rerank-english-v2
Use the rerank-english-v2 embedding or reranking model with Pinecone: specs and index setup. Good reranking model, consumes both a query and a list of.
Reranking models are designed to provide superior accuracy over retriever models but are much slower — so this model shouldn't be used with more than a few hundred documents. Due to the slowness of rerankers, we recommend using them in a two-stage retrieval system, ie use a retrieval to pull in a smaller number of documents from a larger database and then rerank the smaller number of documents using a reranker.
### Using the Model
#### Installation:
```python theme={null}
pip install -qU cohere==4.34
```
#### Rerank:
```python theme={null}
import cohere
co = cohere.Client("<<YOUR_COHERE_API_KEY>>")
reranked_docs = co.rerank(
query=query_str,
documents=docs,
top_n=5, # controls how many reranked docs to return
model="rerank-english-v2.0"
)
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