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Allow markdownlint to run locally (#36398)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
2026-03-09 03:05:24 +00:00
committed by GitHub
parent fde4771bbd
commit a0f44bb616
47 changed files with 394 additions and 392 deletions
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8
examples/pooling/embed/openai_embedding_long_text/README.md
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@@ -34,7 +34,7 @@ python client.py
## 📁 Files
| File | Description |
|------|-------------|
| ---- | ----------- |
| `service.sh` | Server startup script with chunked processing enabled |
| `client.py` | Comprehensive test client for long text embedding |
@@ -61,7 +61,7 @@ The key parameters for chunked processing are in the `--pooler-config`:
Chunked processing uses **MEAN aggregation** for cross-chunk combination when input exceeds the model's native maximum length:
| Component | Behavior | Description |
|-----------|----------|-------------|
| --------- | -------- | ----------- |
| **Within chunks** | Model's native pooling | Uses the model's configured pooling strategy |
| **Cross-chunk aggregation** | Always MEAN | Weighted averaging based on chunk token counts |
| **Performance** | Optimal | All chunks processed for complete semantic coverage |
@@ -69,7 +69,7 @@ Chunked processing uses **MEAN aggregation** for cross-chunk combination when in
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| -------- | ------- | ----------- |
| `MODEL_NAME` | `intfloat/multilingual-e5-large` | Embedding model to use (supports multiple models) |
| `PORT` | `31090` | Server port |
| `GPU_COUNT` | `1` | Number of GPUs to use |
@@ -106,7 +106,7 @@ With `MAX_EMBED_LEN=3072000`, you can process:
### Chunked Processing Performance
| Aspect | Behavior | Performance |
|--------|----------|-------------|
| ------ | -------- | ----------- |
| **Chunk Processing** | All chunks processed with native pooling | Consistent with input length |
| **Cross-chunk Aggregation** | MEAN weighted averaging | Minimal overhead |
| **Memory Usage** | Proportional to number of chunks | Moderate, scalable |