{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-milvus","slug":"pypi-milvus","name":"milvus","type":"repo","url":"https://github.com/milvus-io/milvus-lite","page_url":"https://unfragile.ai/pypi-milvus","categories":["rag-knowledge"],"tags":["Milvus","Embeded","Milvus","Milvus","Server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-milvus__cap_0","uri":"capability://memory.knowledge.embedded.vector.database.initialization.with.subprocess.management","name":"embedded vector database initialization with subprocess management","description":"Milvus Lite spawns and manages a native C++ milvus binary as a subprocess, eliminating the need for separate server infrastructure. The ServerManager component handles process lifecycle (startup, shutdown, cleanup), while the Python client communicates via gRPC to the MilvusServiceImpl endpoint. This single-process architecture uses SQLite for file-based persistence, enabling zero-configuration deployment in Jupyter notebooks, laptops, and edge devices without Docker or Kubernetes.","intents":["I want to prototype a vector search application without setting up a separate database server","I need to run vector search in a Jupyter notebook or Google Colab without infrastructure overhead","I want to deploy vector search on edge devices or resource-constrained environments"],"best_for":["data scientists and ML engineers prototyping in notebooks","solo developers building proof-of-concepts","teams deploying to edge devices or laptops with <1M vectors"],"limitations":["Single-process architecture limits horizontal scaling — not suitable for multi-user production workloads","SQLite backend has performance constraints compared to distributed Milvus deployments","Windows support not yet available (planned for future releases)","Subprocess management adds ~50-200ms startup latency on first connection"],"requires":["Python 3.8+","Ubuntu 20.04+ (x86_64, ARM64) or macOS 11.0+ (Intel, Apple Silicon)","~50MB disk space for embedded milvus binary","pymilvus Python package"],"input_types":["connection URI (local file path or remote endpoint)","collection schema definitions","vector embeddings (float32/float16 arrays)"],"output_types":["MilvusClient instance","collection metadata","search results with scores"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_1","uri":"capability://data.processing.analysis.schema.based.collection.management.with.dynamic.field.definition","name":"schema-based collection management with dynamic field definition","description":"Milvus Lite provides a schema definition system that allows developers to declare collections with typed fields (vectors, scalars, text) before data insertion. The schema validation occurs at the MilvusProxy layer, enforcing field types, dimensions, and constraints. Collections are persisted in SQLite and indexed via the Index component, supporting multiple vector types (dense float32/float16, sparse vectors) and scalar fields (int, float, string, bool) with optional filtering capabilities.","intents":["I want to define a collection schema with vector and scalar fields before inserting data","I need to enforce data types and vector dimensions across my dataset","I want to create collections that support both dense and sparse vector search"],"best_for":["developers building structured vector search applications","teams migrating from relational databases to vector-native schemas","applications requiring mixed vector and scalar field queries"],"limitations":["Schema is immutable after collection creation — cannot add/remove fields without recreating the collection","Vector dimension must be specified at schema definition time and cannot be changed","No automatic schema inference — explicit schema definition required","Sparse vector support requires manual field configuration"],"requires":["pymilvus client library","schema definition with FieldSchema objects","vector dimension specified in advance","field names and types declared before collection creation"],"input_types":["FieldSchema objects (name, dtype, dim, is_primary_key)","CollectionSchema wrapper","field metadata (nullable, default values)"],"output_types":["Collection object with schema metadata","field type validation results","collection statistics (row count, memory usage)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_10","uri":"capability://automation.workflow.multi.platform.binary.packaging.with.conditional.compilation","name":"multi-platform binary packaging with conditional compilation","description":"Milvus Lite uses CMake-based conditional compilation to build optimized binaries for multiple platforms (Ubuntu x86_64/ARM64, macOS Intel/Apple Silicon), with platform-specific code paths and dependencies. The Python package build system (setup.py, pyproject.toml) downloads the appropriate precompiled binary (~50MB) during installation, eliminating the need for users to compile C++ code. The build system detects the target platform and architecture, selecting the correct binary variant automatically.","intents":["I want to install Milvus Lite on my laptop without compiling C++ code","I need to deploy to multiple platforms (Linux, macOS) with a single pip install","I want to use Milvus Lite on Apple Silicon (M1/M2) Macs"],"best_for":["developers on macOS and Linux who want zero-compilation installation","teams deploying to heterogeneous hardware (Intel + ARM)","users without C++ build tools installed"],"limitations":["Windows support not yet available (planned for future releases)","Precompiled binaries add ~50MB to package size","Custom compilation not supported — must use prebuilt binaries","Binary compatibility requires exact OS/architecture match — no cross-platform binaries"],"requires":["Python 3.8+","pip or conda package manager","Ubuntu 20.04+ (x86_64, ARM64) or macOS 11.0+ (Intel, Apple Silicon)","internet connection for binary download"],"input_types":["pip install command"],"output_types":["installed pymilvus package with embedded milvus binary"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_2","uri":"capability://search.retrieval.vector.similarity.search.with.configurable.distance.metrics.and.filtering","name":"vector similarity search with configurable distance metrics and filtering","description":"Milvus Lite executes vector similarity searches through the Query Processing layer, which accepts a query vector and returns ranked results based on configurable distance metrics (L2, IP, COSINE, HAMMING). The search operation supports optional scalar filtering via WHERE clauses, limit/offset pagination, and output field selection. The Index component maintains in-memory vector indexes (FLAT, IVF_FLAT, HNSW, etc.) that are queried during search, with results ranked by similarity score and optionally re-ranked by scalar fields.","intents":["I want to find the top-k most similar vectors to a query vector","I need to search vectors with scalar filtering (e.g., find similar items from a specific category)","I want to use different distance metrics (L2, cosine, inner product) for different use cases"],"best_for":["semantic search applications (documents, images, embeddings)","recommendation systems with vector similarity","applications requiring filtered vector search"],"limitations":["Search latency increases with collection size and index type — FLAT is O(n) but accurate, HNSW is O(log n) but approximate","Scalar filtering is applied post-search (not pre-filtered), reducing efficiency for highly selective filters","Distance metric must be specified at index creation time and cannot be changed per-query","No built-in query result caching — repeated queries incur full search cost"],"requires":["collection with vector field indexed","query vector matching the collection's vector dimension and dtype","distance metric specified during index creation (L2, IP, COSINE, HAMMING)","optional WHERE clause for scalar filtering"],"input_types":["query vector (numpy array or list of floats)","search parameters (top_k, metric_type, filter expression)","output field names (optional)"],"output_types":["ranked list of result objects with id, distance, and scalar fields","distance scores (float values)","result count and pagination metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_3","uri":"capability://search.retrieval.bm25.full.text.search.with.sparse.vector.indexing","name":"bm25 full-text search with sparse vector indexing","description":"Milvus Lite supports BM25 full-text search through sparse vector indexing, where text fields are tokenized and converted to sparse vector representations. The Index component creates sparse indexes that enable keyword-based retrieval with TF-IDF weighting. Sparse vectors can be searched independently or combined with dense vectors in hybrid search queries, with results ranked by BM25 relevance scores. This capability bridges traditional full-text search and modern vector search in a single system.","intents":["I want to perform keyword-based search on text fields using BM25 ranking","I need to combine full-text search with semantic vector search in a single query","I want to index and search sparse vector representations of documents"],"best_for":["hybrid search applications combining keyword and semantic relevance","document retrieval systems requiring both exact and fuzzy matching","RAG pipelines needing multi-modal search (text + embeddings)"],"limitations":["Sparse vector indexing requires manual tokenization and sparse vector generation — no built-in text-to-sparse conversion","BM25 scoring is computed at search time, not pre-computed, adding query latency","Sparse vectors consume more memory than dense vectors for high-dimensional text representations","No built-in stemming or lemmatization — requires preprocessing"],"requires":["sparse vector field defined in collection schema","sparse vector data (dict format with indices and values)","optional text field for reference","BM25 index type specified during index creation"],"input_types":["sparse vectors (dict with indices and values, or COO format)","text content (for reference or preprocessing)","search parameters (top_k, BM25 weights)"],"output_types":["ranked results with BM25 scores","sparse vector similarity scores","matched document IDs and metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_4","uri":"capability://search.retrieval.hybrid.search.with.multi.vector.ranking.and.re.ranking","name":"hybrid search with multi-vector ranking and re-ranking","description":"Milvus Lite enables hybrid search by combining results from multiple vector indexes (dense + sparse) or multiple dense indexes with different metrics, then re-ranking by weighted scores or scalar fields. The Query Processing layer executes parallel searches across indexes and merges results using configurable weighting strategies (e.g., 70% semantic relevance + 30% BM25 score). Re-ranking can apply scalar field sorting (e.g., recency, popularity) to refine final rankings without re-executing searches.","intents":["I want to search using both semantic similarity and keyword relevance in a single query","I need to combine multiple ranking signals (vector similarity, BM25, scalar metadata) into final results","I want to re-rank search results by business logic (recency, popularity, user preferences)"],"best_for":["e-commerce search combining product embeddings with keyword matching","content recommendation systems with multi-signal ranking","RAG systems requiring both semantic and keyword retrieval"],"limitations":["Re-ranking is applied post-search, not pre-filtered, so all indexes must be searched before ranking","Weighting strategy must be specified at query time — no learned ranking models","Parallel index searches add latency compared to single-index search","No automatic weight tuning — developers must manually configure blend ratios"],"requires":["multiple indexed fields (dense vectors + sparse vectors, or multiple dense indexes)","weighting configuration for combining scores","optional scalar fields for re-ranking","search parameters for each index type"],"input_types":["query vectors (dense and/or sparse)","weight configuration (dict mapping index names to weights)","re-ranking criteria (scalar field names and sort order)","search limits and filters"],"output_types":["merged and re-ranked result list","combined scores from multiple indexes","result metadata with individual index scores"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_5","uri":"capability://memory.knowledge.in.memory.index.creation.and.management.with.multiple.index.types","name":"in-memory index creation and management with multiple index types","description":"Milvus Lite's Index component creates and manages in-memory vector indexes (FLAT, IVF_FLAT, HNSW, etc.) that accelerate similarity search. Index creation is triggered explicitly via the create_index() API, specifying the index type, distance metric, and parameters (e.g., nlist for IVF, M/ef for HNSW). Indexes are built synchronously and stored in memory, with optional persistence to SQLite. The index selection strategy balances accuracy (FLAT is exact, HNSW is approximate) against query latency and memory consumption.","intents":["I want to create an index on a vector field to accelerate similarity search","I need to choose between exact (FLAT) and approximate (HNSW) indexing based on accuracy/speed tradeoffs","I want to configure index parameters (nlist, M, ef) for performance tuning"],"best_for":["developers optimizing vector search performance for production workloads","applications with known accuracy/latency requirements","teams tuning index parameters for specific hardware constraints"],"limitations":["Index creation is synchronous and blocks until completion — no async indexing","Index parameters cannot be changed after creation — must drop and recreate","FLAT index is O(n) search complexity, unsuitable for large collections (>100k vectors)","HNSW index memory overhead is ~8-16 bytes per vector plus edge storage","No automatic index selection — developers must choose index type manually"],"requires":["collection with vector field already created","index type specified (FLAT, IVF_FLAT, HNSW, SCANN, etc.)","distance metric matching the index type","index parameters (nlist for IVF, M/ef for HNSW)"],"input_types":["field name (vector field to index)","index type (string: 'FLAT', 'IVF_FLAT', 'HNSW', etc.)","index parameters (dict with type-specific config)","metric type (L2, IP, COSINE, HAMMING)"],"output_types":["index creation status","index metadata (type, parameters, field name)","index statistics (memory usage, build time)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_6","uri":"capability://data.processing.analysis.crud.operations.with.upsert.and.batch.processing","name":"crud operations with upsert and batch processing","description":"Milvus Lite provides CRUD (Create, Read, Update, Delete) operations through the Data Operations layer, supporting insert, upsert, delete, and query methods. Upsert combines insert and update semantics, replacing existing records by primary key or inserting new ones. Batch operations accept lists of records and process them efficiently through the gRPC service layer, with results returned as operation summaries (inserted count, deleted count, etc.). All operations are persisted to SQLite and reflected immediately in subsequent queries.","intents":["I want to insert vectors and metadata into a collection","I need to update existing vectors without deleting and re-inserting","I want to delete records by ID or filter expression","I need to batch insert thousands of vectors efficiently"],"best_for":["applications with frequent data updates (embeddings, metadata)","batch data loading pipelines","real-time data ingestion systems"],"limitations":["Batch insert latency scales with batch size — no automatic batching optimization","Delete operations require primary key or filter expression — no bulk delete by collection","Upsert requires primary key field — cannot upsert without unique identifier","No transaction support — partial batch failures may leave collection in inconsistent state","SQLite backend limits concurrent write throughput compared to distributed Milvus"],"requires":["collection with schema already defined","data matching collection schema (vector dimension, field types)","primary key field for upsert/delete operations","pymilvus client library"],"input_types":["list of dicts (records with field names and values)","vector embeddings (numpy arrays or lists)","scalar metadata (strings, numbers, booleans)","primary key values (for delete/upsert)"],"output_types":["operation result (inserted_ids, deleted_count, upserted_count)","error messages for failed records","operation statistics (latency, throughput)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_7","uri":"capability://tool.use.integration.api.compatibility.layer.enabling.seamless.deployment.migration","name":"api compatibility layer enabling seamless deployment migration","description":"Milvus Lite implements identical Python API to Milvus Standalone and Distributed deployments, allowing the same code to run across all deployment types by changing only the connection URI. The MilvusClient class abstracts the connection details (local file path for Lite, HTTP endpoint for Standalone, gRPC for Distributed), while all collection, data, and search operations remain unchanged. This compatibility is achieved through a unified gRPC service layer that works identically whether the server is a subprocess or remote instance.","intents":["I want to prototype locally with Milvus Lite and deploy to production Milvus without code changes","I need to migrate from Milvus Lite to Milvus Standalone as my application scales","I want to test my application against multiple deployment types"],"best_for":["teams building applications that may scale from prototype to production","developers testing deployment strategies","organizations evaluating Milvus across different scales"],"limitations":["API compatibility does not extend to performance characteristics — Lite and Distributed have different latency/throughput profiles","Some advanced features (sharding, replication) are only available in Distributed, not Lite","Connection URI syntax differs between deployment types — must be updated for migration","No automatic data migration between deployment types — manual export/import required"],"requires":["pymilvus client library (same version across deployments)","connection URI (local path for Lite, HTTP/gRPC for others)","identical collection schema across deployments"],"input_types":["connection URI (string)","optional authentication token","collection and operation parameters"],"output_types":["MilvusClient instance","operation results (identical format across deployments)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_8","uri":"capability://search.retrieval.scalar.field.filtering.with.where.clause.expressions","name":"scalar field filtering with where clause expressions","description":"Milvus Lite supports scalar field filtering through WHERE clause expressions that are evaluated during search or query operations. The MilvusProxy layer parses filter expressions and applies them to scalar fields (int, float, string, bool) before or after vector search, depending on the query type. Filters support comparison operators (==, !=, <, >, <=, >=), logical operators (AND, OR, NOT), and range queries. Filtered results are returned with matching vectors and metadata, enabling precise data retrieval without separate post-processing.","intents":["I want to search vectors only from a specific category or time range","I need to filter results by scalar metadata (price, date, status) during search","I want to combine vector similarity with scalar constraints in a single query"],"best_for":["e-commerce search with category/price filtering","time-series data retrieval with date range constraints","multi-tenant applications filtering by user/organization ID"],"limitations":["Scalar filtering is applied post-search for approximate indexes (HNSW), reducing efficiency for highly selective filters","Filter expressions must be specified at query time — no pre-computed filtered indexes","Complex nested expressions may have performance overhead","String filtering is case-sensitive — no built-in case-insensitive matching","No full-text search within scalar fields — only exact/range matching"],"requires":["scalar fields defined in collection schema","WHERE clause expression using supported operators","field names matching collection schema","scalar data types (int, float, string, bool)"],"input_types":["WHERE clause expression (string or dict format)","comparison operators (==, !=, <, >, <=, >=)","logical operators (AND, OR, NOT)","scalar field values"],"output_types":["filtered result list matching both vector and scalar criteria","result count and pagination metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-milvus__cap_9","uri":"capability://data.processing.analysis.collection.level.statistics.and.metadata.retrieval","name":"collection-level statistics and metadata retrieval","description":"Milvus Lite provides collection statistics and metadata through the MilvusClient API, exposing information such as row count, memory usage, index status, and field definitions. The ServerManager and MilvusLocal components track collection metadata in SQLite, while the gRPC service layer exposes this information through describe_collection() and get_collection_stats() methods. Statistics are updated synchronously after data operations, providing real-time visibility into collection state without separate monitoring systems.","intents":["I want to check how many vectors are in a collection","I need to verify that an index was created successfully","I want to monitor memory usage and collection size"],"best_for":["developers debugging collection state during development","applications monitoring data ingestion progress","teams tracking collection growth over time"],"limitations":["Statistics are point-in-time snapshots, not historical — no time-series metrics","Memory usage estimates may not reflect actual SQLite file size","No built-in alerting for collection size thresholds","Statistics retrieval requires gRPC call — adds latency for frequent checks"],"requires":["collection already created","pymilvus client library","collection name"],"input_types":["collection name (string)"],"output_types":["collection metadata (name, schema, creation time)","row count (number of vectors)","index information (type, field, status)","field definitions (name, type, dimension)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Ubuntu 20.04+ (x86_64, ARM64) or macOS 11.0+ (Intel, Apple Silicon)","~50MB disk space for embedded milvus binary","pymilvus Python package","pymilvus client library","schema definition with FieldSchema objects","vector dimension specified in advance","field names and types declared before collection creation","pip or conda package manager","internet connection for binary download"],"failure_modes":["Single-process architecture limits horizontal scaling — not suitable for multi-user production workloads","SQLite backend has performance constraints compared to distributed Milvus deployments","Windows support not yet available (planned for future releases)","Subprocess management adds ~50-200ms startup latency on first connection","Schema is immutable after collection creation — cannot add/remove fields without recreating the collection","Vector dimension must be specified at schema definition time and cannot be changed","No automatic schema inference — explicit schema definition required","Sparse vector support requires manual field configuration","Precompiled binaries add ~50MB to package size","Custom compilation not supported — must use prebuilt binaries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.55,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:17.402Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-milvus","compare_url":"https://unfragile.ai/compare?artifact=pypi-milvus"}},"signature":"dTWJmUOJZrYJBes56r7hGEvspb926gihqF24uLrI1/c3QQ2eF7S87FQZiXaCAMDKMpwy6G9H1b7AoUuLH3dlAA==","signedAt":"2026-06-21T21:29:04.752Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-milvus","artifact":"https://unfragile.ai/pypi-milvus","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-milvus","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}