37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
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import cohere
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from typing import List
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import numpy as np
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from config import settings
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class CohereEmbeddings:
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def __init__(self):
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self.settings = settings
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self.client = cohere.Client(self.settings.COHERE_API_KEY)
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def generate_embedding(self, text: str) -> np.ndarray:
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"""Generate embeddings for a single text using Cohere."""
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response = self.client.embed(
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texts=[text],
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model="embed-english-v3.0",
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input_type="search_document"
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)
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return np.array(response.embeddings[0])
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def rerank_results(self, query: str, documents: List[str], top_n: int = 5) -> List[str]:
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"""Rerank documents based on relevance to the query."""
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results = self.client.rerank(
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query=query,
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documents=documents,
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top_n=top_n,
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model="rerank-english-v2.0"
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)
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# Extract the reranked documents in order
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reranked_docs = []
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for result in results.results:
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# Get the document at the index returned by the rerank API
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doc_index = result.index
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if 0 <= doc_index < len(documents):
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reranked_docs.append(documents[doc_index])
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return reranked_docs
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