| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - clickhouse |
| - rag |
| - embeddings |
| - documentation |
| pretty_name: ClickHouse Documentation Embeddings |
| task_categories: |
| - feature-extraction |
| --- |
| |
| # ClickHouse Documentation Embeddings |
|
|
| Vector dataset used as the knowledge base for the ClickHouse RAG API. Contains chunked ClickHouse documentation with precomputed embeddings. |
|
|
| **Not intended for fine-tuning the embedding model.** |
|
|
| ## Purpose |
|
|
| This dataset backs the retrieval step of the ClickHouse RAG API. At query time, the API embeds an incoming question using the same model and performs |
| cosine distance search against the `embedding` column in ClickHouse to retrieve relevant documentation chunks. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `source_link` | `string` | URL of the source documentation page | |
| | `title` | `string` | Title of the documentation page | |
| | `chunk_index` | `int32` | Position of this chunk within the source page (0-based) | |
| | `chunk_text` | `string` | Text content of the chunk | |
| | `embedding` | `Sequence[float32]` | 4096-dimensional embedding vector | |
|
|
| ## Embedding Model |
|
|
| Embeddings were produced by `qwen3_embedding_8b` served via [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference). |
|
|
| - **Dimensions:** 4096 |
| - **Dtype:** float32 |
|
|
| To query this dataset, embed your query using the same model and configuration. Using a different model will produce incompatible vectors and degrade |
| retrieval quality. |
|
|
| ## Source |
|
|
| Sourced from the [ClickHouse documentation](https://clickhouse.com/docs). |
| |
