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Aug 30, 2025
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chore: Clean up write_turbopuffer guide #5093

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2 changes: 1 addition & 1 deletion docs/connectors/turbopuffer.md
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Expand Up @@ -30,7 +30,7 @@ This example does the following:
model = "BAAI/bge-base-en-v1.5"

turbopuffer_config = {
"namespace": "daft-tpuf-example2",
"namespace": "daft-tpuf-example",
"region": "gcp-us-central1",
"distance_metric": "cosine_distance",
"schema": {
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2 changes: 1 addition & 1 deletion docs/examples/text-embeddings.md
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Expand Up @@ -232,7 +232,7 @@ Now we'll execute the complete data processing pipeline:
5. **Generate embeddings**: Apply embedding UDF to text
6. **Create IDs**: Generate unique IDs combining URL and chunk_id
7. **Select columns**: Keep only the necessary columns
8. **Write to Turbopuffer**: Store data and vectors in Turbopuffer
8. **Write to Turbopuffer**: Store data and vectors in Turbopuffer using Daft's [`DataFrame.write_turbopuffer`](../api/dataframe#daft.DataFrame.write_turbopuffer) method

If all works out well, when you run this script on your cluster, you should notice that network I/O, CPU work, and GPU work are pipelined to run in parallel, and you should see high GPU utilization :)

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