Changelog

v0.1.12

The 0.1.12 release brings Pinecone v7 support and AWS Bedrock boto3 client integration, improving compatibility and expanding cloud deployment options.

Feature: Pinecone v7 Upgrade

Semantic Router now supports Pinecone Python SDK v7, providing:

Improved data plane performance and reliability
Enhanced async support for high-throughput applications
Simplified API structure for better developer experience
Better error handling and retry logic

The upgrade is backward compatible - existing code continues to work without changes. For shared index deployments, set PINECONE_INDEX_NAME to reuse existing indexes and avoid quota limits. Semantic Router automatically isolates data using namespaces.

Feature: Bedrock boto3 Client Support

Added support for custom boto3 clients in the BedrockEncoder, enabling:

Advanced AWS credential management (IAM roles, cross-account access)
Custom retry policies and timeout configurations
VPC endpoint support for private deployments
Better integration with existing AWS infrastructure

This provides greater flexibility for enterprise AWS deployments while maintaining backward compatibility with the existing credential-based approach.

Chore: Relaxed OpenAI SDK Dependency

Updated OpenAI SDK dependency constraints from <2.0.0 to <3.0.0, allowing compatibility with OpenAI SDK v2.x releases. This provides access to newer OpenAI features and improvements while maintaining stability through the v2 major version.

v0.1.11

The 0.1.11 release introduces new encoder options for local and self-hosted deployments, alongside sparse encoding improvements and CI/CD enhancements.

Feature: Local Encoder

Added LocalEncoder for fully local embedding generation using sentence-transformers:

No API keys or internet connection required
Automatic device selection (CUDA, MPS, CPU)
Support for any sentence-transformers model
Privacy-first design - all data stays on your machine

Perfect for offline deployments, privacy-sensitive applications, or development environments.

Feature: Local Sparse Encoder

Added sparse encoder support using sentence transformers with LocalSparseEncoder.

Same offline advantages as the Local Encoder above.
Better handling of sentence boundaries
Improved BM25 and TF-IDF implementations
Enhanced compatibility with HybridRouter

Feature: Ollama Encoder

Introduced OllamaEncoder for using Ollama-hosted embedding models:

Works with any Ollama embedding model (nomic-embed-text, mxbai-embed-large, etc.)
Full control over model versions and hosting
Low latency with local Ollama instances
Both sync and async support

Ideal for organizations wanting to self-host embedding models while maintaining API-like convenience.

Feature: Dagger CI

Implemented Dagger for CI/CD pipeline, providing:

Faster, more reliable builds
Better caching and parallelization
Consistent local and remote execution
Improved developer experience

Chore: Torch Dependency Optimization

Removed torch from main dependencies to reduce installation size. PyTorch is now only installed when needed via semantic-router[local] extras, reducing default installation from ~2GB to <100MB.

v0.1.10

The 0.1.10 release was primarily focused on expanding async support for QdrantIndex, PostgresIndex, and HybridRouter, alongside many synchronization and testing improvements.

Feature: Expanded Async Support

QdrantIndex: Async methods have been brought inline with our other indexes, ensuring consistent behavior.
PostgresIndex: Async methods have been added to the PostgresIndex for improved performance in async environments.
HybridRouter: Async support for the HybridRouter is now aligned with the SemanticRouter, providing a more consistent experience.

Fixes and Optimizations

LocalIndex Bug Fix: Added a metadata attribute to the local index. This fixes a bug where LocalIndex embeddings would always be recomputed, as reported in issue #585.
Various other bug fixes and optimizations have been included in this release.
The urllib3 library has been upgraded.
Test compatibility and synchronization have been optimized.

v0.1.9

The 0.1.9 update focuses on improving support for our local deployment options. We have standardized the PostgresIndex to bring it in line with other index options and prepare it for future feature releases. For local extras (inclusing transformers and llama_cpp support) and postgres extras we have resolved issues making those extras unusable with Python 3.13.

Continue reading below for more detail.

Feature: Improvements to `postgres` Support

Upgrade from psycopg2 to v3 (psycopg): We've upgraded our PostgreSQL driver from psycopg2 to the newer psycopg v3, which provides better performance and modern Python compatibility.
Standardization of sync methods: The PostgresIndex class now has standardized synchronous methods that integrate seamlessly into our standard pytest test suite, ensuring better testing coverage and reliability.
Addition of IndexType support: We've introduced IndexType which includes FLAT, IVFFLAT, and HNSW index types. Both FLAT and IVFFLAT map to the IVFFLAT index in pgvector (pgvector does not currently support FLAT and so IVFFLAT is the closest approximation - but our other indexes do). FLAT is now the default index type. We recommend HNSW for high-scale use cases with many millions of utterances due to its higher memory requirements and complexity.

Feature: Configurable Logging

Environment variable log level control: Logger now supports configurable log levels via environment variables. Set SEMANTIC_ROUTER_LOG_LEVEL or LOG_LEVEL to control logging verbosity (e.g., DEBUG, INFO, WARNING, ERROR). Defaults to INFO if not specified.

Fix: Local Installation for Python 3.13 and up

Python 3.13 and up had originally been incompatible with our local installations due to the lack of compatability with PyTorch and the (then) new version of Python. We have now added the following version support for local libraries:

local = [
    "torch>=2.6.0 ; python_version >= '3.13'",
    "transformers>=4.50.0 ; python_version >= '3.13'",
    "tokenizer>=0.20.2 ; python_version >= '3.13'",
    "llama-cpp-python>=0.3.0 ; python_version >= '3.13'",
]

Fix: Consistent Index Length Behavior

Standardized __len__ method across all index implementations: All index types now consistently return 0 when uninitialized, preventing potential AttributeError exceptions that could occur in PineconeIndex and QdrantIndex when checking the length of uninitialized indexes.

Chore: Broader and More Useful Tests

We have broken our tests apart into strict unit and integration test directories. Now, when incoming PRs are raised we will no longer trigger integration tests that require API keys to successfully run. To ensure we're still covering all components of the library we have broadened our testing suite to extensively test LocalIndex, PineconeIndex (via Pinecone Local), PostgresIndex, and QdrantIndex within those unit tests.

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