hotfix(embeddings): name the 3-series models, drop unneeded auth, dec…

tests/requirements.txt

@@ -2,6 +2,7 @@ pytest
 pytest-cov
 pytest-xdist
 requests
+numpy
 openai
 langchain~=1.2
 langchain-openai~=1.1

v3/llms/embeddings.mdx

@@ -16,7 +16,7 @@
   proficiencyLevel="Intermediate"
   keywords={["Eden AI", "AI API", "LLM API", "chat completion", "OpenAI compatible"]}
   datePublished="2026-05-06T00:00:00Z"
-  dateModified="2026-05-07T00:00:00Z"
+  dateModified="2026-05-22T00:00:00Z"
 />
 
 Embeddings turn text into numerical vectors that capture meaning. They are the foundation for [RAG pipelines](#use-cases), semantic search, recommendations, and clustering.
@@ -37,7 +37,7 @@
 - **Semantic search** — match queries against documents by meaning, not keywords.
 - **Recommendations** — suggest similar products, articles, or songs based on description vectors.
 - **Clustering and topic discovery** — group thousands of texts by meaning without labels.
 - **Deduplication** — find near-duplicates that don't share exact wording.
 - **Anomaly detection** — flag inputs that look unlike anything in your corpus.
 - **Classification** — train a small classifier on top of frozen embeddings instead of fine-tuning a full model.
 
@@ -56,18 +56,14 @@
 ```python Python
 import requests
 
-response = requests.get(
-    "https://api.edenai.run/v3/embeddings/models",
-    headers={"Authorization": "Bearer YOUR_API_KEY"},
-)
+response = requests.get("https://api.edenai.run/v3/embeddings/models")
 
 for model in response.json()["data"]:
     print(model["id"], "-", model.get("context_length"))
 ```
 
 ```bash cURL
-curl https://api.edenai.run/v3/embeddings/models \
-  -H "Authorization: Bearer YOUR_API_KEY"
+curl https://api.edenai.run/v3/embeddings/models
 ```
 </CodeGroup>
 
@@ -83,9 +79,8 @@
 
 headers = {"Authorization": "Bearer YOUR_API_KEY"}
 
 model_id = requests.get(
     "https://api.edenai.run/v3/embeddings/models",
-    headers=headers,
 ).json()["data"][0]["id"]
 
 response = requests.post(
@@ -124,10 +119,9 @@
 API_KEY = "YOUR_API_KEY"
 headers = {"Authorization": f"Bearer {API_KEY}"}
 
-# 1. Pick a model from the catalog.
+# 1. Pick a model from the catalog (no auth required for /models).
 model_id = requests.get(
     "https://api.edenai.run/v3/embeddings/models",
-    headers=headers,
 ).json()["data"][0]["id"]
 
 # 2. Define a query and a corpus of documents to search over.
@@ -172,7 +166,7 @@
 | `model` | string | yes | `provider/model` from `/v3/embeddings/models`. |
 | `input` | string \| string[] \| int[] \| int[][] | yes | Text to embed, or a batch. Pre-tokenized integer inputs are also accepted. |
 | `encoding_format` | `"float"` \| `"base64"` | no | Defaults to `"float"`. `"base64"` reduces wire payload size. |
-| `dimensions` | integer | no | Truncate the output vector. Only supported by models that advertise it (3-series). |
+| `dimensions` | integer | no | Truncate the output vector. Only supported by models that advertise it (e.g. `openai/text-embedding-3-small` and `openai/text-embedding-3-large`). |
 | `user` | string | no | End-user identifier for abuse tracking. |
 | `metadata` | object | no | Eden extension. Free-form metadata stored with the request. |
 
@@ -217,7 +211,7 @@
 - **Small / fast / cheap** (e.g. `*-small` variants) — good default for most semantic-search workloads. Lower latency, lower cost per token, vectors are smaller so storage and dot products are faster.
 - **Large / higher quality** (e.g. `*-large` variants) — meaningfully better recall on hard retrieval tasks (long technical docs, multilingual corpora). Costs more and produces larger vectors.
 - **Context length** — long-context embedding models let you embed entire documents without chunking. Most 8k-context models still need chunking for paragraphs above ~2k tokens.
-- **Dimensions** — some 3-series models support a `dimensions` parameter to truncate outputs. Smaller vectors save storage and speed up similarity search at a small recall cost.
+- **Dimensions** — some models (e.g. `openai/text-embedding-3-small` and `openai/text-embedding-3-large`) support a `dimensions` parameter to truncate outputs. Smaller vectors save storage and speed up similarity search at a small recall cost.
 - **Multilingual** — verify language support in the model's `capabilities` before using it for non-English corpora.
 
 A common pattern: prototype with a small model, then A/B test a larger model on your evaluation set before committing to the storage cost.