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This library uses a universal format for vector datasets to easily export and import data from all vector databases.
Request support for a VectorDB by voting/commenting on this poll
See the Contributing section to add support for your favorite vector database.
Fully Supported
| Vector Database | Import | Export |
|---|---|---|
| Pinecone | ✅ | ✅ |
| Qdrant | ✅ | ✅ |
| Milvus | ✅ | ✅ |
| GCP Vertex AI Vector Search | ✅ | ✅ |
| KDB.AI | ✅ | ✅ |
| LanceDB | ✅ | ✅ |
| DataStax Astra DB | ✅ | ✅ |
| Chroma | ✅ | ✅ |
| Turbopuffer | ✅ | ✅ |
Partial
| Vector Database | Import | Export |
|---|
In Progress
| Vector Database | Import | Export |
|---|---|---|
| Azure AI Search | ❌ | ❌ |
| Weaviate | ❌ | ❌ |
| MongoDB Atlas | ❌ | ❌ |
| OpenSearch | ❌ | ❌ |
| Apache Cassandra | ❌ | ❌ |
| txtai | ❌ | ❌ |
| pgvector | ❌ | ❌ |
| SQLite-VSS | ❌ | ❌ |
Not Supported
| Vector Database | Import | Export |
|---|---|---|
| Vespa | ❌ | ❌ |
| Marqo | ❌ | ❌ |
| Elasticsearch | ❌ | ❌ |
| Redis Search | ❌ | ❌ |
| ClickHouse | ❌ | ❌ |
| USearch | ❌ | ❌ |
| Rockset | ❌ | ❌ |
| Epsilla | ❌ | ❌ |
| Activeloop Deep Lake | ❌ | ❌ |
| ApertureDB | ❌ | ❌ |
| CrateDB | ❌ | ❌ |
| Meilisearch | ❌ | ❌ |
| MyScale | ❌ | ❌ |
| Neo4j | ❌ | ❌ |
| Nuclia DB | ❌ | ❌ |
| OramaSearch | ❌ | ❌ |
| Typesense | ❌ | ❌ |
| Anari AI | ❌ | ❌ |
| Vald | ❌ | ❌ |
| Apache Solr | ❌ | ❌ |
pip install vdf-io
git clone https://github.com/AI-Northstar-Tech/vector-io.git
cd vector-io
pip install -r requirements.txt- VDF_META.json: It is a json file with the following schema VDFMeta defined in src/vdf_io/meta_types.py:
class NamespaceMeta(BaseModel): namespace: str index_name: str total_vector_count: int exported_vector_count: int dimensions: int model_name: str | None = None vector_columns: List[str] = ["vector"] data_path: str metric: str | None = None index_config: Optional[Dict[Any, Any]] = None schema_dict: Optional[Dict[str, Any]] = None class VDFMeta(BaseModel): version: str file_structure: List[str] author: str exported_from: str indexes: Dict[str, List[NamespaceMeta]] exported_at: str id_column: Optional[str] = None
- Parquet files/folders for metadata and vectors.
export_vdf --help usage: export_vdf [-h] [-m MODEL_NAME] [--max_file_size MAX_FILE_SIZE] [--push_to_hub | --no-push_to_hub] [--public | --no-public] {pinecone,qdrant,kdbai,milvus,vertexai_vectorsearch} ... Export data from various vector databases to the VDF format for vector datasets options: -h, --help show this help message and exit -m MODEL_NAME, --model_name MODEL_NAME Name of model used --max_file_size MAX_FILE_SIZE Maximum file size in MB (default: 1024) --push_to_hub, --no-push_to_hub Push to hub --public, --no-public Make dataset public (default: False) Vector Databases: Choose the vectors database to export data from {pinecone,qdrant,kdbai,milvus,vertexai_vectorsearch} pinecone Export data from Pinecone qdrant Export data from Qdrant kdbai Export data from KDB.AI milvus Export data from Milvus vertexai_vectorsearch Export data from Vertex AI Vector Search
import_vdf --help usage: import_vdf [-h] [-d DIR] [-s | --subset | --no-subset] [--create_new | --no-create_new] {milvus,pinecone,qdrant,vertexai_vectorsearch,kdbai} ... Import data from VDF to a vector database options: -h, --help show this help message and exit -d DIR, --dir DIR Directory to import -s, --subset, --no-subset Import a subset of data (default: False) --create_new, --no-create_new Create a new index (default: False) Vector Databases: Choose the vectors database to export data from {milvus,pinecone,qdrant,vertexai_vectorsearch,kdbai} milvus Import data to Milvus pinecone Import data to Pinecone qdrant Import data to Qdrant vertexai_vectorsearch Import data to Vertex AI Vector Search kdbai Import data to KDB.AI
This Python script is used to re-embed a vector dataset. It takes a directory of vector dataset in the VDF format and re-embeds it using a new model. The script also allows you to specify the name of the column containing text to be embedded.
reembed_vdf --help usage: reembed_vdf [-h] -d DIR [-m NEW_MODEL_NAME] [-t TEXT_COLUMN] Reembed a vector dataset options: -h, --help show this help message and exit -d DIR, --dir DIR Directory of vector dataset in the VDF format -m NEW_MODEL_NAME, --new_model_name NEW_MODEL_NAME Name of new model to be used -t TEXT_COLUMN, --text_column TEXT_COLUMN Name of the column containing text to be embedded
export_vdf -m hkunlp/instructor-xl --push_to_hub pinecone --environment gcp-starter import_vdf -d /path/to/vdf/dataset milvus reembed_vdf -d /path/to/vdf/dataset -m sentence-transformers/all-MiniLM-L6-v2 -t title
Follow the prompt to select the index and id range to export.
If you wish to add an import/export implementation for a new vector database, you must also implement the other side of the import/export for the same database. Please fork the repo and send a PR for both the import and export scripts.
Steps to add a new vector database (ABC):
- Add your database name in src/vdf_io/names.py in the DBNames enum class.
- Create new files
src/vdf_io/export_vdf/export_abc.pyandsrc/vdf_io/import_vdf/import_abc.pyfor the new DB.
Export:
- In your export file, define a class ExportABC which inherits from ExportVDF.
- Specify a DB_NAME_SLUG for the class
- The class should implement:
- make_parser() function to add database specific arguments to the export_vdf CLI
- export_vdb() function to prompt user for info not provided in the CLI. It should then call the get_data() function.
- get_data() function to download points (in a batched manner) with all the metadata from the specified index of the vector database. This data should be stored in a series of parquet files/folders. The metadata should be stored in a json file with the schema above.
- Use the script to export data from an example index of the vector database and verify that the data is exported correctly.
Import:
- In your import file, define a class ImportABC which inherits from ImportVDF.
- Specify a DB_NAME_SLUG for the class
- The class should implement:
- make_parser() function to add database specific arguments to the import_vdf CLI, such as the url of the database, any authentication tokens, etc.
- import_vdb() function to prompt user for info not provided in the CLI. It should then call the upsert_data() function.
- upsert_data() function to upload points from a vdf dataset (in a batched manner) with all the metadata to the specified index of the vector database. All metadata about the dataset should be read from the VDF_META.json file in the vdf folder.
- Use the script to import data from the example vdf dataset exported in the previous step and verify that the data is imported correctly.
If you wish to change the VDF specification, please open an issue to discuss the change before sending a PR.
If you wish to improve the efficiency of the import/export scripts, please fork the repo and send a PR.
Running the scripts in the repo will send anonymous usage data to AI Northstar Tech to help improve the library.
You can opt out this by setting the environment variable DISABLE_TELEMETRY_VECTORIO to 1.
If you have any questions, please open an issue on the repo or message Dhruv Anand on LinkedIn