> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Config

> Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings.

## Common Configuration Parameters

All rerankers share these common configuration parameters:

| Parameter | Description | Type | Default |
| - | - | - | - |
| `provider` | Reranker provider name | `str` | Required |
| `top_k` | Maximum number of results to return after reranking | `int` | `None` |
| `api_key` | API key for the reranker service | `str` | `None` |

## Provider-Specific Configuration

### Zero Entropy

| Parameter | Description | Type | Default |
| - | - | - | - |
| `model` | Model to use: `zerank-1` or `zerank-1-small` | `str` | `"zerank-1"` |
| `api_key` | Zero Entropy API key | `str` | `None` |

### Cohere

| Parameter | Description | Type | Default |
| - | - | - | - |
| `model` | Cohere rerank model | `str` | `"rerank-v3.5"` |
| `api_key` | Cohere API key | `str` | `None` |
| `return_documents` | Whether to return document texts in response | `bool` | `False` |
| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |

### Sentence Transformer

| Parameter | Description | Type | Default |
| - | - | - | - |
| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
| `batch_size` | Batch size for processing | `int` | `32` |
| `show_progress_bar` | Show progress during processing | `bool` | `False` |

### Hugging Face

| Parameter | Description | Type | Default |
| - | - | - | - |
| `model` | HuggingFace reranker model name | `str` | `"BAAI/bge-reranker-large"` |
| `api_key` | HuggingFace API token | `str` | `None` |
| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |

### LLM-based

| Parameter | Description | Type | Default |
| - | - | - | - |
| `model` | LLM model to use for scoring | `str` | `"gpt-5-mini"` |
| `provider` | LLM provider (`openai`, `anthropic`, etc.) | `str` | `"openai"` |
| `api_key` | API key for LLM provider | `str` | `None` |
| `temperature` | Temperature for LLM generation | `float` | `0.0` |
| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
| `scoring_prompt` | Custom prompt template for scoring | `str` | Default scoring prompt |

### LLM Reranker

| Parameter | Description | Type | Default |
| - | - | - | - |
| `llm.provider` | LLM provider for reranking | `str` | Required |
| `llm.config` | LLM configuration object | `dict` | Required |
| `top_n` | Number of results to return | `int` | `None` |

## Environment Variables

You can set API keys using environment variables:

* `ZERO_ENTROPY_API_KEY` - Zero Entropy API key
* `COHERE_API_KEY` - Cohere API key
* `HUGGINGFACE_API_KEY` - HuggingFace API token
* `OPENAI_API_KEY` - OpenAI API key (for LLM-based reranker)
* `ANTHROPIC_API_KEY` - Anthropic API key (for LLM-based reranker)

## Basic Configuration Example

```python Python theme={null}
config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_memories",
            "path": "./chroma_db"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-5-mini"
        }
    },
    "reranker": {
        "provider": "zero_entropy",
        "config": {
            "model": "zerank-1",
            "top_k": 5
        }
    }
}
```

## TypeScript SDK

The self-hosted [TypeScript SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) supports the same five providers. Config keys are camelCase (`apiKey`, `topK`, `maxLength`) and each provider's SDK is a peer dependency you install per reranker.

| Provider | Install | Default model | Key config fields |
| - | - | - | - |
| `cohere` | `pnpm add cohere-ai` | `rerank-v3.5` | `apiKey`, `model`, `topK` |
| `zero_entropy` | `pnpm add zeroentropy` | `zerank-1` | `apiKey`, `model`, `topK` |
| `sentence_transformer` | `pnpm add @huggingface/transformers` | `Xenova/ms-marco-MiniLM-L-6-v2` | `model`, `device`, `maxLength`, `normalize`, `topK` |
| `huggingface` | `pnpm add @huggingface/transformers` | `Xenova/bge-reranker-base` | `model`, `device`, `maxLength`, `normalize`, `topK` |
| `llm_reranker` | None (uses your LLM provider's own SDK) | `openai` / `gpt-5-mini` | `provider`, `model`, `apiKey`, `llm` (nested override), `topK` |

```typescript theme={null}
import { Memory } from "mem0ai/oss";

const memory = new Memory({
  reranker: {
    provider: "zero_entropy",
    config: { apiKey: process.env.ZERO_ENTROPY_API_KEY, topK: 5 },
  },
});
```

<Note>
  The local cross-encoder providers (`sentence_transformer`, `huggingface`) run on [Transformers.js](https://huggingface.co/docs/transformers.js) and default to ONNX (`Xenova/*`) model mirrors, so Python default model strings must be swapped for their ONNX equivalents. `batchSize` and `showProgressBar` are accepted for parity with Python but are no-ops in the TypeScript runtime. See the [reranker feature guide](/open-source/features/reranker-search#typescript-sdk) for full examples.
</Note>


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.