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Overview

The Cohere provider integrates Cohere’s reranking API with LlamaIndex.TS to improve the relevance of search results. Reranking is a powerful technique to reorder retrieved documents based on their relevance to a query.

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Installation

npm install @llamaindex/cohere

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Basic Usage

import { CohereRerank } from "@llamaindex/cohere";

const reranker = new CohereRerank({
  apiKey: process.env.COHERE_API_KEY,
  topN: 5,
  model: "rerank-english-v2.0"
});

// Use as a node postprocessor
const rerankedNodes = await reranker.postprocessNodes(nodes, query);

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Constructor Options

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apiKey
string
required
Cohere API key (no environment variable default - must be provided)
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topN
number
default:2
Number of top results to return after reranking
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model
string
default:"rerank-english-v2.0"
Cohere rerank model to use
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baseUrl
string
Optional custom API endpoint URL
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timeout
number
Request timeout in seconds

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Supported Models

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Rerank Models

  • rerank-english-v2.0: General-purpose English reranking (default)
  • rerank-multilingual-v2.0: Multilingual reranking support
  • rerank-english-v3.0: Latest English model
  • rerank-multilingual-v3.0: Latest multilingual model

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With Query Engine

import { VectorStoreIndex } from "llamaindex";
import { CohereRerank } from "@llamaindex/cohere";

const index = await VectorStoreIndex.fromDocuments(documents);

const reranker = new CohereRerank({
  apiKey: process.env.COHERE_API_KEY,
  topN: 3,
  model: "rerank-english-v3.0"
});

const queryEngine = index.asQueryEngine({
  nodePostprocessors: [reranker]
});

const response = await queryEngine.query({
  query: "What are the main features?"
});

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With Retriever

import { VectorStoreIndex } from "llamaindex";
import { CohereRerank } from "@llamaindex/cohere";

const index = await VectorStoreIndex.fromDocuments(documents);
const retriever = index.asRetriever({ similarityTopK: 10 });

const reranker = new CohereRerank({
  apiKey: process.env.COHERE_API_KEY,
  topN: 5
});

// Retrieve initial results
const nodes = await retriever.retrieve({ query: "user query" });

// Rerank for better relevance
const rerankedNodes = await reranker.postprocessNodes(
  nodes,
  "user query"
);

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Multilingual Reranking

const reranker = new CohereRerank({
  apiKey: process.env.COHERE_API_KEY,
  topN: 5,
  model: "rerank-multilingual-v3.0"
});

const rerankedNodes = await reranker.postprocessNodes(
  nodes,
  "Quelles sont les principales caractéristiques?" // French query
);

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Custom Base URL

const reranker = new CohereRerank({
  apiKey: process.env.COHERE_API_KEY,
  baseUrl: "https://custom-cohere-endpoint.com",
  topN: 3
});

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Configuration

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Environment Variables

COHERE_API_KEY=your-api-key-here
Note: Unlike other providers, the Cohere package does not automatically read from environment variables. You must explicitly pass the API key.

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How Reranking Works

  1. Initial Retrieval: Your retriever fetches top-K documents (e.g., 10-20)
  2. Reranking: Cohere’s model re-scores each document for relevance
  3. Top-N Selection: Returns only the most relevant N documents
  4. Score Update: Updates each node’s score to the relevance score
// Before reranking: 10 documents with embedding similarity scores
const initialNodes = await retriever.retrieve({ query, similarityTopK: 10 });

// After reranking: 3 most relevant documents with Cohere relevance scores
const reranker = new CohereRerank({ apiKey, topN: 3 });
const finalNodes = await reranker.postprocessNodes(initialNodes, query);

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Performance Tips

  1. Retrieve more, rerank to fewer: Retrieve 10-20 documents, rerank to top 3-5
  2. Use for complex queries: Most beneficial when semantic search alone isn’t sufficient
  3. Choose right model: v3.0 models offer better quality, v2.0 is faster
  4. Set appropriate timeout: For large document sets, increase timeout

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Error Handling

try {
  const rerankedNodes = await reranker.postprocessNodes(nodes, query);
} catch (error) {
  if (error.message.includes("API key")) {
    console.error("Invalid or missing Cohere API key");
  } else {
    console.error("Reranking failed:", error.message);
  }
}

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Use Cases

  • Improve RAG quality: Rerank retrieved documents before sending to LLM
  • Multi-stage retrieval: First pass with embeddings, second pass with reranking
  • Cross-lingual search: Use multilingual models for queries in different languages
  • Semantic search refinement: Improve relevance beyond vector similarity

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Best Practices

  1. Always provide a query: Reranking requires a query string to work
  2. Retrieve enough candidates: Aim for 10-20 initial results for best reranking
  3. Don’t over-rerank: Top 3-5 results usually sufficient for most use cases
  4. Handle empty results: Check if initial retrieval returns documents
  5. Monitor costs: Reranking adds API costs, use judiciously

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See Also