Search Index for Documents
curl --request GET \
--url https://secureai.hiperai.ai/api/external/indexes/{indexName}/search \
--header 'Authorization: Bearer <token>'import requests
url = "https://secureai.hiperai.ai/api/external/indexes/{indexName}/search"
headers = {"Authorization": "Bearer <token>"}
response = requests.get(url, headers=headers)
print(response.text)const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};
fetch('https://secureai.hiperai.ai/api/external/indexes/{indexName}/search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"success": true,
"request_id": "550e8400-e29b-41d4-a716-446655440000",
"query": "What is machine learning?",
"results": {
"matches": [
{
"rank": 1,
"score": 0.85,
"source": "training",
"content": "Machine learning is a subset of artificial intelligence...",
"metadata": {
"page": 1,
"chunkIndex": 0,
"title": "Introduction to ML",
"documentId": "60a7c8f5e8b4f5001f7a8c26"
}
}
],
"total": 5,
"top_k": 10
},
"index": {
"name": "my-knowledge-base",
"namespace": "user-60a7c8f5e8b4f5001f7a8c24-index-my-knowledge-base"
}
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}Base de Connaissances & RAG
Index de recherche de documents
Search documents within an index using semantic search. Returns matching documents with relevance scores.
GET
/
indexes
/
{indexName}
/
search
Search Index for Documents
curl --request GET \
--url https://secureai.hiperai.ai/api/external/indexes/{indexName}/search \
--header 'Authorization: Bearer <token>'import requests
url = "https://secureai.hiperai.ai/api/external/indexes/{indexName}/search"
headers = {"Authorization": "Bearer <token>"}
response = requests.get(url, headers=headers)
print(response.text)const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};
fetch('https://secureai.hiperai.ai/api/external/indexes/{indexName}/search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"success": true,
"request_id": "550e8400-e29b-41d4-a716-446655440000",
"query": "What is machine learning?",
"results": {
"matches": [
{
"rank": 1,
"score": 0.85,
"source": "training",
"content": "Machine learning is a subset of artificial intelligence...",
"metadata": {
"page": 1,
"chunkIndex": 0,
"title": "Introduction to ML",
"documentId": "60a7c8f5e8b4f5001f7a8c26"
}
}
],
"total": 5,
"top_k": 10
},
"index": {
"name": "my-knowledge-base",
"namespace": "user-60a7c8f5e8b4f5001f7a8c24-index-my-knowledge-base"
}
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked",
"request_id": "req-abc123"
}Index de recherche de documents
Recherchez des documents dans un index à l’aide de la recherche sémantique.Point de terminaison
GET /indexes/{indexName}/search
Description
Recherchez des documents dans un index à l’aide de la recherche sémantique. Renvoie les documents correspondants avec des scores de pertinence, triés par pertinence.Authentification
Obligatoire : clé APIAuthorization: Bearer sk-your-api-key-here
Paramètres du chemin
| Paramètre | Tapez | Obligatoire | Descriptif |
|---|---|---|---|
indexName | chaîne | Oui | Nom de l’index à rechercher |
Paramètres de requête
| Paramètre | Tapez | Obligatoire | Descriptif |
|---|---|---|---|
query | chaîne | Oui | Texte de la requête de recherche |
top_k | entier | Non | Nombre maximum de résultats à renvoyer (1-50, par défaut : 10) |
min_score | flotter | Non | Seuil de score de pertinence minimum (0,0-1,0, par défaut : 0,0) |
Exemple de demande
curl -X GET "https://{customer.name}.hiperai.ai/api/external/indexes/my-knowledge-base/search?query=What%20is%20machine%20learning?&top_k=10&min_score=0.5" \
-H "Authorization: Bearer sk-your-api-key-here"
JavaScript/Node.js
const query = encodeURIComponent('What is machine learning?');
const response = await fetch(`https://{customer.name}.hiperai.ai/api/external/indexes/my-knowledge-base/search?query=${query}&top_k=10&min_score=0.5`, {
method: 'GET',
headers: {
'Authorization': 'Bearer sk-your-api-key-here'
}
});
const data = await response.json();
console.log('Total matches:', data.results.total);
data.results.matches.forEach(match => {
console.log(`Rank ${match.rank}: ${match.content.substring(0, 100)}... (score: ${match.score})`);
});
import requests
url = "https://{customer.name}.hiperai.ai/api/external/indexes/my-knowledge-base/search"
headers = {
"Authorization": "Bearer sk-your-api-key-here"
}
params = {
"query": "What is machine learning?",
"top_k": 10,
"min_score": 0.5
}
response = requests.get(url, headers=headers, params=params)
result = response.json()
print('Total matches:', result['results']['total'])
for match in result['results']['matches']:
print(f"Rank {match['rank']}: {match['content'][:100]}... (score: {match['score']})")
Réponse
Réponse réussie (200)
{
"success": true,
"request_id": "550e8400-e29b-41d4-a716-446655440000",
"query": "What is machine learning?",
"results": {
"matches": [
{
"rank": 1,
"score": 0.85,
"source": "training",
"content": "Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed...",
"metadata": {
"page": 1,
"chunkIndex": 0,
"title": "Introduction to ML",
"documentId": "60a7c8f5e8b4f5001f7a8c26"
}
},
{
"rank": 2,
"score": 0.78,
"source": "training",
"content": "Machine learning algorithms build mathematical models based on training data to make predictions or decisions...",
"metadata": {
"page": 2,
"chunkIndex": 1,
"title": "Introduction to ML",
"documentId": "60a7c8f5e8b4f5001f7a8c26"
}
}
],
"total": 5,
"top_k": 10
},
"index": {
"name": "my-knowledge-base",
"namespace": "user-60a7c8f5e8b4f5001f7a8c24-index-my-knowledge-base"
}
}
Champs de réponse
| Champ | Tapez | Descriptif |
|---|---|---|
success | booléen | Toujours vrai pour les demandes réussies |
request_id | chaîne | Demander un identifiant pour le suivi |
query | chaîne | La requête de recherche utilisée |
results | objet | Résultats de recherche |
index | objet | Informations sur l’index |
Objet de résultats
| Champ | Tapez | Descriptif |
|---|---|---|
matches | tableau | Tableau de documents correspondants, triés par pertinence |
total | entier | Nombre total de correspondances trouvées |
top_k | entier | Valeur top_k demandée |
Objet de correspondance
| Champ | Tapez | Descriptif |
|---|---|---|
rank | entier | Classement des résultats (basé sur 1) |
score | flotter | Score de pertinence (0,0-1,0, plus élevé est plus pertinent) |
source | chaîne | Identificateur de la source du document |
content | chaîne | Aperçu du contenu (tronqué à 500 caractères) |
metadata | objet | Métadonnées supplémentaires |
Objet de métadonnées
| Champ | Tapez | Descriptif |
|---|---|---|
page | entier|null | Numéro de page (si à partir d’un PDF) |
chunkIndex | entier|null | Index de fragments dans le document |
title | chaîne|null | Titre du document |
documentId | chaîne|null | Numéro d’identification du document |
Réponses d’erreur
400 Requête incorrecte
{
"success": false,
"error": "Invalid request",
"message": "Missing or invalid query parameter"
}
401 Non autorisé
{
"success": false,
"error": "Invalid API key",
"message": "The provided API key is invalid or has been revoked"
}
403 Interdit
{
"success": false,
"error": "Access denied",
"message": "User doesn't have access to this index"
}
404 Introuvable
{
"success": false,
"error": "Index not found",
"message": "The specified index does not exist"
}
500 Erreur de serveur interne
{
"success": false,
"error": "Search failed",
"message": "An error occurred during search"
}
Remarques
- La recherche sémantique utilise la similarité vectorielle pour trouver des documents pertinents
- Les résultats sont triés par score de pertinence (le plus élevé en premier)
- Utilisez
min_scorepour filtrer les résultats peu pertinents - Les aperçus du contenu sont tronqués à 500 caractères
- Le paramètre
top_klimite le nombre de résultats renvoyés - Les métadonnées incluent des informations sur la source et l’emplacement du document
Autorisations
API key authentication using Bearer token format.
Example: Authorization: Bearer sk-your-api-key-here
Paramètres de chemin
Name of the index to search
Exemple:
"my-knowledge-base"
Paramètres de requête
Search query text
Exemple:
"What is machine learning?"
Maximum number of results to return (1-50, default: 10)
Plage requise:
1 <= x <= 50Exemple:
10
Minimum relevance score threshold (0.0-1.0, default: 0.0)
Plage requise:
0 <= x <= 1Exemple:
0.5

