Skip to content

docs: refactor vector query example for multiple queries #2573

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
watersRand wants to merge 1 commit into
base: main
Choose a base branch
from
Open

docs: refactor vector query example for multiple queries #2573

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 10 additions & 6 deletions content/develop/get-started/vector-database.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,12 +244,16 @@ To utilize a vector query with the [`FT.SEARCH`]({{< relref "commands/ft.search/
You must pass the vectorized query as a byte array with the param name `query_vector`. The following code creates a Python NumPy array from the query vector and converts it into a compact, byte-level representation that can be passed as a parameter to the query:

```python
client.ft('idx:bikes_vss').search(
query,
{
'query_vector': np.array(encoded_query, dtype=np.float32).tobytes()
}
).docs
for i, encoded_query in enumerate(encoded_queries):
result = client.ft('idx:bikes_vss').search(
query,
{
'query_vector': np.array(encoded_query, dtype=np.float32).tobytes()
}
)
print(f"Results for query '{queries[i]}':")
for doc in result.docs:
print(f"\t{doc.id}: {doc.brand} {doc.model}")
```

With the template for the query in place, you can execute all queries in a loop. Notice that the script calculates the `vector_score` for each result as `1 - doc.vector_score`. Because the cosine distance is used as the metric, the items with the smallest distance are closer and, therefore, more similar to the query.
Expand Down