{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-pymilvus","slug":"pypi-pymilvus","name":"pymilvus","type":"repo","url":"https://pypi.org/project/pymilvus/","page_url":"https://unfragile.ai/pypi-pymilvus","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-pymilvus__cap_0","uri":"capability://memory.knowledge.vector.embedding.storage.and.retrieval","name":"vector-embedding-storage-and-retrieval","description":"Stores and retrieves high-dimensional vector embeddings using Milvus's distributed vector database backend, which implements HNSW (Hierarchical Navigable Small World) and IVF (Inverted File) indexing strategies. The SDK provides Python bindings that marshal numpy arrays and Python lists into Milvus's internal columnar storage format, enabling approximate nearest neighbor search across billions of vectors with configurable recall/latency tradeoffs.","intents":["Store embeddings from language models and retrieve semantically similar vectors at scale","Build semantic search systems that find similar documents, images, or concepts without keyword matching","Implement RAG pipelines that efficiently retrieve relevant context from large embedding collections","Scale vector similarity search from millions to billions of embeddings across distributed clusters"],"best_for":["ML engineers building semantic search and RAG systems","Teams deploying production vector databases with high throughput requirements","Developers migrating from single-machine vector stores (Faiss, Pinecone) to distributed infrastructure"],"limitations":["Requires running a separate Milvus server instance — no embedded/in-process mode like Faiss","Network latency overhead for each query compared to local vector libraries","Index building can be memory-intensive for very large collections (100M+ vectors); requires careful capacity planning","HNSW index doesn't support incremental updates efficiently — full rebuild may be needed for large deletes"],"requires":["Python 3.8+","Running Milvus server 2.0+ (Docker, Kubernetes, or standalone binary)","Network connectivity to Milvus instance (localhost or remote)","numpy or compatible array library for vector data"],"input_types":["numpy arrays (float32, float64)","Python lists of numbers","Batch vectors as 2D arrays"],"output_types":["List of vector IDs with similarity scores","Structured search results with metadata","Distance/similarity metrics (L2, IP, cosine)"],"categories":["memory-knowledge","vector-database"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_1","uri":"capability://memory.knowledge.metadata.filtering.with.vector.search","name":"metadata-filtering-with-vector-search","description":"Combines vector similarity search with scalar metadata filtering using Milvus's expression-based filtering system, which evaluates WHERE-like clauses on structured fields (strings, integers, timestamps) before or alongside vector search. The SDK translates Python filter expressions into Milvus's internal expression language, enabling hybrid queries that narrow vector search results by attributes without full table scans.","intents":["Filter vector search results by user ID, timestamp, category, or other metadata attributes","Implement multi-tenant vector search where each user only sees their own embeddings","Combine semantic similarity with business logic constraints (e.g., 'find similar products in stock')","Build faceted search experiences that combine vector relevance with categorical filtering"],"best_for":["E-commerce and content platforms combining semantic search with inventory/availability filters","Multi-tenant SaaS applications requiring data isolation at query time","Recommendation systems that filter by user preferences, time windows, or business rules"],"limitations":["Filter evaluation happens on Milvus server side — complex expressions may reduce query throughput","String matching is exact or prefix-based; no full-text search within metadata fields","Filtering on high-cardinality fields (millions of unique values) can degrade performance","No support for complex nested JSON filtering — metadata must be flattened into scalar columns"],"requires":["Python 3.8+","Milvus 2.0+","Metadata fields defined in collection schema before insertion"],"input_types":["Filter expressions as Python strings or dict-like objects","Scalar field values (int, float, string, bool, datetime)"],"output_types":["Filtered vector search results with IDs and scores","Metadata values for matched records"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_10","uri":"capability://data.processing.analysis.transaction.support.for.multi.step.operations","name":"transaction-support-for-multi-step-operations","description":"Provides transaction-like semantics for multi-step operations (insert, delete, search) within a single transaction context, ensuring atomicity and isolation. The SDK implements optimistic locking and timestamp-based isolation to prevent dirty reads and ensure consistency; transactions are scoped to collection level and automatically rolled back on error.","intents":["Ensure consistency when inserting vectors and metadata together","Prevent race conditions when updating vectors and associated metadata","Implement atomic multi-step operations without manual rollback logic","Guarantee isolation between concurrent transactions on same collection"],"best_for":["Applications with strict consistency requirements (financial, healthcare)","Multi-step data pipelines where partial failures must be rolled back","Teams implementing complex business logic that spans multiple Milvus operations"],"limitations":["Transactions are collection-scoped; cannot span multiple collections atomically","Transaction overhead adds latency; not suitable for latency-critical queries","Rollback is automatic on error but may not catch all consistency violations","No explicit transaction control (commit/rollback) — transactions are implicit per operation"],"requires":["Python 3.8+","Milvus 2.1+"],"input_types":["Collection name","Insert/delete/search operations within transaction context"],"output_types":["Transaction status (committed, rolled back)","Operation results if transaction succeeds"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_11","uri":"capability://memory.knowledge.time.travel.and.point.in.time.queries","name":"time-travel-and-point-in-time-queries","description":"Enables querying collections at specific points in time using timestamp-based snapshots, allowing retrieval of historical data state without maintaining separate collection versions. The SDK accepts timestamp parameters in search/get operations and transparently routes queries to appropriate snapshot; snapshots are automatically managed by Milvus and garbage-collected after retention period.","intents":["Query historical state of embeddings for debugging or auditing","Implement time-travel analytics to analyze how embeddings evolved","Recover from accidental data modifications by querying previous state","Support compliance requirements for data audit trails"],"best_for":["Compliance-heavy applications requiring audit trails and historical data access","Data science teams analyzing how embeddings and metadata evolved over time","Debugging applications where understanding historical state is critical"],"limitations":["Time-travel queries are slower than current-state queries due to snapshot overhead","Snapshots are retained for limited time (configurable, typically 24-48 hours); very old queries fail","Snapshot storage overhead scales with data change rate; high-churn collections consume more storage","No built-in time-series analytics; must manually query multiple timestamps and compare results"],"requires":["Python 3.8+","Milvus 2.1+","Timestamp in milliseconds since epoch"],"input_types":["Timestamp (milliseconds since epoch)","Collection name"],"output_types":["Search results or records as they existed at specified timestamp"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_12","uri":"capability://data.processing.analysis.bulk.delete.and.purge.operations","name":"bulk-delete-and-purge-operations","description":"Provides efficient bulk deletion of records by primary key or filter expression, with optional immediate purge to reclaim storage. The SDK implements soft-delete semantics (marking records as deleted without immediate storage reclamation) and hard-delete/purge operations that physically remove data and rebuild indexes; purge operations can be scheduled asynchronously.","intents":["Delete large numbers of records efficiently without individual delete calls","Implement data retention policies that automatically purge old records","Reclaim storage space from deleted records through index rebuilding","Support GDPR/privacy requirements by permanently deleting user data"],"best_for":["Applications with high data churn requiring efficient bulk deletion","Compliance-heavy systems implementing data retention and right-to-be-forgotten","Storage-constrained deployments where reclaiming space is critical"],"limitations":["Bulk delete by filter expression can be slow for large collections; may require full table scan","Purge operations are blocking and rebuild indexes; causes collection unavailability during purge","Soft-delete doesn't immediately reclaim storage; purge must be explicitly triggered","No transactional guarantees for bulk deletes; partial failures may leave inconsistent state"],"requires":["Python 3.8+","Milvus 2.0+"],"input_types":["List of primary keys to delete","Filter expression for conditional deletion"],"output_types":["Number of deleted records","Purge status and storage reclaimed"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_2","uri":"capability://data.processing.analysis.dynamic.schema.definition.and.evolution","name":"dynamic-schema-definition-and-evolution","description":"Allows defining collection schemas with typed fields (vectors, scalars, dynamic fields) and modifying them post-creation through add/drop field operations. The SDK provides a schema builder API that maps Python type hints to Milvus field types, handles schema versioning, and supports dynamic fields that accept arbitrary JSON-like data without pre-definition, enabling schema flexibility for evolving data models.","intents":["Define vector collections with mixed data types (embeddings + metadata) in a single schema","Add new metadata fields to existing collections without downtime or data migration","Store semi-structured data (JSON) alongside vectors for flexible attribute storage","Evolve application schemas as requirements change without recreating collections"],"best_for":["Startups and rapid-prototyping teams that need schema flexibility","Applications with evolving data models that can't afford schema migrations","Multi-tenant systems where different tenants have different metadata requirements"],"limitations":["Field deletion is not supported — only addition of new fields","Dynamic fields incur slight performance overhead compared to pre-defined scalar fields","Schema changes require collection to be flushed; may cause brief query latency spikes","No automatic schema migration tools — developers must manage versioning manually"],"requires":["Python 3.8+","Milvus 2.1+ for dynamic field support","Understanding of Milvus field types (FloatVector, Int64, VarChar, etc.)"],"input_types":["Python dataclasses or dict definitions","Type hints (int, str, float, list)","JSON-serializable objects for dynamic fields"],"output_types":["Collection schema objects","Field metadata and constraints"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_3","uri":"capability://data.processing.analysis.batch.insert.and.upsert.operations","name":"batch-insert-and-upsert-operations","description":"Provides high-throughput bulk data loading through batch insert/upsert operations that accumulate records in memory and flush to Milvus in optimized chunks. The SDK implements client-side buffering with configurable batch sizes, automatic flush triggers based on record count or time intervals, and transaction-like semantics for upsert (insert-or-update) operations that deduplicate by primary key.","intents":["Load millions of embeddings and metadata into Milvus efficiently without individual insert calls","Update existing vectors and metadata by primary key without deleting and re-inserting","Implement incremental data pipelines that batch embeddings from upstream sources","Achieve throughput of 100K+ records/second for bulk embedding ingestion"],"best_for":["Data engineers building ETL pipelines that feed embeddings into Milvus","ML teams doing periodic bulk re-indexing of embedding collections","Applications with high-volume embedding generation (e.g., real-time document indexing)"],"limitations":["Batch operations are not transactional — partial failures may leave collection in inconsistent state","Memory overhead scales with batch size; very large batches (10M+ records) may cause OOM on client","Upsert performance degrades if primary key cardinality is very high (billions of unique keys)","No built-in deduplication across multiple batch calls — application must manage idempotency"],"requires":["Python 3.8+","Milvus 2.0+","Sufficient client-side memory for batch buffering (typically 100MB-1GB per batch)"],"input_types":["List of dicts with vector and metadata fields","numpy arrays for vector data","Pandas DataFrames (via conversion)"],"output_types":["List of inserted/upserted record IDs","Insertion statistics (count, time)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_4","uri":"capability://data.processing.analysis.distributed.collection.partitioning","name":"distributed-collection-partitioning","description":"Partitions large collections into logical subsets based on partition key fields, enabling parallel search and insert operations across partitions. The SDK abstracts partition management, allowing queries to target specific partitions or search across all partitions transparently; partitions are distributed across Milvus cluster nodes for horizontal scalability.","intents":["Scale vector search across multiple partitions to parallelize query execution","Isolate data by tenant, time period, or category for faster targeted searches","Implement time-series vector data where old partitions can be archived or deleted","Distribute write load across partitions to avoid hotspots on single partition"],"best_for":["Large-scale deployments with billions of vectors requiring horizontal scaling","Multi-tenant systems where data isolation by partition improves query performance","Time-series embedding systems where data is naturally partitioned by date/time"],"limitations":["Partition key must be chosen at collection creation time; cannot be changed later","Cross-partition queries incur coordination overhead; single-partition queries are faster","Uneven partition distribution (skewed partition keys) can cause performance imbalance","Partition pruning requires explicit partition key in query filter; optimizer cannot infer pruning from other predicates"],"requires":["Python 3.8+","Milvus 2.0+ with cluster deployment","Partition key field defined in collection schema"],"input_types":["Partition key values (string, int) during insert","Partition names or keys in search filters"],"output_types":["Search results from specified partitions","Partition statistics (record count, size)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_5","uri":"capability://data.processing.analysis.index.management.and.configuration","name":"index-management-and-configuration","description":"Provides fine-grained control over vector index types (HNSW, IVF_FLAT, IVF_SQ8, SCANN) and index parameters (M, efConstruction, nlist, nprobe) through a configuration API. The SDK allows building multiple indexes on the same collection, selecting indexes at query time, and monitoring index build progress; index parameters directly trade off memory usage, build time, and query latency.","intents":["Optimize index parameters for specific latency/recall/memory requirements","Build multiple indexes with different parameters for A/B testing query performance","Monitor index build progress and manage index lifecycle (rebuild, drop)","Select appropriate index type for collection size and query patterns (HNSW for small, IVF for large)"],"best_for":["Performance engineers tuning vector search latency and recall","Teams with heterogeneous query patterns requiring multiple index strategies","Large-scale deployments where index parameter tuning significantly impacts cost"],"limitations":["Index building is blocking — collection is read-only during index creation","Changing index parameters requires rebuilding entire index; no incremental index updates","Index memory overhead can be 2-10x the raw vector data size depending on index type","No automatic index selection — developers must manually choose index type and parameters"],"requires":["Python 3.8+","Milvus 2.0+","Understanding of vector index trade-offs (HNSW vs IVF vs SCANN)"],"input_types":["Index type name (string)","Index parameters as dict (M, efConstruction, nlist, nprobe, etc.)"],"output_types":["Index metadata and statistics","Index build progress and status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_6","uri":"capability://tool.use.integration.asynchronous.query.and.insert.operations","name":"asynchronous-query-and-insert-operations","description":"Provides async/await API for non-blocking vector search, insert, and delete operations using Python's asyncio framework. The SDK implements async client that maintains connection pools and handles concurrent requests without blocking the event loop, enabling high-concurrency applications to issue thousands of queries in parallel.","intents":["Build high-concurrency web services that handle multiple vector search requests simultaneously","Implement async data pipelines that insert embeddings without blocking downstream processing","Parallelize multiple vector searches across different collections or partitions","Integrate Milvus into async frameworks (FastAPI, aiohttp) without thread pool overhead"],"best_for":["Web service developers building async APIs with FastAPI or similar frameworks","Data pipeline engineers implementing concurrent embedding ingestion","Teams optimizing for high request concurrency with minimal thread overhead"],"limitations":["Async API is separate from sync API — cannot mix sync/async calls in same client instance","Connection pool size must be tuned for expected concurrency; too small causes queuing, too large wastes resources","Error handling in async context requires careful exception management; unhandled exceptions in tasks may be silently dropped","Debugging async code is harder than sync; stack traces are less informative"],"requires":["Python 3.8+","asyncio event loop running","Milvus 2.0+"],"input_types":["Same as sync API (vectors, metadata, filters)"],"output_types":["Coroutines that resolve to search results or insert confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_7","uri":"capability://automation.workflow.connection.pooling.and.cluster.failover","name":"connection-pooling-and-cluster-failover","description":"Manages persistent connections to Milvus cluster nodes with automatic connection pooling, health checking, and failover to replica nodes. The SDK maintains a pool of connections per node, detects node failures through heartbeat mechanisms, and automatically routes requests to healthy replicas without application intervention.","intents":["Maintain stable connections to Milvus cluster across network interruptions","Automatically failover to replica nodes when primary node becomes unavailable","Optimize connection reuse to reduce connection establishment overhead","Monitor cluster health and detect node failures proactively"],"best_for":["Production deployments requiring high availability and fault tolerance","Teams running Milvus on Kubernetes with replica nodes","Applications with strict uptime requirements where manual failover is unacceptable"],"limitations":["Failover is not instantaneous — brief request failures may occur during node detection and rerouting","Connection pool size must be tuned; too large causes resource waste, too small causes connection starvation","Health checks add latency overhead; check frequency must balance responsiveness vs network load","No automatic load balancing across healthy nodes — all requests go to primary until it fails"],"requires":["Python 3.8+","Milvus 2.0+ cluster with replica nodes","Network connectivity to all cluster nodes"],"input_types":["Cluster connection string with multiple node addresses"],"output_types":["Connection status and health metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_8","uri":"capability://automation.workflow.collection.backup.and.restore","name":"collection-backup-and-restore","description":"Provides APIs to create point-in-time backups of collections and restore them to recover from data loss or corruption. The SDK abstracts backup/restore operations that copy collection data to external storage (S3, GCS, local filesystem) and can restore to same or different Milvus cluster; backups are incremental and can be scheduled periodically.","intents":["Create regular backups of vector collections for disaster recovery","Restore collections to previous state after accidental data deletion or corruption","Migrate collections between Milvus clusters using backup/restore","Implement compliance requirements for data retention and recovery"],"best_for":["Production deployments with strict data durability requirements","Teams managing multiple Milvus clusters and needing cross-cluster data migration","Compliance-heavy industries requiring audit trails and recovery capabilities"],"limitations":["Backup/restore operations are slow for very large collections (100GB+); can take hours","Requires external storage (S3, GCS) for backups; local filesystem backups don't scale","No incremental restore — full collection must be restored even if only partial data is needed","Backup consistency depends on collection flush state; unflushed data may be lost"],"requires":["Python 3.8+","Milvus 2.1+","External storage credentials (S3, GCS, etc.) or local filesystem access"],"input_types":["Collection name","Backup destination (S3 path, GCS bucket, local path)"],"output_types":["Backup metadata and status","Restore confirmation and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pymilvus__cap_9","uri":"capability://automation.workflow.collection.statistics.and.monitoring","name":"collection-statistics-and-monitoring","description":"Exposes collection-level metrics (record count, memory usage, index size, query latency) and provides hooks for custom monitoring integration. The SDK retrieves statistics from Milvus server and can export metrics to monitoring systems (Prometheus, Datadog) through standard metric formats; statistics are updated asynchronously to avoid query latency impact.","intents":["Monitor collection size and growth to plan capacity and storage","Track query latency and throughput to identify performance bottlenecks","Alert on collection health metrics (e.g., index build failures, memory pressure)","Export metrics to observability platforms for dashboarding and alerting"],"best_for":["DevOps and SRE teams managing production Milvus deployments","Data engineers monitoring ETL pipelines that feed Milvus","Teams implementing observability and alerting for vector search infrastructure"],"limitations":["Statistics are point-in-time snapshots; no built-in time-series history","Retrieving statistics requires server round-trip; frequent polling can impact performance","No built-in alerting — must integrate with external monitoring system for alerts","Metrics granularity is collection-level; no per-partition or per-index metrics"],"requires":["Python 3.8+","Milvus 2.0+"],"input_types":["Collection name"],"output_types":["Collection statistics dict (record count, memory, index size, etc.)","Metrics in Prometheus format (optional)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Running Milvus server 2.0+ (Docker, Kubernetes, or standalone binary)","Network connectivity to Milvus instance (localhost or remote)","numpy or compatible array library for vector data","Milvus 2.0+","Metadata fields defined in collection schema before insertion","Milvus 2.1+","Timestamp in milliseconds since epoch","Milvus 2.1+ for dynamic field support","Understanding of Milvus field types (FloatVector, Int64, VarChar, etc.)"],"failure_modes":["Requires running a separate Milvus server instance — no embedded/in-process mode like Faiss","Network latency overhead for each query compared to local vector libraries","Index building can be memory-intensive for very large collections (100M+ vectors); requires careful capacity planning","HNSW index doesn't support incremental updates efficiently — full rebuild may be needed for large deletes","Filter evaluation happens on Milvus server side — complex expressions may reduce query throughput","String matching is exact or prefix-based; no full-text search within metadata fields","Filtering on high-cardinality fields (millions of unique values) can degrade performance","No support for complex nested JSON filtering — metadata must be flattened into scalar columns","Transactions are collection-scoped; cannot span multiple collections atomically","Transaction overhead adds latency; not suitable for latency-critical queries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","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-pymilvus","compare_url":"https://unfragile.ai/compare?artifact=pypi-pymilvus"}},"signature":"UspvPWHl+AItlGarQaHfm4Xnm2bWyVIj2uDKOxOHUgvZHRswIsMRzUz9NBqvnWBq/3ACcqGM4UqPRaOMTMoABQ==","signedAt":"2026-06-21T01:03:02.012Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-pymilvus","artifact":"https://unfragile.ai/pypi-pymilvus","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-pymilvus","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"}}