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Diffstat (limited to '.rules/docs/ollama/embed.md')
| -rw-r--r-- | .rules/docs/ollama/embed.md | 16 |
1 files changed, 15 insertions, 1 deletions
diff --git a/.rules/docs/ollama/embed.md b/.rules/docs/ollama/embed.md index 9c81ebf..53d46df 100644 --- a/.rules/docs/ollama/embed.md +++ b/.rules/docs/ollama/embed.md @@ -1,9 +1,17 @@ -# Generate embeddings +# Generate Embeddings `POST /api/embed` — Creates vector embeddings representing the input text. **Server:** `http://localhost:11434` +Embeddings turn text into numeric vectors for semantic search, retrieval, and RAG pipelines. Vector length depends on the model (typically 384–1024 dimensions). Vectors are **L2-normalized** (unit-length). + +## Recommended Models + +- `embeddinggemma` +- `qwen3-embedding` +- `all-minilm` + ## Request | Field | Type | Required | Description | @@ -54,3 +62,9 @@ curl http://localhost:11434/api/embed -d '{ "dimensions": 128 }' ``` + +## Tips + +- Use **cosine similarity** for most semantic search use cases. +- Use the **same embedding model** for both indexing and querying. +- Batch multiple strings in one request for efficiency. |