> ## Documentation Index
> Fetch the complete documentation index at: https://www.edenai.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Repowise

> Run Repowise codebase intelligence on Eden AI, for both the LLM and the embedder, with an EU endpoint.

export const TechArticleSchema = ({title, description, path, articleSection, about, proficiencyLevel = "Beginner", dependencies, keywords = [], datePublished, dateModified, image, inLanguage = "en"}) => {
  const baseUrl = "https://www.edenai.co/docs";
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  const ogParams = new URLSearchParams({
    division: articleSection || "",
    title: title || "",
    description: description || ""
  });
  const resolvedImage = image || `https://edenai.mintlify.app/_mintlify/api/og?${ogParams.toString()}`;
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    "@type": "TechArticle",
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    mainEntityOfPage: {
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      "@id": canonicalUrl
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    headline: title,
    name: title,
    description: description,
    url: canonicalUrl,
    inLanguage: inLanguage,
    isPartOf: {
      "@type": "WebSite",
      name: "Eden AI Documentation",
      url: baseUrl
    },
    author: [{
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/"
    }],
    publisher: {
      "@type": "Organization",
      name: "Eden AI",
      url: "https://www.edenai.co/",
      logo: {
        "@type": "ImageObject",
        url: "https://www.edenai.co/assets/logo.png"
      }
    }
  };
  if (articleSection) data.articleSection = articleSection;
  if (about) data.about = {
    "@type": "Thing",
    name: about
  };
  if (proficiencyLevel) data.proficiencyLevel = proficiencyLevel;
  if (dependencies) data.dependencies = dependencies;
  if (keywords && keywords.length) data.keywords = keywords;
  if (datePublished) data.datePublished = datePublished;
  if (dateModified) data.dateModified = dateModified;
  data.image = Array.isArray(resolvedImage) ? resolvedImage : [resolvedImage];
  const json = JSON.stringify(data);
  const schemaId = `techarticle-${canonicalUrl}`;
  React.useEffect(() => {
    if (typeof document === "undefined") return;
    document.querySelectorAll(`script[data-schema-id="${schemaId}"]`).forEach(n => n.remove());
    const script = document.createElement("script");
    script.type = "application/ld+json";
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    script.textContent = json;
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<TechArticleSchema title={"Repowise"} description={"Run Repowise codebase intelligence on Eden AI, for both the LLM and the embedder, with an EU endpoint."} path="v3/integrations/repowise" articleSection="AI Frameworks" about={"LLM Framework Integration"} proficiencyLevel="Intermediate" keywords={["Eden AI", "AI API", "Repowise", "Embeddings", "Semantic Search"]} datePublished="2026-08-31T00:00:00Z" dateModified="2026-08-31T00:00:00Z" />

Run [Repowise](https://github.com/repowise-dev/repowise) codebase intelligence on Eden AI, for both the LLM and the embedder, with an EU endpoint.

## Overview

Repowise reads a repository and produces code health scores, generated documentation and git analytics. Eden AI is a **built-in provider on both sides**, which is unusual: one key covers the language model and the embedder used for semantic search.

* **500+ models**: reach OpenAI, Anthropic, Google, Mistral and more through one key
* **LLM and embedder from the same key**: no second account for embeddings
* **EU endpoint**: `EDENAI_BASE_URL` covers both the LLM and the embedder in one setting

## Prerequisites

* Repowise installed. See the [Repowise repository](https://github.com/repowise-dev/repowise)
* An Eden AI API key from [app.edenai.run](https://app.edenai.run) → **API Keys**

## Setup

### 1. Export your key

<CodeGroup>
  ```bash Terminal theme={null}
  export EDENAI_API_KEY="your_api_key_here"
  ```
</CodeGroup>

### 2. Initialise with Eden AI

<CodeGroup>
  ```bash Terminal theme={null}
  repowise init --provider edenai --model mistral/mistral-small-latest
  ```
</CodeGroup>

Reasoning models are supported with an explicit effort level:

<CodeGroup>
  ```bash Terminal theme={null}
  repowise init --provider edenai --model openai/gpt-5-mini --reasoning low
  ```
</CodeGroup>

## Model naming

Eden AI addresses models as `vendor/model`, and Repowise passes the identifier through unchanged. The live catalogues are public and need no key:

<CodeGroup>
  ```bash Chat models theme={null}
  curl https://api.edenai.run/v3/models
  ```

  ```bash Embedding models theme={null}
  curl https://api.edenai.run/v3/embeddings/models
  ```
</CodeGroup>

<Note>
  The chat and embedding catalogues are **separate endpoints**. An embedding model will not appear in `/v3/models`, which is a common source of confusion when picking one.
</Note>

## Embeddings for semantic search

The same key also selects `edenai` as the embedder. Pick a model with `REPOWISE_EMBEDDING_MODEL`:

<CodeGroup>
  ```bash .env theme={null}
  export EDENAI_API_KEY="your_api_key_here"
  export REPOWISE_EMBEDDING_MODEL="google/gemini-embedding-001"
  ```
</CodeGroup>

The embedder can also be chosen explicitly, and Eden AI is one of the accepted values alongside `openai`, `gemini`, `ollama`, `openrouter` and `mock`:

<CodeGroup>
  ```bash Terminal theme={null}
  repowise init --embedder edenai
  ```
</CodeGroup>

<Note>
  A model's vector width is a property of the gateway serving it, not of the model name alone. The same identifier can return a different number of dimensions through two different gateways, so let Repowise read the width from Eden AI rather than copying a figure from another provider's table.
</Note>

## EU data residency

One variable covers both the LLM and the embedder:

<CodeGroup>
  ```bash .env theme={null}
  export EDENAI_API_KEY="your_api_key_here"
  export EDENAI_BASE_URL="https://api.eu.edenai.run/v3"
  ```
</CodeGroup>

The EU endpoint serves the subset of the catalogue available in the EU, so a model that works on the global endpoint is not guaranteed to be reachable through it. This is the main reason to reach for Eden AI here: the other hosted providers Repowise supports terminate outside the EU.

## Troubleshooting

### `401 Unauthorized`

Check that `EDENAI_API_KEY` is exported in the shell Repowise runs from, and that the value is an Eden AI key rather than a vendor key.

### `model not found`

Copy the identifier exactly as the catalogue returns it, vendor prefix included, and make sure you read the right catalogue: chat models from `/v3/models`, embedding models from `/v3/embeddings/models`.

### Semantic search returns nothing useful

Confirm the embedder actually resolved to `edenai` rather than falling back. An unset or unrecognised embedder value silently degrades to a keyless stub, which indexes without producing real vectors.

## Next Steps

* [Chat Completions](/docs/v3/llms/chat-completions) - Core LLM endpoint
* [Embeddings](/docs/v3/llms/embeddings) - Vector embeddings endpoint
* [List LLM Models](/docs/v3/llms/listing-models) - Browse available providers and models
* [aisuite](/docs/v3/integrations/aisuite) - Unified LLM interface on Eden AI