "ollama": "OLLAMA_BASE_URL",
@@ -48,6 +50,7 @@
     "openai": "https://platform.openai.com/api-keys",
     "anthropic": "https://console.anthropic.com/settings/keys",
     "deepseek": "https://platform.deepseek.com/api_keys",
+    "edenai": "https://app.edenai.run/user/register",
     "codex_cli": "https://developers.openai.com/codex/cli",
     "opencode": "https://opencode.ai",
     "ollama": "https://ollama.com/download",

packages/core/src/repowise/core/providers/embedding/edenai.py

@@ -0,0 +1,108 @@
+"""Eden AI embedding support for repowise semantic search.
+
+Uses Eden AI's OpenAI-compatible endpoint at ``https://api.edenai.run/v3``.
+No additional pip install required — uses the ``openai`` package. Set
+``EDENAI_BASE_URL=https://api.eu.edenai.run/v3`` for EU data residency.
+
+Default model: openai/text-embedding-3-small (1536 dims)
+
+Usage:
+    from repowise.core.providers.embedding.edenai import EdenAIEmbedder
+
+    embedder = EdenAIEmbedder(api_key="...")
+    vectors = await embedder.embed(["some text"])
+"""
+
+from __future__ import annotations
+
+import asyncio
+import math
+import os
+
+
+class EdenAIEmbedder:
+    """Eden AI embedding adapter implementing the repowise Embedder protocol.
+
+    Args:
+        api_key:  Eden AI API key. Falls back to EDENAI_API_KEY env var.
+        model:    Embedding model in ``vendor/model`` form. Default:
+                  "openai/text-embedding-3-small".
+        base_url: Override the Eden AI API URL. Falls back to EDENAI_BASE_URL,
+                  then the global endpoint.
+    """
+
+    _DIMS: dict[str, int] = {
+        "openai/text-embedding-3-small": 1536,
+        "openai/text-embedding-3-large": 3072,
+        "cohere/embed-multilingual-v3.0": 1024,
+    }
+
+    _DEFAULT_BASE_URL: str = "https://api.edenai.run/v3"
+    _DEFAULT_TIMEOUT: float = 10.0
+
+    def __init__(
+        self,
+        api_key: str | None = None,
+        model: str = "openai/text-embedding-3-small",
+        base_url: str | None = None,
+        timeout: float = _DEFAULT_TIMEOUT,
+    ) -> None:
+        self._api_key = api_key or os.environ.get("EDENAI_API_KEY")
+        if not self._api_key:
+            raise ValueError(
+                "Eden AI API key required. Pass api_key= or set EDENAI_API_KEY env var."
+            )
+        if model not in self._DIMS:
+            known = ", ".join(sorted(self._DIMS))
+            raise ValueError(
+                f"Unknown embedding model {model!r}. Stored vectors would be mis-sized "
+                f"against the model's real output, silently corrupting the vector store. "
+                f"Add {model!r} to EdenAIEmbedder._DIMS with its correct dimension count, "
+                f"or pick a known model: {known}."
+            )
+        self._model = model
+        self._base_url = (base_url or os.environ.get("EDENAI_BASE_URL") or self._DEFAULT_BASE_URL).rstrip(
+            "/"
+        )
+        self._timeout = timeout
+        self._client: object | None = None
+
+    @property
+    def dimensions(self) -> int:
+        return self._DIMS[self._model]
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        """Embed a batch of texts using Eden AI.
+
+        Runs the synchronous SDK call in a thread pool to avoid blocking the
+        asyncio event loop.
+        """
+        if not texts:
+            return []
+
+        model = self._model
+        timeout = self._timeout
+        base_url = self._base_url
+
+        def _embed_sync() -> list[list[float]]:
+            import openai
+
+            if self._client is None:
+                self._client = openai.OpenAI(
+                    api_key=self._api_key,
+                    base_url=base_url,
+                    timeout=timeout,
+                )
+            response = self._client.embeddings.create(model=model, input=texts)  # type: ignore[union-attr]
+            raw_vectors = [list(item.embedding) for item in response.data]
+            return [_l2_normalize(v) for v in raw_vectors]
+
+        return await asyncio.to_thread(_embed_sync)
+
+
+def _l2_normalize(vec: list[float]) -> list[float]:
+    """L2-normalize a vector to unit length."""
+    norm = math.sqrt(sum(x * x for x in vec))
+    if norm == 0.0:
+        norm = 1.0
+    return [x / norm for x in vec]

packages/core/src/repowise/core/providers/embedding/registry.py

@@ -6,6 +6,7 @@
 Built-in embedders:
     openai  → OpenAIEmbedder  (text-embedding-3-small default)
     gemini  → GeminiEmbedder  (gemini-embedding-001 default)
+    edenai  → EdenAIEmbedder  (openai/text-embedding-3-small via Eden AI's EU gateway)
     mock    → MockEmbedder    (testing only, zero dependencies)
 
 Custom embedder registration:
@@ -28,6 +29,7 @@
     "gemini": ("repowise.core.providers.embedding.gemini", "GeminiEmbedder"),
     "ollama": ("repowise.core.providers.embedding.ollama", "OllamaEmbedder"),
     "openrouter": ("repowise.core.providers.embedding.openrouter", "OpenRouterEmbedder"),
+    "edenai": ("repowise.core.providers.embedding.edenai", "EdenAIEmbedder"),
     "mock": ("repowise.core.providers.embedding.base", "MockEmbedder"),
 }
 
@@ -81,6 +83,7 @@ def get_embedder(name: str, **kwargs: Any) -> Embedder:
         "gemini": "google-genai",
         "ollama": "httpx",
         "openrouter": "openai",  # openrouter uses the openai package
+        "edenai": "openai",  # edenai uses the openai package
     }
     try:
         module = importlib.import_module(module_path)

packages/core/src/repowise/core/providers/llm/__init__.py

@@ -14,6 +14,7 @@
     gemini     — gemini-3.1-flash-lite-preview, gemini-3-flash-preview, gemini-3.1-pro-preview
     openrouter — 200+ models via OpenRouter (anthropic/claude-sonnet-4.6, etc.)
     deepseek   — deepseek-v4-flash, deepseek-v4-pro via api.deepseek.com
+    edenai     — 700+ models (mistral/gpt/claude/gemini/...) via Eden AI's EU gateway
     ollama     — local inference (llama3.2, codellama, etc.)
     litellm    — 100+ providers via LiteLLM proxy
     codex_cli  — local authenticated Codex CLI via codex exec