TxtAI MCP Server

# ANN Approximate Nearest Neighbor (ANN) index configuration for storing vector embeddings. ## backend ```yaml backend: faiss|hnsw|annoy|ggml|numpy|torch|pgvector|sqlite|custom ``` Sets the ANN backend. Defaults to `faiss`. Additional backends are available via the [ann](../../../install/#ann) extras package. Set custom backends via setting this parameter to the fully resolvable class string. Backend-specific settings are set with a corresponding configuration object having the same name as the backend (i.e. annoy, faiss, or hnsw). These are optional and set to defaults if omitted. ### faiss ```yaml faiss: components: comma separated list of components - defaults to "IDMap,Flat" for small indices and "IVFx,Flat" for larger indexes where x = min(4 * sqrt(embeddings count), embeddings count / 39) automatically calculates number of IVF cells when omitted (supports "IVF,Flat") nprobe: search probe setting (int) - defaults to x/16 (as defined above) for larger indexes nflip: same as nprobe - only used with binary hash indexes quantize: store vectors with x-bit precision vs 32-bit (boolean|int) true sets 8-bit precision, false disables, int sets specified precision mmap: load as on-disk index (boolean) - trade query response time for a smaller RAM footprint, defaults to false sample: percent of data to use for model training (0.0 - 1.0) reduces indexing time for larger (>1M+ row) indexes, defaults to 1.0 ``` Faiss supports both floating point and binary indexes. Floating point indexes are the default. Binary indexes are used when indexing scalar-quantized datasets. See the following Faiss documentation links for more information. - [Guidelines for choosing an index](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index) - [Index configuration summary](https://github.com/facebookresearch/faiss/wiki/Faiss-indexes) - [Index Factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory) - [Binary Indexes](https://github.com/facebookresearch/faiss/wiki/Binary-indexes) - [Search Tuning](https://github.com/facebookresearch/faiss/wiki/Faster-search) Note: For macOS users, an existing bug in an upstream package restricts the number of processing threads to 1. This limitation is managed internally to prevent system crashes. ### hnsw ```yaml hnsw: efconstruction: ef_construction param for init_index (int) - defaults to 200 m: M param for init_index (int) - defaults to 16 randomseed: random-seed param for init_index (int) - defaults to 100 efsearch: ef search param (int) - defaults to None and not set ``` See [Hnswlib documentation](https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md) for more information on these parameters. ### annoy ```yaml annoy: ntrees: number of trees (int) - defaults to 10 searchk: search_k search setting (int) - defaults to -1 ``` See [Annoy documentation](https://github.com/spotify/annoy#full-python-api) for more information on these parameters. Note that annoy indexes can not be modified after creation, upserts/deletes and other modifications are not supported. ### ggml ```yaml ggml: gpu: enable GPU - defaults to True quantize: sets the tensor quantization - defaults to F32 querysize: query buffer size - defaults to 64 ``` The [GGML](https://github.com/ggml-org/ggml) backend is a k-nearest neighbors backend. It stores tensors using GGML and [GGUF](https://huggingface.co/docs/hub/en/gguf). It supports GPU-enabled operations and supports quantization. GGML is the framework used by [llama.cpp](https://github.com/ggml-org/llama.cpp). [See this](https://github.com/ggml-org/ggml/blob/master/include/ggml.h#L379) for a list of quantization types. ### numpy The NumPy backend is a k-nearest neighbors backend. It's designed for simplicity and works well with smaller datasets that fit into memory. ```yaml numpy: safetensors: stores vectors using the safetensors format defaults to NumPy array storage ``` ### torch The Torch backend is a k-nearest neighbors backend like NumPy. It supports GPU-enabled operations. It also has support for quantization which enables fitting larger arrays into GPU memory. When quantization is enabled, vectors are _always_ stored in safetensors. _Note that macOS support for quantization is limited._ ```yaml torch: safetensors: stores vectors using the safetensors format - defaults to NumPy array storage if quantization is disabled quantize: type: quantization type (fp4, nf4, int8) blocksize: quantization block size parameter ``` ### pgvector ```yaml pgvector: url: database url connection string, alternatively can be set via ANN_URL environment variable schema: database schema to store vectors - defaults to being determined by the database table: database table to store vectors - defaults to `vectors` precision: vector float precision (half or full) - defaults to `full` efconstruction: ef_construction param (int) - defaults to 200 m: M param for init_index (int) - defaults to 16 ``` The pgvector backend stores embeddings in a Postgres database. See the [pgvector documentation](https://github.com/pgvector/pgvector-python?tab=readme-ov-file#sqlalchemy) for more information on these parameters. See the [SQLAlchemy](https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls) documentation for more information on how to construct url connection strings. ### sqlite ```yaml sqlite: quantize: store vectors with x-bit precision vs 32-bit (boolean|int) true sets 8-bit precision, false disables, int sets specified precision table: database table to store vectors - defaults to `vectors` ``` The SQLite backend stores embeddings in a SQLite database using [sqlite-vec](https://github.com/asg017/sqlite-vec). This backend supports 1-bit and 8-bit quantization at the storage level. See [this note](https://alexgarcia.xyz/sqlite-vec/python.html#macos-blocks-sqlite-extensions-by-default) on how to run this ANN on MacOS.