Rerank Memory
Provides a memory reranking API based on the memos-reranker small model. It takes a user query and a list of candidate memories and completes memory relevance reranking with a single call.
POST
import os
import requests
import json
# Replace with your API Key
os.environ["MEMOS_API_KEY"] = "YOUR_API_KEY"
os.environ["MEMOS_BASE_URL"] = "https://memos.memtensor.cn/api/openmem/v1"
url = f"{os.environ['MEMOS_BASE_URL']}/rerank"
payload = {
"model": "memos-reranker-0.6b",
"query": "What are the user's hobbies?",
"documents": [
"User likes playing badminton",
"User is a backend developer in Hangzhou",
"User prefers concise replies",
"User prefers Jiangxiang-flavored baijiu",
"User is going on a business trip to Beijing next Wednesday"
]
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Token {os.environ['MEMOS_API_KEY']}"
}
response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())
curl --request POST \
--url https://memos.memtensor.cn/api/openmem/v1/rerank \
--header 'Authorization: Token YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "memos-reranker-0.6b",
"query": "What are the user'\''s hobbies?",
"documents": [
"User likes playing badminton",
"User is a backend developer in Hangzhou",
"User prefers concise replies",
"User prefers Jiangxiang-flavored baijiu",
"User is going on a business trip to Beijing next Wednesday"
]
}'
{
"id": "<string>",
"model": "<string>",
"usage": {
"prompt_tokens": 0,
"total_tokens": 0
},
"results": [
{
"index": 0,
"document": {
"text": "<string>"
},
"relevance_score": 0
}
]
}Authorizations
Authorization
string
header
required
Token API_key, available in API Console > API Keys
Body
application/json
model
string
Model name to use.
Enum:"memos-reranker-0.6b""memos-reranker-4b"
query
string
required
The user query.
documents
string[]
required
A list of document texts to rerank.
top_n
number
Return the top N most relevant results. If omitted, returns all results by default.
Response
application/json
Successful Response
id
string
required
Unique identifier for this request.
model
string
required
Model name used.
usage
object
required
Token usage statistics.
Show child attributes
results
RerankResult·object[]
required
List of reranked results, sorted in descending order by relevance_score.
Show child attributes
import os
import requests
import json
# Replace with your API Key
os.environ["MEMOS_API_KEY"] = "YOUR_API_KEY"
os.environ["MEMOS_BASE_URL"] = "https://memos.memtensor.cn/api/openmem/v1"
url = f"{os.environ['MEMOS_BASE_URL']}/rerank"
payload = {
"model": "memos-reranker-0.6b",
"query": "What are the user's hobbies?",
"documents": [
"User likes playing badminton",
"User is a backend developer in Hangzhou",
"User prefers concise replies",
"User prefers Jiangxiang-flavored baijiu",
"User is going on a business trip to Beijing next Wednesday"
]
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Token {os.environ['MEMOS_API_KEY']}"
}
response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())
curl --request POST \
--url https://memos.memtensor.cn/api/openmem/v1/rerank \
--header 'Authorization: Token YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "memos-reranker-0.6b",
"query": "What are the user'\''s hobbies?",
"documents": [
"User likes playing badminton",
"User is a backend developer in Hangzhou",
"User prefers concise replies",
"User prefers Jiangxiang-flavored baijiu",
"User is going on a business trip to Beijing next Wednesday"
]
}'
{
"id": "<string>",
"model": "<string>",
"usage": {
"prompt_tokens": 0,
"total_tokens": 0
},
"results": [
{
"index": 0,
"document": {
"text": "<string>"
},
"relevance_score": 0
}
]
}