LanceDB
AI & LLMsDeveloper-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Release History
View all versions →v0.38.0-beta.14Breaking1 fix1 featureThis release introduces support for GPU resource requirements in functions and fixes a bug related to multiple function bindings per table.
v0.38.0-beta.13Breaking2 featuresThis release updates the Node.js requirement to version 22 and introduces support for blob computed column refresh and nested Function Arrow types in Python. It also drops support for npm lockfiles.
v0.38.0-beta.12Breaking4 fixes2 featuresThis release introduces new features for Python, including accepting expressions in update filters and allowing computed columns to read their own declarations. It also includes several bug fixes across Python and Node.js, and a breaking change in how table listings are paginated.
v0.38.0-beta.11Breaking7 fixes3 featuresThis release introduces new features like blob URI writes and improved FTS granularity, alongside several bug fixes for Python, Node.js, and remote operations. Breaking changes include modifications to embedding registration and auto-search routing in Node.js.
v0.38.0-beta.101 fixThis release fixes a bug related to the unsupported Function secrets contract.
v0.38.0-beta.9Refactored function binding identity for simplification.
v0.38.0-beta.8Breaking3 fixes2 featuresThis release introduces a new `listTables` method for the Node.js client and improves data loader stability by pinning base table versions. It also includes a breaking change related to @udf body packaging.
v0.38.0-beta.73 fixes3 featuresThis release introduces new features for streaming datasets and materialized views, along with several bug fixes to improve table and remote operations.
v0.38.0-beta.6v0.38.0-beta.51 featureThis release introduces backpressure to the StreamingDataset post-transform queue, improving data handling efficiency.
v0.38.0-beta.4Breaking8 fixes6 featuresThis release introduces materialized views, supports remote tables in the data loader, and includes various bug fixes and refactors across Python, Node.js, and Rust. Key operations like branch merging have been renamed for clarity.
v0.38.0-beta.3Breaking2 fixes4 featuresThis release brings MemWAL LSM surface parity across SDKs and introduces first-class function wire contracts. It also includes several bug fixes and refactors, with a breaking change requiring pydantic v2 for the Python package.
v0.38.0-beta.22 fixes1 featureThis release introduces new features for RemoteTable synchronization and fixes critical bugs in datetime handling and hybrid search.
v0.38.0-beta.15 featuresThis release introduces several new features related to computed columns, including declaration via SQL expressions, refreshing capabilities, and support for remote tables. It also enhances index catch-up mechanisms and the refresh_column_async function.
v0.38.0-beta.0Breaking3 fixes2 featuresThis release introduces fault tolerance for StreamingDataset and an asynchronous drop table API. It also includes a breaking change where table existence checks are now manifest-authoritative.
v0.37.1Breaking35 fixes13 featuresThis release introduces several new features including FTS enhancements, batched blob reads, and connection-level job operations. It also includes numerous bug fixes across Python, Rust, and Node.js, along with breaking changes to the `add_columns` method in both Rust and Python.
v0.37.1-beta.22 fixesThis release includes bug fixes for retrieving table size from metadata and preserving nested Arrow data across versions in the node package.
v0.37.1-beta.1Breaking29 fixes8 featuresThis release introduces several new features including custom stop-word lists for FTS, a RemoteTable fetch_blobs client, and connection-level job operations. It also includes numerous bug fixes across Python, Rust, and Node.js, along with breaking changes to the `add_columns` function in Rust and Python.
v0.37.1-beta.04 fixes5 featuresThis release introduces new features for full-text search, query optimization, and batched reads, alongside several bug fixes for rerankers, metadata handling, and versioning.
python-v0.36.0Breaking15 fixes14 featuresThis release introduces an elastic dataloader, support for various data types in the expression builder, and enhanced object store capabilities. It also includes numerous bug fixes and performance improvements across Python, Rust, and Node.js clients.
v0.33.0Breaking15 fixes14 featuresThis release introduces an elastic dataloader, support for various data types in the expression builder, and enhanced object store capabilities. It also includes numerous bug fixes and performance improvements, alongside updates to Python and Node.js integrations.
v0.32.0-beta.31 fix1 featureThis release introduces a new block size configuration for Full-Text Search and fixes a crash in specific reranker configurations.
python-v0.35.0-beta.31 fix1 featureThis release introduces a new block size configuration for Full-Text Search (FTS) and resolves a crash issue in AnswerdotaiRerankers/ColbertReranker.
python-v0.36.0-beta.06 fixes5 featuresThis release introduces new features for distributed analytics, client APIs for branch management, and improved skill integration. It also includes several bug fixes for ingestion, data handling, and client operations.
v0.33.0-beta.06 fixes5 featuresThis release introduces new features for distributed analytics, client APIs for branch management, and improved handling of skill references. It also includes various bug fixes across Python, Rust, and Node.js components.
Common Errors
ModuleNotFoundError5 reportsModuleNotFoundError typically arises when a required Python package is not installed in your environment, or when the PYTHONPATH is not correctly set. To fix this, first ensure the missing module (e.g., lance, numpy) is installed using `pip install <module_name>`. If the error persists, verify your Python environment and PYTHONPATH are configured correctly to include the installation path of the missing module.
NotImplementedError3 reportsThe "NotImplementedError" in LanceDB usually arises when attempting to use a feature that is either unavailable in the specific LanceDB deployment (cloud vs. local) or has not yet been implemented in the Python client. To resolve this, confirm the desired feature (e.g., namespaces, certain index types, full-text search) is supported in your LanceDB environment; if not, consider using a supported alternative or check for updates to the LanceDB client library if it's a missing feature in the client. For cloud versions, check the available features supported.
MaxRetryError1 reportMaxRetryError in LanceDB usually arises from network connectivity problems or LanceDB server unavailability, particularly during operations like vector index creation or querying remote tables. Resolve this by ensuring a stable network connection and verifying the LanceDB server is running and accessible. If using cloud providers, check their status and consider implementing retry logic with exponential backoff to gracefully handle temporary failures.
UnboundLocalError1 reportThe UnboundLocalError arises when a variable is referenced before it's assigned within its scope, often inside conditional blocks. To fix this, ensure the variable is always initialized with a default value before the conditional logic is executed; this guarantees it exists regardless of the conditional outcome. For example, initialize `results = None` before the `if` block that might assign actual results.
UnsupportedOperationException1 reportUnsupportedOperationException in LanceDB often arises from trying to perform operations, particularly with UDFs or complex data types like arrays within structs, that are not yet fully implemented or optimized in the underlying data processing engine (Arrow or DataFusion). The fix usually involves either simplifying the query to use supported operations, pre-computing or restructuring the data to avoid the unsupported feature, or updating LanceDB to a version with the necessary functionality implemented, assuming it's a known and resolved issue. If the operation is essential and unfixed, consider filing a bug report detailing your query and data structure for the LanceDB team to address.
ValidationError1 reportValidationError in lancedb often arises from mismatches between the schema defined in your LanceModel (or equivalent data structures) and the actual data being written or queried. Ensure that your data types, field names, and any aliases specified in the LanceModel precisely match the data's structure; double-check for inconsistencies like incorrect data types, misspelled field names, or missing fields. Validate your LanceModel's schema against the expected data format using tools like pydantic's validation features or by inspecting the data before writing to lancedb.
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