{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-alibaba--zvec","slug":"alibaba--zvec","name":"zvec","type":"repo","url":"https://zvec.org","page_url":"https://unfragile.ai/alibaba--zvec","categories":["rag-knowledge"],"tags":["agent-skills","db","embedded","faiss","hnsw","llm-memory","local","rag","search-engine","semantic-search","similarity-search","vector-database","vector-db"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-alibaba--zvec__cap_0","uri":"capability://search.retrieval.in.process.vector.similarity.search.with.hnsw.indexing","name":"in-process vector similarity search with hnsw indexing","description":"Executes approximate nearest neighbor search directly within application memory using Hierarchical Navigable Small World (HNSW) graph indexes, eliminating network latency and external server dependencies. Implements multi-layer graph traversal with configurable M (max connections) and ef (search expansion factor) parameters to balance recall vs latency tradeoffs. Supports both dense and sparse vector embeddings within a single collection, with native handling of variable-dimension vectors through the zvec_core search engine.","intents":["I need to search millions of vectors in milliseconds without deploying a separate database server","I want to embed semantic search directly into my application with minimal operational overhead","I need to support both dense embeddings (e.g., from BERT) and sparse embeddings (e.g., BM25) in the same query","I want to avoid network round-trips for vector similarity operations in latency-critical applications"],"best_for":["solo developers building LLM agents with local RAG pipelines","teams deploying edge AI applications where external databases are unavailable","high-frequency trading or real-time recommendation systems requiring sub-millisecond latency","research teams prototyping vector search algorithms without infrastructure overhead"],"limitations":["HNSW index construction is single-threaded and memory-intensive; building indexes for >1B vectors requires offline preprocessing via local_builder tool","No built-in distributed sharding — all data must fit in process memory; horizontal scaling requires application-level partitioning","Graph structure is not persistent across index rebuilds; full reindexing required when adding large batches of vectors","Recall quality degrades with very high-dimensional vectors (>10k dims) without quantization; RaBitQ quantization adds ~5-10% latency overhead"],"requires":["Python 3.8+ or C++17 compiler","Embedding vectors pre-computed (zvec does not generate embeddings)","RAM sufficient for index + data (typically 10-50 bytes per vector depending on dimension and quantization)","Linux (x86_64, ARM64), macOS (ARM64), or Windows (x86_64)"],"input_types":["float32 dense vectors (1-10k dimensions)","sparse vectors (coordinate format with indices and values)","scalar metadata (strings, integers, floats for filtering)"],"output_types":["ranked list of document IDs with similarity scores","structured results with metadata and optional re-ranking scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_1","uri":"capability://search.retrieval.hybrid.vector.scalar.filtering.with.sql.query.planning","name":"hybrid vector-scalar filtering with sql query planning","description":"Combines dense vector similarity search with structured scalar filters (e.g., date ranges, categorical tags) through a unified SQL query engine that optimizes filter pushdown and index selection. The query planner analyzes predicates to determine whether to apply filters before (pre-filter) or after (post-filter) vector search, minimizing irrelevant vector comparisons. Supports complex boolean expressions on metadata fields while maintaining vector search semantics through the zvec_db layer's query interface.","intents":["I need to find similar documents but only within a specific date range or category","I want to combine semantic search with business logic filters (e.g., 'documents from verified sources with similarity > 0.8')","I need to efficiently narrow the search space before expensive vector operations","I want to avoid materializing all results and then filtering in application code"],"best_for":["e-commerce platforms combining product embeddings with inventory/pricing filters","document retrieval systems filtering by source, date, or access control lists","multi-tenant SaaS applications isolating vector search results by tenant ID","compliance-heavy industries (finance, healthcare) requiring audit-trail filtering"],"limitations":["Query planner does not support complex nested boolean expressions; deeply nested AND/OR conditions may fall back to post-filtering with performance degradation","Scalar filter cardinality estimation is naive; highly selective filters may not be pushed down optimally, requiring manual query rewriting","No support for range queries on vector similarity scores themselves (e.g., 'similarity BETWEEN 0.7 AND 0.9'); must retrieve top-k and filter in application","Metadata indexing is optional; unindexed scalar fields force full-collection scans before vector search"],"requires":["Scalar metadata fields defined in CollectionSchema with explicit types (string, int, float, bool)","Optional: indexes on frequently-filtered fields for optimal query planning","Python 3.8+ or C++ API consumer"],"input_types":["VectorQuery with vector embedding and optional filter predicates","scalar metadata (strings, integers, floats, booleans) stored alongside vectors","SQL-like filter expressions (e.g., 'category = \"electronics\" AND price < 100')"],"output_types":["filtered ranked list of document IDs matching both vector similarity and scalar predicates","query execution plan (for debugging/optimization)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_10","uri":"capability://data.processing.analysis.batch.vector.insertion.with.automatic.segment.flushing","name":"batch vector insertion with automatic segment flushing","description":"Accepts multiple vectors and metadata in a single batch operation, buffering them in memory until a configurable threshold (e.g., 100k vectors) is reached, then automatically flushing to a new segment. Batch insertion amortizes the cost of segment creation and metadata updates across multiple vectors, improving throughput compared to single-vector inserts. The flush operation is asynchronous; queries can proceed while new segments are being written to disk.","intents":["I want to insert millions of vectors efficiently without creating a segment per vector","I need to balance write throughput with query latency during bulk data ingestion","I want to configure flush frequency based on my application's memory and latency constraints","I need to monitor insertion progress and handle failures during bulk operations"],"best_for":["real-time RAG systems ingesting documents in batches (e.g., hourly or daily updates)","data pipelines that periodically compute embeddings and insert them into the index","systems where write throughput is more important than individual insert latency","applications with predictable batch sizes (e.g., 10k documents per batch)"],"limitations":["Batch size and flush frequency are global settings; cannot configure per-batch or per-collection","Flushing is not transactional; partial batches may be lost if the process crashes before flush completes","No built-in deduplication; inserting the same vector twice creates duplicate entries","Batch insertion is single-threaded; cannot parallelize inserts across multiple threads without external synchronization"],"requires":["Batch configuration (max_batch_size, flush_interval_ms)","Vectors and metadata in memory (numpy arrays, Python lists, or C++ vectors)","Sufficient disk space for segment files"],"input_types":["list of vectors (numpy arrays or lists of floats)","list of metadata dicts (one per vector)","list of document IDs (strings)"],"output_types":["insertion status (success/failure per vector)","segment ID (for tracking which segment contains inserted vectors)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_11","uri":"capability://tool.use.integration.embedding.function.abstraction.with.pluggable.re.rankers","name":"embedding function abstraction with pluggable re-rankers","description":"Provides an abstraction layer for embedding functions that can be registered with a collection, enabling automatic embedding computation during insertion and query. Supports pluggable re-rankers that post-process search results using alternative similarity metrics (e.g., cross-encoder models) to improve ranking quality. Re-rankers are applied transparently after vector search, trading ~10-50% latency overhead for improved result quality.","intents":["I want to automatically compute embeddings during insertion without manual preprocessing","I need to re-rank search results using a more expensive but accurate model (e.g., cross-encoder)","I want to experiment with different embedding models without changing application code","I need to combine multiple embedding models (e.g., dense + sparse) in a single query"],"best_for":["RAG systems where embedding computation is part of the indexing pipeline","applications requiring high-quality ranking (e.g., search engines, recommendation systems)","teams experimenting with different embedding models and re-ranking strategies","systems where embedding latency is acceptable during indexing but critical during queries"],"limitations":["Embedding functions are not versioned; changing embedding models requires reindexing the entire collection","Re-rankers are applied to all queries; no per-query control over re-ranking","Embedding function must be deterministic; non-deterministic embeddings (e.g., with dropout) produce inconsistent results","Re-ranker latency is not bounded; slow re-rankers can dominate query latency"],"requires":["Embedding function implementation (e.g., Hugging Face model, OpenAI API client)","Optional: re-ranker implementation (e.g., cross-encoder model)","Python 3.8+ or C++ API consumer"],"input_types":["text documents (for embedding computation)","embedding function callable (Python function or C++ callback)","re-ranker callable (Python function or C++ callback)"],"output_types":["computed embeddings (float32 vectors)","re-ranked results with updated scores"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_12","uri":"capability://automation.workflow.concurrent.query.execution.with.segment.level.parallelism","name":"concurrent query execution with segment-level parallelism","description":"Executes queries in parallel across multiple segments, with each segment searched independently and results merged at the end. The query executor uses thread pools to parallelize segment searches, enabling multi-core utilization for large collections with many segments. Concurrent queries on different collections do not block each other; read-write conflicts are avoided through segment immutability.","intents":["I want to maximize throughput for concurrent queries on large collections","I need to utilize multiple CPU cores for vector search","I want to avoid query latency spikes when multiple queries arrive simultaneously","I need to support high-concurrency workloads (100+ QPS) on a single machine"],"best_for":["high-throughput search services (100+ QPS per machine)","systems with many small segments (e.g., from frequent insertions)","multi-core servers where CPU is the bottleneck","applications where query latency percentiles (p99, p999) are critical"],"limitations":["Parallelism is limited by number of segments; collections with few large segments cannot parallelize queries","Thread pool size is global; cannot configure per-collection or per-query parallelism","Context switching overhead may dominate for very fast queries (< 1ms); parallelism may not improve latency for small result sets","Memory bandwidth is shared across cores; very large vectors (>10k dims) may be bandwidth-limited despite parallelism"],"requires":["Multi-core CPU (2+ cores for meaningful parallelism)","Thread pool configuration (number of threads, queue size)","Multiple segments (automatic from batch insertion or offline construction)"],"input_types":["VectorQuery objects","thread pool configuration"],"output_types":["merged results from all segments","query execution statistics (per-segment latencies)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_13","uri":"capability://automation.workflow.persistent.storage.with.memory.mapped.file.access","name":"persistent storage with memory-mapped file access","description":"Stores index segments as binary files on disk with memory-mapped access, enabling efficient loading of large indexes without copying data into memory. Segment files include metadata headers (vector count, dimension, index type, quantization parameters) followed by index data. Memory-mapped access allows the OS to page segments in/out based on access patterns, enabling indexes larger than physical RAM. Checksums protect against corruption.","intents":["I want to persist my index to disk and reload it without recomputing embeddings","I need to load very large indexes (>100GB) without copying data into memory","I want to share index files across multiple processes without duplication","I need to protect against index corruption and detect data integrity issues"],"best_for":["production systems requiring persistent storage and recovery","large-scale deployments where index size exceeds available RAM","multi-process systems where index sharing reduces memory overhead","systems with strict data integrity requirements (checksums, validation)"],"limitations":["Memory-mapped files are OS-dependent; behavior differs between Linux, macOS, and Windows","Segment files are not human-readable; debugging requires specialized tools","Index format is tied to zvec version; upgrading zvec may require index rebuilding","Memory-mapped access has higher latency than in-memory access for random patterns; sequential access is optimized"],"requires":["Persistent storage (filesystem with sufficient space)","Memory-mapped file support (all modern OSes)","Read permissions for index files (no write permissions required for queries)"],"input_types":["index segment data (vectors, metadata, index structures)","segment metadata (vector count, dimension, index type)"],"output_types":["segment files (binary format with metadata headers)","checksum validation results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_2","uri":"capability://data.processing.analysis.rabitq.quantization.with.lossless.re.ranking","name":"rabitq quantization with lossless re-ranking","description":"Compresses vector embeddings using Rotation-Aware Bit Quantization (RaBitQ) to reduce memory footprint and accelerate distance computations, then re-ranks top-k candidates using original full-precision vectors to recover recall lost during quantization. The quantization pipeline learns rotation matrices per segment to align high-variance dimensions, enabling 8-16x compression while maintaining >95% recall. Re-ranking is applied transparently during query execution, trading ~5-10% latency overhead for dramatic memory savings.","intents":["I need to index billions of vectors but only have limited RAM available","I want to reduce model serving costs by compressing embeddings without sacrificing search quality","I need to fit high-dimensional embeddings (e.g., 1536-dim OpenAI embeddings) into memory-constrained edge devices","I want automatic recall recovery without manually tuning quantization parameters"],"best_for":["mobile and edge AI applications with strict memory budgets (<1GB)","large-scale RAG systems indexing 100M+ documents with commodity hardware","cost-sensitive cloud deployments where memory is the primary expense driver","real-time systems where inference latency is critical but indexing latency is flexible"],"limitations":["Quantization is learned per segment; adding new vectors to an existing segment may degrade quantization quality if the new vectors have different statistical properties","Re-ranking requires storing original vectors or reconstructing them from quantized values; full lossless recovery requires ~25% additional storage overhead","Quantization learning requires a representative sample of vectors; poor sample selection leads to suboptimal rotation matrices and recall degradation","RaBitQ is optimized for dense vectors; sparse vectors cannot be quantized and must be stored at full precision"],"requires":["Segment-based storage architecture (zvec_db handles this automatically)","Representative sample of vectors for learning rotation matrices (typically 10k-100k vectors)","Python 3.8+ or C++ API consumer","CPU with AVX2 or better for efficient quantized distance computation"],"input_types":["float32 dense vectors (typically 128-2048 dimensions)","segment configuration specifying quantization bit width (8, 16 bits)"],"output_types":["quantized vector index with 8-16x compression ratio","re-ranked results with full-precision similarity scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_3","uri":"capability://search.retrieval.multi.index.strategy.selection.hnsw.ivf.flat","name":"multi-index strategy selection (hnsw, ivf, flat)","description":"Provides three index types optimized for different recall-latency-memory tradeoffs: HNSW for balanced performance on medium-scale datasets (millions of vectors), IVF (Inverted File) for very large-scale datasets (billions of vectors) with coarse quantization, and Flat (brute-force) for small datasets or when 100% recall is required. The schema definition allows specifying index type and parameters (e.g., HNSW M=16, IVF nlist=1000) per collection, with automatic index selection based on dataset size heuristics if not explicitly configured.","intents":["I need to choose the right index type for my dataset size and latency requirements","I want to experiment with different index strategies without rewriting application code","I need 100% recall for small datasets but can tolerate approximate search for large datasets","I want to understand the memory and latency tradeoffs of each index type"],"best_for":["teams evaluating vector search strategies before committing to a specific approach","applications with variable dataset sizes that need to scale from thousands to billions of vectors","research projects comparing index algorithms (HNSW vs IVF vs brute-force)","systems requiring different index types for different collections (e.g., Flat for small reference sets, HNSW for large user embeddings)"],"limitations":["Index type cannot be changed after collection creation; migrating from one index type to another requires rebuilding the entire collection","IVF requires careful tuning of nlist (number of inverted lists) and nprobe (lists to search); suboptimal values lead to either slow queries or poor recall","Flat index is impractical for >10M vectors due to O(n) query complexity; queries become prohibitively slow","No automatic index selection based on query patterns; users must manually choose index type upfront based on estimated dataset size"],"requires":["CollectionSchema definition with explicit index_type parameter","Index-specific parameters (e.g., M and ef for HNSW, nlist for IVF)","Python 3.8+ or C++ API consumer"],"input_types":["index type specification (HNSW, IVF, Flat)","index parameters (M, ef, nlist, nprobe, etc.)"],"output_types":["index object with query interface","performance metrics (latency, recall, memory usage)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_4","uri":"capability://data.processing.analysis.segment.based.storage.with.incremental.updates","name":"segment-based storage with incremental updates","description":"Organizes data into immutable segments that are independently indexed and queried, enabling efficient incremental updates without full index rebuilds. New vectors are written to a mutable buffer that periodically flushes to a new segment; queries transparently search all segments and merge results. Segments are stored as binary files with metadata headers, supporting both in-memory and memory-mapped access patterns. This architecture enables concurrent reads while writes are buffered, avoiding lock contention.","intents":["I need to add new vectors to my index without rebuilding the entire HNSW graph","I want to support concurrent reads and writes without blocking queries","I need to persist my index to disk and reload it without recomputing embeddings","I want to delete or update documents without reindexing the entire collection"],"best_for":["real-time RAG systems that continuously ingest new documents","production systems requiring zero-downtime updates","applications with append-heavy workloads (logs, events, documents)","systems needing to balance write throughput with query latency"],"limitations":["Segment merging is not automatic; many small segments degrade query performance; manual compaction or background merging required","Deletes are soft-deletes (marked as deleted, not physically removed); space is not reclaimed until segment compaction","Segment size is fixed at creation time; cannot dynamically adjust segment boundaries based on data distribution","Concurrent writes to the same collection require external synchronization; zvec does not provide built-in write locking"],"requires":["Persistent storage (filesystem or memory-mapped file support)","Segment configuration specifying max vectors per segment and flush frequency","Python 3.8+ or C++ API consumer","External coordination for multi-process writes (e.g., distributed lock or single writer)"],"input_types":["vectors and metadata to insert","document IDs to delete or update","segment configuration (max_segment_size, flush_interval)"],"output_types":["segment files (binary format with metadata headers)","segment manifest (list of active segments and their properties)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_5","uri":"capability://data.processing.analysis.simd.accelerated.distance.computation.with.cpu.auto.dispatch","name":"simd-accelerated distance computation with cpu auto-dispatch","description":"Automatically detects CPU capabilities at runtime and dispatches distance computations to optimized SIMD kernels (AVX-512 VNNI, AVX2, SSE) without manual configuration. The ailego utility library provides vectorized implementations of L2, cosine, and inner product distances, with specialized kernels for quantized vectors. CPU Auto-Dispatch eliminates the need for separate binaries per architecture, enabling single-binary deployments across heterogeneous hardware (x86_64, ARM64).","intents":["I want vector search to automatically use the fastest available SIMD instructions without manual tuning","I need to deploy the same binary across servers with different CPU generations (Skylake, Cascade Lake, Ice Lake)","I want to maximize throughput for batch similarity computations","I need consistent performance across Linux, macOS, and Windows without architecture-specific builds"],"best_for":["cloud deployments with heterogeneous instance types (AWS c5, c6, c7 instances)","edge deployments where binary size and build complexity must be minimized","high-throughput batch processing systems (e.g., offline embedding similarity computation)","teams without specialized SIMD optimization expertise"],"limitations":["SIMD kernel selection is determined at first query execution; cannot be changed without process restart","AVX-512 kernels are optimized for Intel CPUs; AMD Ryzen AVX-512 support is limited and may not benefit from VNNI optimizations","ARM64 SIMD support (NEON) is less optimized than x86_64 AVX-512; ARM deployments typically see 30-50% lower throughput","Quantized distance computation has different SIMD paths; mixing quantized and unquantized vectors in the same query may not benefit from full SIMD acceleration"],"requires":["CPU with SSE2 support (baseline; all modern CPUs)","Optional: AVX2 or AVX-512 for 2-8x speedup","Linux (x86_64, ARM64), macOS (ARM64), or Windows (x86_64)","C++ compiler with SIMD intrinsics support (GCC 7+, Clang 6+, MSVC 2019+)"],"input_types":["dense vectors (float32)","quantized vectors (int8, int16)","distance metric specification (L2, cosine, inner product)"],"output_types":["similarity scores (float32)","performance metrics (throughput in vectors/second)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_6","uri":"capability://tool.use.integration.python.api.with.pybind11.c.bindings","name":"python api with pybind11 c++ bindings","description":"Exposes zvec's C++ core through a Pythonic API using pybind11, providing classes like CollectionSchema, VectorQuery, and Doc for intuitive data manipulation. The bindings maintain zero-copy semantics for vector data, avoiding serialization overhead when passing large arrays between Python and C++. Type hints and docstrings enable IDE autocompletion and documentation discovery, while exception handling translates C++ errors to Python exceptions.","intents":["I want to use zvec from Python without learning C++ or managing FFI complexity","I need IDE autocompletion and type hints for vector search operations","I want to integrate zvec into Jupyter notebooks and data science workflows","I need to avoid serialization overhead when passing numpy arrays to the search engine"],"best_for":["data scientists and ML engineers building RAG prototypes in Python","teams using Python-first ML stacks (PyTorch, Hugging Face, LangChain)","research projects requiring rapid iteration and interactive development","production systems where Python is the primary application language"],"limitations":["pybind11 bindings add ~5-10% overhead per operation due to Python/C++ boundary crossing; performance-critical loops should use C++ API directly","Type hints are not enforced at runtime; passing wrong types (e.g., int32 instead of float32 vectors) fails at C++ boundary with cryptic errors","GIL (Global Interpreter Lock) is not released during long-running C++ operations; multi-threaded Python code cannot parallelize vector search across cores","Wheel distribution requires pre-built binaries for each Python version and platform; building from source requires C++ compiler and CMake"],"requires":["Python 3.8+","numpy (for vector data representation)","Pre-built wheel for your platform (Linux x86_64/ARM64, macOS ARM64, Windows x86_64) or C++ compiler + CMake for building from source"],"input_types":["numpy arrays (float32 dense vectors)","Python lists or dicts (metadata)","Python strings (document IDs)"],"output_types":["Python lists of tuples (document ID, similarity score)","Python dicts (structured results with metadata)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_7","uri":"capability://automation.workflow.offline.index.construction.with.local.builder.tool","name":"offline index construction with local_builder tool","description":"Provides a standalone CLI tool (local_builder) that pre-computes HNSW and IVF indexes from raw vector files without loading data into memory, enabling efficient batch index construction for billion-scale datasets. The tool reads vectors from binary or text formats, applies quantization if specified, and writes index segments to disk. This decouples index construction (expensive, one-time) from query serving (latency-critical), enabling offline preprocessing on high-memory machines before deploying to resource-constrained environments.","intents":["I need to build indexes for billions of vectors without running out of memory","I want to pre-compute indexes offline and deploy them to production without rebuilding","I need to apply quantization to reduce index size before deployment","I want to parallelize index construction across multiple machines"],"best_for":["large-scale RAG systems with 100M+ documents indexed once and queried frequently","batch processing pipelines that compute embeddings offline and build indexes separately","teams deploying to memory-constrained environments (edge devices, serverless functions)","systems where index construction latency is acceptable but query latency is critical"],"limitations":["local_builder is single-threaded; index construction is CPU-bound and cannot be parallelized within a single machine","No support for distributed index construction; building indexes for >10B vectors requires manual sharding across machines","Index format is tied to zvec version; upgrading zvec may require rebuilding indexes","local_builder requires all vectors to fit in a single file; very large datasets must be split and merged manually"],"requires":["Vector data in binary format (float32 arrays) or text format (one vector per line)","local_builder binary (included in zvec distribution)","Disk space for output index (typically 10-50 bytes per vector depending on quantization)","High-memory machine for construction (RAM = vector_count * dimension * 4 bytes + overhead)"],"input_types":["binary files (raw float32 arrays)","text files (space-separated or comma-separated vectors)","configuration file specifying index type, quantization, and parameters"],"output_types":["index segment files (binary format)","segment manifest (metadata about index properties)","construction report (timing, memory usage, index statistics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_8","uri":"capability://data.processing.analysis.collection.schema.definition.with.type.safe.metadata","name":"collection schema definition with type-safe metadata","description":"Defines collection structure through CollectionSchema and VectorSchema classes that specify vector dimensions, data types, index parameters, and metadata fields with explicit types (string, int, float, bool). Schema validation occurs at collection creation time, preventing runtime type mismatches. Metadata fields can be indexed for efficient filtering, and schema can be introspected at runtime to enable dynamic query construction.","intents":["I want to define the structure of my vector collection upfront with type safety","I need to specify which metadata fields should be indexed for efficient filtering","I want to validate data types before inserting vectors and metadata","I need to introspect collection schema at runtime for dynamic query construction"],"best_for":["production systems requiring strict schema validation and type safety","teams building multi-tenant systems where schema isolation is critical","applications with complex metadata requirements (multiple indexed fields, mixed types)","systems where schema evolution must be carefully managed"],"limitations":["Schema is immutable after collection creation; adding or removing fields requires recreating the collection","No schema versioning or migration tools; evolving schemas requires manual data migration","Metadata field types are limited to primitives (string, int, float, bool); no support for nested objects or arrays","Index configuration is per-collection; cannot have different index types for different metadata fields"],"requires":["Python 3.8+ or C++ API consumer","Explicit schema definition before collection creation","Type-safe metadata values matching schema definition"],"input_types":["VectorSchema specification (dimension, metric, index type)","metadata field definitions (name, type, indexed flag)","index parameters (HNSW M/ef, IVF nlist, quantization settings)"],"output_types":["CollectionSchema object with validation and introspection methods","schema validation errors (type mismatches, missing required fields)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alibaba--zvec__cap_9","uri":"capability://tool.use.integration.c.api.for.language.agnostic.integration","name":"c api for language-agnostic integration","description":"Exposes zvec functionality through a C API (C99 compatible) that enables integration with any language supporting C FFI (Go, Rust, Java, C#, etc.). The C API provides opaque pointers to collections, queries, and results, with explicit memory management functions (malloc/free) for language binding authors. C API examples demonstrate integration patterns for common languages, enabling teams to build language-specific wrappers without modifying zvec core.","intents":["I need to use zvec from a language other than Python or C++","I want to build a language-specific wrapper for zvec without modifying the core","I need to integrate zvec into a polyglot system with multiple language components","I want to maintain ABI stability across zvec versions for production deployments"],"best_for":["teams using Go, Rust, Java, or C# as primary languages","polyglot systems where different components use different languages","organizations requiring ABI stability for long-term maintenance","systems where C FFI is preferred over language-specific bindings"],"limitations":["C API is lower-level than Python API; error handling requires manual null-pointer checks and error code inspection","Memory management is manual; language binding authors must correctly pair allocations with deallocations to avoid leaks","C API does not provide high-level abstractions (e.g., context managers); bindings must implement these patterns","ABI compatibility is not guaranteed across minor versions; breaking changes may require recompilation of dependent code"],"requires":["C compiler (GCC, Clang, MSVC)","C FFI support in target language (cgo for Go, FFI for Rust, JNI for Java, P/Invoke for C#)","Understanding of C memory management and pointer semantics"],"input_types":["opaque pointers to collections and queries","C arrays of floats (vectors)","C strings (document IDs, metadata)"],"output_types":["opaque result pointers","C arrays of document IDs and scores","error codes (integer return values)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or C++17 compiler","Embedding vectors pre-computed (zvec does not generate embeddings)","RAM sufficient for index + data (typically 10-50 bytes per vector depending on dimension and quantization)","Linux (x86_64, ARM64), macOS (ARM64), or Windows (x86_64)","Scalar metadata fields defined in CollectionSchema with explicit types (string, int, float, bool)","Optional: indexes on frequently-filtered fields for optimal query planning","Python 3.8+ or C++ API consumer","Batch configuration (max_batch_size, flush_interval_ms)","Vectors and metadata in memory (numpy arrays, Python lists, or C++ vectors)","Sufficient disk space for segment files"],"failure_modes":["HNSW index construction is single-threaded and memory-intensive; building indexes for >1B vectors requires offline preprocessing via local_builder tool","No built-in distributed sharding — all data must fit in process memory; horizontal scaling requires application-level partitioning","Graph structure is not persistent across index rebuilds; full reindexing required when adding large batches of vectors","Recall quality degrades with very high-dimensional vectors (>10k dims) without quantization; RaBitQ quantization adds ~5-10% latency overhead","Query planner does not support complex nested boolean expressions; deeply nested AND/OR conditions may fall back to post-filtering with performance degradation","Scalar filter cardinality estimation is naive; highly selective filters may not be pushed down optimally, requiring manual query rewriting","No support for range queries on vector similarity scores themselves (e.g., 'similarity BETWEEN 0.7 AND 0.9'); must retrieve top-k and filter in application","Metadata indexing is optional; unindexed scalar fields force full-collection scans before vector search","Batch size and flush frequency are global settings; 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