Search vector store
vector_stores.search(strvector_store_id, VectorStoreSearchParams**kwargs) -> SyncPage[VectorStoreSearchResponse]
POST/vector_stores/{vector_store_id}/search
Search a vector store for relevant chunks based on a query and file attributes filter.
Parameters
vector_store_id: str
max_num_results: Optional[int]
The maximum number of results to return. This number should be between 1 and 50 inclusive.
minimum1
maximum50
Whether to rewrite the natural language query for vector search.
Returns
Search vector store
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("OPENAI_API_KEY"), # This is the default and can be omitted
)
page = client.vector_stores.search(
vector_store_id="vs_abc123",
query="string",
)
page = page.data[0]
print(page.file_id){
"object": "vector_store.search_results.page",
"search_query": "What is the return policy?",
"data": [
{
"file_id": "file_123",
"filename": "document.pdf",
"score": 0.95,
"attributes": {
"author": "John Doe",
"date": "2023-01-01"
},
"content": [
{
"type": "text",
"text": "Relevant chunk"
}
]
},
{
"file_id": "file_456",
"filename": "notes.txt",
"score": 0.89,
"attributes": {
"author": "Jane Smith",
"date": "2023-01-02"
},
"content": [
{
"type": "text",
"text": "Sample text content from the vector store."
}
]
}
],
"has_more": false,
"next_page": null
}
Returns Examples
{
"object": "vector_store.search_results.page",
"search_query": "What is the return policy?",
"data": [
{
"file_id": "file_123",
"filename": "document.pdf",
"score": 0.95,
"attributes": {
"author": "John Doe",
"date": "2023-01-01"
},
"content": [
{
"type": "text",
"text": "Relevant chunk"
}
]
},
{
"file_id": "file_456",
"filename": "notes.txt",
"score": 0.89,
"attributes": {
"author": "Jane Smith",
"date": "2023-01-02"
},
"content": [
{
"type": "text",
"text": "Sample text content from the vector store."
}
]
}
],
"has_more": false,
"next_page": null
}