1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
|
import os
from typing import List
import cohere
from loguru import logger as log
from rag.message import Message
from rag.retriever.rerank.abstract import AbstractReranker
from rag.retriever.vector import Document
class CohereReranker(metaclass=AbstractReranker):
def __init__(self) -> None:
self.client = cohere.Client(os.environ["COHERE_API_KEY"])
self.top_k = int(os.environ["RERANK_TOP_K"])
self.relevance_threshold = float(os.environ["RETRIEVER_RELEVANCE_THRESHOLD"])
def rerank_documents(self, query: str, documents: List[Document]) -> List[str]:
response = self.client.rerank(
model="rerank-english-v3.0",
query=query,
documents=[d.text for d in documents],
top_n=self.top_k,
)
ranking = list(
filter(
lambda x: x.relevance_score > self.relevance_threshold,
response.results,
)
)
log.debug(
f"Reranking gave {len(ranking)} relevant documents of {len(documents)}"
)
return [documents[r.index] for r in ranking]
def rerank_messages(self, query: str, messages: List[Message]) -> List[Message]:
response = self.client.rerank(
model="rerank-english-v3.0",
query=query,
documents=[m.content for m in messages],
top_n=self.top_k,
)
ranking = list(
filter(
lambda x: x.relevance_score > self.relevance_threshold,
response.results,
)
)
log.debug(
f"Reranking gave {len(ranking)} relevant chat messages of {len(messages)}"
)
return [messages[r.index] for r in ranking]
|
