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Merge pull request #320 from langflow-ai/docs-perf-test

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docs: performance expectations
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Mendon Kissling 2025-10-28 14:09:23 -04:00 • committed by GitHub
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@ -83,4 +83,43 @@ The **OpenRAG Backend** is the central orchestration service that coordinates al
**Third Party Services** like **Google Drive** connect to the **OpenRAG Backend** through OAuth authentication, allowing synchronication of cloud storage with the OpenSearch knowledge base.
The **OpenRAG Frontend** provides the user interface for interacting with the system.
The **OpenRAG Frontend** provides the user interface for interacting with the system.
## Performance expectations
On a local VM with 7 vCPUs and 8 GiB RAM, OpenRAG ingested approximately 5.03 GB across 1,083 files in about 42 minutes.
This equates to approximately 2.4 documents per second.
You can generally expect equal or better performance on developer laptops and significantly faster on servers.
Throughput scales with CPU cores, memory, storage speed, and configuration choices such as embedding model, chunk size and overlap, and concurrency.
This test returned 12 errors (approximately 1.1%).
All errors were file‑specific, and they didn't stop the pipeline.
Ingestion dataset:
* Total files: 1,083 items mounted
* Total size on disk: 5,026,474,862 bytes (approximately 5.03 GB)
Hardware specifications:
* Machine: Apple M4 Pro
* Podman VM:
* Name: `podman-machine-default`
* Type: `applehv`
* vCPUs: 7
* Memory: 8 GiB
* Disk size: 100 GiB
Test results:
```text
2025-09-24T22:40:45.542190Z /app/src/main.py:231 Ingesting default documents when ready disable_langflow_ingest=False
2025-09-24T22:40:45.546385Z /app/src/main.py:270 Using Langflow ingestion pipeline for default documents file_count=1082
...
2025-09-24T23:19:44.866365Z /app/src/main.py:351 Langflow ingestion completed success_count=1070 error_count=12 total_files=1082
```
Elapsed time: ~42 minutes 15 seconds (2,535 seconds)
Throughput: ~2.4 documents/second