VectorConfig
Configuration for a vector generation method. This model defines how vectors should be generated for a specific field or set of fields. Different vector types have different requirements and behaviors.
Properties
| Name | Type | Description | Notes |
|---|---|---|---|
| name | str | Unique name for this vector configuration | |
| type | str | Type of vector (dense_model, sparse_model, full_text, trigrams, whitespace, wmtr, dense_custom, sparse_custom) | |
| model | str | Model name for transformer-based vectors (max 210 characters, e.g., 'sentence-transformers/all-MiniLM-L6-v2'). Used for document indexing. | [optional] |
| revision | str | Optional model revision (max 210 characters, branch/tag/commit) for specific model version. Used for document indexing. | [optional] |
| query_model | str | Optional model name for query vectorization (max 210 characters). If not specified, uses 'model' for both documents and queries. | [optional] |
| query_revision | str | Optional model revision for query vectorization (max 210 characters). If not specified, uses 'revision' for both documents and queries. | [optional] |
| dimensions | int | Dimensions for the vector. Required for dense_custom vectors. For dense_model vectors, auto-detected if not specified. | [optional] |
| top_k | int | Number of top-scoring terms to keep for sparse vectors. Used by sparse_model, full_text, trigrams, whitespace, wmtr, and sparse_custom vectors. Ignored by dense vectors. | [optional] [default to 128] |
| wmtr_word_weight | int | Percentage of WMTR top_k allocated to word weights. | [optional] [default to 80] |
| index_fields | List[str] | List of fields to index with this vector (name, description, content). Defaults to ['content'] if not specified. | [optional] |
| language_default_code | str | Two-letter ISO 639-1 language code for language-based vector types (e.g., 'en', 'es', 'fr') | [optional] [default to 'en'] |
| language_detect | bool | Whether to automatically detect language for language-based vector types | [optional] [default to False] |
| language_confidence | float | Minimum confidence threshold for language detection. If detection confidence is below this value, language_default_code will be used instead. | [optional] [default to 0.9] |
| normalization | bool | Whether to normalize vectors. Only supported for dense vectors. Sparse vectors do not support normalization. | [optional] |
| dense_distance | str | Distance metric for dense vectors (cosine, dot, euclid). Defaults to cosine. | [optional] [default to 'cosine'] |
| keep_case | bool | Whether to keep original case for text preprocessing. Only applies to model-based vectors (dense_model, sparse_model). Defaults to False (lowercase). | [optional] [default to False] |
Example
from amgix_client.models.vector_config import VectorConfig
# TODO update the JSON string below
json = "{}"
# create an instance of VectorConfig from a JSON string
vector_config_instance = VectorConfig.from_json(json)
# print the JSON string representation of the object
print(VectorConfig.to_json())
# convert the object into a dict
vector_config_dict = vector_config_instance.to_dict()
# create an instance of VectorConfig from a dict
vector_config_from_dict = VectorConfig.from_dict(vector_config_dict)