Documentation Index
Fetch the complete documentation index at: https://docs.getnetra.ai/llms.txt
Use this file to discover all available pages before exploring further.
Installation
Install both the Netra SDK and Redis:
pip install netra-sdk redis
Usage
Initialize the Netra SDK to automatically trace all Redis operations:
from netra import Netra
import redis
from redis.commands.search.field import VectorField
import os
# Initialize Netra
Netra.init(
headers=f"x-api-key={os.environ.get('NETRA_API_KEY')}",
trace_content=True
)
# Create Redis client - automatically traced
client = redis.Redis.from_url(os.environ.get('REDIS_URL'))
# Create index for vector search
client.ft("idx:vectors").create_index([
VectorField("vector",
"HNSW", {
"TYPE": "FLOAT32",
"DIM": 384,
"DISTANCE_METRIC": "COSINE"
}
)
])
Index Operations
Trace index creation and management:
from netra.decorators import task
from netra import SpanWrapper
from redis.commands.search.field import VectorField
@task()
def create_vector_index(client, index_name: str, dimension: int):
span = SpanWrapper("redis-create-index", {
"index.name": index_name,
"vector.dimension": dimension
}).start()
client.ft(index_name).create_index([
VectorField("vector", "HNSW", {
"TYPE": "FLOAT32",
"DIM": dimension,
"DISTANCE_METRIC": "COSINE"
})
])
span.end()
Vector Storage
Trace vector insertions:
from netra.decorators import task
from netra import SpanWrapper, ActionModel
import numpy as np
@task()
def store_vector(client, key: str, vector: list[float], metadata: dict):
span = SpanWrapper("redis-store-vector", {
"key": key,
"vector.size": len(vector)
}).start()
client.hset(key, mapping={
"vector": np.array(vector, dtype=np.float32).tobytes(),
**metadata
})
span.set_action([ActionModel(
action="set",
action_type="database.set",
success=True,
affected_records=[{"id": key}],
metadata={"vector_size": len(vector)}
)])
span.set_attribute("status", "success")
span.end()
Vector Search
Trace similarity searches:
from netra.decorators import workflow
from netra import SpanWrapper
import numpy as np
@workflow()
def search_vectors(client, index_name: str, query: list[float], limit: int = 5):
span = SpanWrapper("redis-search", {
"index": index_name,
"query.size": len(query),
"limit": limit
}).start()
query_vec = np.array(query, dtype=np.float32).tobytes()
results = client.ft(index_name).search(
f"*=>[KNN {limit} @vector $query_vec]",
query_params={"query_vec": query_vec}
)
span.set_attribute("results.count", results.total)
span.end()
return results
Hybrid Search
Trace combined vector and metadata searches:
from netra.decorators import task
from netra import SpanWrapper
import numpy as np
@task()
def hybrid_search(client, index_name: str, query: list[float], filter: str):
span = SpanWrapper("redis-hybrid-search", {
"index": index_name,
"filter": filter
}).start()
query_vec = np.array(query, dtype=np.float32).tobytes()
results = client.ft(index_name).search(
f"{filter}=>[KNN 10 @vector $query_vec]",
query_params={"query_vec": query_vec}
)
span.set_attribute("results.count", results.total)
span.end()
return results
Next Steps
