feat: add MiniMax as embedding and LLM (genie) provider with M3 as default

docs/oss/embeddings/minimax-embeddings.mdx

@@ -0,0 +1,71 @@
+---
+title: MiniMaxEmbeddings
+description: MiniMaxEmbeddings is a utility for embedding chunks using MiniMax's embo-01 model.
+icon: "brain-circuit"
+iconType: "solid"
+---
+
+MiniMaxEmbeddings provides access to [MiniMax](https://www.minimax.io/)'s embedding API for Chonkie's semantic chunking. It uses the **embo-01** model with 1536-dimensional vectors.
+
+MiniMax's embedding API distinguishes between `"db"` (storage) and `"query"` (search) embedding types, enabling asymmetric retrieval for better search quality.
+
+## Installation
+
+MiniMaxEmbeddings uses `httpx` (a core dependency), so no extra packages are needed:
+
+```bash
+pip install chonkie
+```
+
+## Usage
+
+```python
+from chonkie import MiniMaxEmbeddings
+
+# Initialize with API key (or set MINIMAX_API_KEY env var)
+embeddings = MiniMaxEmbeddings(api_key="your-minimax-api-key")
+
+# Embed a single text
+vector = embeddings.embed("Hello, world!")
+
+# Batch embed multiple texts
+vectors = embeddings.embed_batch(["text 1", "text 2", "text 3"])
+
+# Use with semantic chunker
+from chonkie import SemanticChunker
+
+chunker = SemanticChunker(embeddings)
+chunks = chunker("Your long text here...")
+```
+
+### Search-optimized embeddings
+
+Use `embedding_type="query"` when embedding search queries for asymmetric retrieval:
+
+```python
+# For storing documents
+db_embeddings = MiniMaxEmbeddings(embedding_type="db")
+doc_vectors = db_embeddings.embed_batch(documents)
+
+# For search queries
+query_embeddings = MiniMaxEmbeddings(embedding_type="query")
+query_vector = query_embeddings.embed("search query")
+```
+
+### Using the provider URI
+
+```python
+from chonkie import AutoEmbeddings
+
+# Auto-detect MiniMax from model name
+embeddings = AutoEmbeddings.get_embeddings("embo-01", api_key="your-key")
+
+# Or use provider prefix
+embeddings = AutoEmbeddings.get_embeddings("minimax://embo-01", api_key="your-key")
+```
+
+## Available Models
+
+| Model | Dimensions | Description |
+|-------|-----------|-------------|
+| `embo-01` | 1536 | General-purpose embedding model |

docs/oss/embeddings/overview.mdx

@@ -87,6 +87,13 @@ Embeddings handlers require additional dependencies. See the [Installation Guide
   >
     Embed text using VoyageAI embeddings (requires `voyageai`).
   </Card>
+  <Card
+    title="MiniMaxEmbeddings"
+    icon="brain-circuit"
+    href="/oss/embeddings/minimax-embeddings"
+  >
+    Embed text using MiniMax embeddings (embo-01, 1536 dimensions).
+  </Card>
 </CardGroup>
 
 ## Common Interface

pyproject.toml

@@ -116,6 +116,7 @@ cohere = ["tokenizers>=0.16.0", "cohere>=5.13.0"]
 jina = ["tokenizers>=0.16.0"]
 catsu = ["catsu>=0.0.1"]
 litellm = ["litellm>=1.0.0", "tiktoken>=0.5.0", "tokenizers>=0.16.0"]
+minimax = []  # uses httpx (core dep) for native MiniMax API
 
 # Optional dependencies for the friends
 chroma = ["chromadb>=1.0.0"]
@@ -156,6 +157,7 @@ all = [
     "chonkie[jina]",
     "chonkie[catsu]",
     "chonkie[litellm]",
+    "chonkie[minimax]",
     "chonkie[chroma]",
     "chonkie[qdrant]",
     "chonkie[tpuf]",

src/chonkie/__init__.py

@@ -23,13 +23,14 @@
     GeminiEmbeddings,
     JinaEmbeddings,
     LiteLLMEmbeddings,
+    MiniMaxEmbeddings,
     Model2VecEmbeddings,
     OpenAIEmbeddings,
     SentenceTransformerEmbeddings,
     VoyageAIEmbeddings,
 )
 from .fetcher import BaseFetcher, FileFetcher
-from .genie import AzureOpenAIGenie, BaseGenie, CerebrasGenie, GeminiGenie, GroqGenie, OpenAIGenie
+from .genie import AzureOpenAIGenie, BaseGenie, CerebrasGenie, GeminiGenie, GroqGenie, MiniMaxGenie, OpenAIGenie
 from .handshakes import (
     BaseHandshake,
     ChromaHandshake,
@@ -99,6 +100,7 @@
     "GeminiEmbeddings",
     "JinaEmbeddings",
     "LiteLLMEmbeddings",
+    "MiniMaxEmbeddings",
     "Model2VecEmbeddings",
     "OpenAIEmbeddings",
     "SentenceTransformerEmbeddings",
@@ -112,6 +114,7 @@
     "CerebrasGenie",
     "GeminiGenie",
     "GroqGenie",
+    "MiniMaxGenie",
     "OpenAIGenie",
     # handshakes
     "BaseHandshake",

src/chonkie/embeddings/__init__.py

@@ -10,6 +10,7 @@
 from .gemini import GeminiEmbeddings
 from .jina import JinaEmbeddings
 from .litellm import LiteLLMEmbeddings
+from .minimax import MiniMaxEmbeddings
 from .mistral import MistralEmbeddings
 from .mixedbread import MixedbreadEmbeddings
 from .model2vec import Model2VecEmbeddings
@@ -41,4 +42,5 @@
     "NomicEmbeddings",
     "DeepInfraEmbeddings",
     "CloudflareEmbeddings",
+    "MiniMaxEmbeddings",
 ]