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