{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-chroma-core--chroma","slug":"chroma-core--chroma","name":"chroma","type":"mcp","url":"https://www.trychroma.com/","page_url":"https://unfragile.ai/chroma-core--chroma","categories":["mcp-servers","rag-knowledge"],"tags":["agents","ai","ai-agents","database","rust","rust-lang"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-chroma-core--chroma__cap_0","uri":"capability://memory.knowledge.multi.deployment.vector.database.with.embedded.to.distributed.scaling","name":"multi-deployment vector database with embedded-to-distributed scaling","description":"Chroma provides a unified API across three deployment modes (embedded SQLite, single-node FastAPI server, and Kubernetes-distributed) using a client factory pattern that abstracts underlying storage and compute layers. The architecture uses a Rust frontend service for performance-critical operations and Python FastAPI for HTTP access, with a gRPC-based log service for distributed coordination. This allows developers to start with in-process SQLite and scale to multi-node clusters without changing application code.","intents":["I want to prototype a RAG system locally without external dependencies, then deploy it to production Kubernetes","I need a vector database that works in-process for testing but scales horizontally for production workloads","I want to avoid vendor lock-in by using an open-source vector store with flexible deployment options"],"best_for":["teams building LLM applications who need flexible deployment from development to production","developers prototyping RAG systems and wanting to avoid infrastructure setup initially","organizations migrating from single-node to distributed vector search without application rewrites"],"limitations":["Embedded mode uses SQLite which is not optimized for concurrent writes — suitable for read-heavy or single-writer workloads only","Distributed Kubernetes deployment requires external PostgreSQL for SysDB (system database) coordination, adding operational complexity","No built-in multi-region replication — requires external tooling for geo-distributed deployments","Python client adds ~50-200ms latency per request in server mode due to HTTP/gRPC serialization overhead"],"requires":["Python 3.8+ for Python client library","Node.js 14+ for JavaScript client (optional)","Rust toolchain if building from source","PostgreSQL 12+ for distributed SysDB (only in Kubernetes mode)","Kubernetes 1.20+ for distributed deployment"],"input_types":["embeddings (float32/float64 vectors)","documents (text strings)","metadata (JSON objects with string/int/float/bool values)","collection schemas (field definitions)"],"output_types":["query results (ranked documents with similarity scores)","collection metadata","operation confirmations (add/delete/update status)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_1","uri":"capability://search.retrieval.semantic.similarity.search.with.hnsw.indexing.and.knn.query.execution","name":"semantic similarity search with hnsw indexing and knn query execution","description":"Chroma implements approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs built in Rust, with a query execution pipeline that fetches candidate records from the log service, applies metadata filters via a query expression system, and ranks results by cosine/L2 distance. The knn_hnsw operator in the worker service performs graph traversal with configurable ef (exploration factor) parameters for accuracy/latency trade-offs. Results are merged across multiple segments and returned with similarity scores.","intents":["I want to find semantically similar documents to a query embedding in milliseconds","I need to filter vector search results by metadata (e.g., 'source == pdf' AND 'date > 2024-01-01') before ranking","I want to tune search accuracy vs latency by adjusting HNSW parameters"],"best_for":["RAG applications requiring sub-100ms semantic search over millions of embeddings","recommendation systems filtering by user/item metadata before similarity ranking","developers building chatbots that need context retrieval with temporal or categorical constraints"],"limitations":["HNSW index is built in-memory and not persisted to disk — requires full rebuild on restart unless using S3 blockstore","Metadata filtering is applied post-ranking (after kNN), not pre-filtering, so large result sets with selective filters are inefficient","No support for hybrid search combining keyword matching with semantic similarity in a single query","HNSW construction is single-threaded in current implementation — index building scales linearly with vector count"],"requires":["embeddings as float32 or float64 vectors (typically 384-1536 dimensions)","Rust worker service running for distributed deployments","S3 or local filesystem for persistent blockstore (optional but recommended for production)"],"input_types":["query_embeddings (float32/float64 vector)","n_results (integer, number of neighbors to return)","where (metadata filter expression using query expression DSL)","where_document (text filter for document content)"],"output_types":["ids (list of document identifiers)","distances (list of similarity scores)","documents (optional, list of document texts)","metadatas (optional, list of metadata objects)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_10","uri":"capability://memory.knowledge.system.database.sysdb.for.metadata.management.with.sqlite.and.postgresql.backends","name":"system database (sysdb) for metadata management with sqlite and postgresql backends","description":"Chroma uses a system database (SysDB) to store metadata about collections, tenants, databases, and version history. The SysDB supports two backends: SQLite for embedded/single-node deployments and PostgreSQL for distributed Kubernetes deployments. The SysDB schema tracks collection ownership, segment references, version pointers, and compaction state. In distributed mode, a Go coordinator service manages SysDB access and ensures consistency across worker nodes. The SysDB is queried during collection creation, deletion, and version management operations.","intents":["I want to track which collections exist and their metadata without querying the vector index","I need to manage collection versions and support rollback to previous states","I want to enforce multi-tenancy by storing tenant-to-collection mappings"],"best_for":["multi-tenant deployments requiring collection isolation and ownership tracking","systems managing large numbers of collections (>1000) with version history","teams needing audit trails of collection creation/deletion/modification"],"limitations":["SysDB is a separate system from the vector index — metadata and vectors can become inconsistent if not carefully managed","SQLite backend is not suitable for concurrent writes — embedded deployments with multiple writers may experience lock contention","PostgreSQL backend adds operational complexity and requires external database management","Version history is not garbage-collected automatically — old versions accumulate and must be manually cleaned up"],"requires":["SQLite (embedded in Python client, no external dependency)","PostgreSQL 12+ (for distributed deployments)","Go coordinator service (for distributed SysDB access)"],"input_types":["collection metadata (name, id, tenant_id, created_at)","version information (segment references, compaction state)","tenant information (tenant_id, owner, created_at)"],"output_types":["collection list (names, ids, metadata)","version history (list of previous versions with timestamps)","tenant information"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_11","uri":"capability://automation.workflow.compaction.and.garbage.collection.with.segment.merging.and.hnsw.index.construction","name":"compaction and garbage collection with segment merging and hnsw index construction","description":"Chroma's compaction service (rust/worker/src/compactor/) periodically consolidates log entries into immutable Arrow-formatted segments and constructs HNSW indices for efficient similarity search. The compaction workflow is triggered when log size exceeds a threshold or on a schedule, and it merges multiple segments into a single larger segment while deduplicating records and removing deleted entries. HNSW index construction is single-threaded and CPU-intensive, taking O(n log n) time for n vectors. The garbage collection service removes unreferenced segments and log entries after compaction completes. Compaction is asynchronous and may cause temporary query latency spikes.","intents":["I want to automatically consolidate log entries into indexed segments without manual intervention","I need to reclaim disk space by removing deleted documents and old log entries","I want to tune compaction frequency to balance write latency and query performance"],"best_for":["long-running RAG systems with continuous data ingestion requiring background maintenance","applications with high delete/update rates needing garbage collection","teams wanting to avoid manual index rebuilds"],"limitations":["Compaction is CPU-intensive and may cause query latency spikes during index construction","Single-threaded HNSW construction limits compaction throughput — large segments (>10M vectors) may take hours to compact","Compaction scheduling is not adaptive — fixed thresholds may not suit all workloads","No built-in monitoring of compaction progress — long-running compactions may fail silently"],"requires":["compaction service running (Rust worker service)","blockstore configured for segment storage","log service for reading log entries"],"input_types":["compaction trigger (log size threshold, time-based schedule)","segment list (segments to merge)"],"output_types":["compacted segment (Arrow-formatted with HNSW index)","garbage collection status (segments removed, disk space freed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_12","uri":"capability://automation.workflow.kubernetes.native.distributed.deployment.with.multi.node.scaling","name":"kubernetes-native distributed deployment with multi-node scaling","description":"Chroma supports Kubernetes deployment via Helm charts and Docker images, with separate services for frontend (gRPC), worker (query execution), and log service (write coordination). The deployment uses a PostgreSQL SysDB for metadata consistency, a shared blockstore (S3) for segment storage, and a log service for write ordering. Kubernetes manifests define resource requests/limits, health checks, and service discovery, enabling automatic scaling via Horizontal Pod Autoscaler (HPA). The architecture is stateless at the frontend/worker level, allowing pods to be added/removed without data loss.","intents":["I want to deploy Chroma on Kubernetes with automatic scaling based on query load","I need high availability with multiple replicas of each service component","I want to manage Chroma infrastructure using Kubernetes-native tools (Helm, kubectl)"],"best_for":["cloud-native teams already using Kubernetes for other services","organizations needing high availability and automatic failover","deployments requiring horizontal scaling to handle variable query load"],"limitations":["Kubernetes deployment requires external PostgreSQL, S3, and log service — adds operational complexity","Network latency between services (frontend, worker, log service) adds 10-50ms per query compared to single-node","Helm charts are community-maintained and may lag behind latest Chroma releases","No built-in multi-region replication — requires external tools (Velero, Kasten) for disaster recovery"],"requires":["Kubernetes cluster 1.20+ with sufficient resources (CPU, memory, storage)","Helm 3+ for deploying charts","PostgreSQL 12+ for SysDB","S3-compatible storage (AWS S3, MinIO, etc.)","Docker images for Chroma services"],"input_types":["Helm values (configuration for services, replicas, resources)","Kubernetes manifests (deployments, services, configmaps)"],"output_types":["deployed services (frontend, worker, log service)","Kubernetes resources (pods, services, persistent volumes)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_2","uri":"capability://data.processing.analysis.metadata.filtering.with.query.expression.dsl.and.type.safe.schema.validation","name":"metadata filtering with query expression dsl and type-safe schema validation","description":"Chroma implements a query expression system (where clauses) that supports logical operators ($and, $or, $not) and comparison operators ($eq, $ne, $gt, $gte, $lt, $lte, $in) on typed metadata fields (string, int, float, bool). The system validates filter expressions against collection schemas defined at creation time, catching type mismatches before query execution. Filters are compiled into predicates evaluated during the query execution pipeline, applied after kNN retrieval but before result ranking.","intents":["I want to search vectors only from documents with specific metadata (e.g., source='arxiv' AND year >= 2023)","I need to ensure metadata filters are type-safe and catch schema violations at query time","I want to combine multiple filter conditions with AND/OR/NOT logic without writing SQL"],"best_for":["multi-tenant RAG systems filtering by user_id or organization_id","document retrieval systems with temporal, categorical, or hierarchical metadata constraints","developers building search interfaces with faceted filtering (source, date range, category)"],"limitations":["Filters are applied post-kNN (after similarity ranking), not pre-filtering, so queries with highly selective filters may retrieve many irrelevant vectors before filtering","No support for full-text search or fuzzy matching on string metadata — only exact equality/inequality","Complex nested queries with deep AND/OR nesting may have unpredictable performance due to lack of query optimization","Metadata field types are fixed at collection creation time — cannot add new field types to existing collections without migration"],"requires":["collection schema defined with field names and types (string, int, float, bool)","metadata objects matching schema types when adding documents","query expression syntax conforming to Chroma's DSL (JSON-like format)"],"input_types":["where (dict/JSON with $and/$or/$not/$eq/$ne/$gt/$gte/$lt/$lte/$in operators)","where_document (string filter for document content using $contains or $not_contains)"],"output_types":["filtered_ids (document IDs matching both kNN and metadata filters)","filtered_metadatas (metadata objects for matching documents)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_3","uri":"capability://memory.knowledge.multi.tenant.collection.management.with.version.control.and.forking","name":"multi-tenant collection management with version control and forking","description":"Chroma supports creating isolated collections within a database, each with independent schemas, embeddings, and metadata. Collections are versioned using a segment-based architecture where each write operation creates a new log entry, and compaction consolidates segments into immutable snapshots. The system supports collection forking (creating a copy at a specific version) without duplicating underlying data through copy-on-write semantics. The SysDB (system database) tracks collection metadata, ownership, and version history using SQLite (embedded) or PostgreSQL (distributed).","intents":["I want to isolate vector data for different users/organizations in a single Chroma instance","I need to version my embeddings and roll back to a previous state if reindexing fails","I want to fork a collection to experiment with different embeddings without affecting the original"],"best_for":["SaaS platforms serving multiple customers with isolated vector stores","ML teams experimenting with different embedding models and needing version rollback","organizations managing multiple RAG applications with shared infrastructure"],"limitations":["Collection isolation is logical, not cryptographic — no built-in access control between collections in the same instance","Forking creates a new collection with independent segments, so large collections (>1GB) take time to fork due to segment copying","Version history is not garbage-collected by default — old segments accumulate on disk unless manual cleanup is performed","No built-in multi-version concurrency control (MVCC) — concurrent writes to the same collection may cause conflicts"],"requires":["collection name (string, unique within database)","optional metadata dict for collection-level properties","SysDB access (SQLite for embedded, PostgreSQL for distributed)"],"input_types":["collection_name (string)","metadata (dict with collection-level properties)","get_or_create (boolean, create if not exists)"],"output_types":["collection object (with add/query/delete/update methods)","collection metadata (name, id, version, creation timestamp)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_4","uri":"capability://automation.workflow.asynchronous.batch.operations.with.log.based.write.path.and.compaction","name":"asynchronous batch operations with log-based write path and compaction","description":"Chroma implements a write-ahead log (WAL) architecture where add/update/delete operations are appended to an immutable log service (gRPC-based in distributed mode, in-memory in embedded mode) before being applied to the in-memory index. A background compaction service periodically consolidates log entries into immutable Arrow-formatted segments stored in the blockstore (S3 or local filesystem). This design decouples write latency from indexing latency and enables efficient batch operations. The log service guarantees ordering and durability, while the compaction workflow handles segment merging and HNSW index construction.","intents":["I want to add millions of embeddings efficiently without blocking on index construction","I need to ensure writes are durable and can be replayed if the system crashes","I want to batch delete/update operations and have them reflected in search results within seconds"],"best_for":["bulk indexing pipelines ingesting large document corpora into RAG systems","real-time systems requiring sub-second write latency with eventual consistency","applications needing write durability guarantees without synchronous index updates"],"limitations":["Compaction is asynchronous and may lag behind writes by seconds to minutes, so newly added documents may not appear in search results immediately","Log service in distributed mode requires external gRPC infrastructure and adds network latency (~10-50ms per write)","Compaction is CPU-intensive (HNSW index construction is single-threaded) and may cause query latency spikes during compaction windows","No built-in write conflict resolution — concurrent updates to the same document may result in undefined behavior"],"requires":["log service running (in-process for embedded, gRPC service for distributed)","blockstore configured (local filesystem or S3 with credentials)","compaction service running in background"],"input_types":["ids (list of document identifiers)","embeddings (list of float32/float64 vectors)","documents (optional, list of text strings)","metadatas (optional, list of metadata dicts)"],"output_types":["operation status (success/failure per document)","collection state (updated document count, segment count)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_5","uri":"capability://search.retrieval.distributed.query.execution.with.segment.based.parallelism.and.result.merging","name":"distributed query execution with segment-based parallelism and result merging","description":"Chroma's query execution pipeline (in rust/worker/src/execution/operators/) processes queries across multiple segments in parallel, with each segment performing independent kNN search using its HNSW index. A knn_merge operator combines results from all segments using a priority queue, deduplicating results and ranking by similarity score. The execution is orchestrated by a DAG-based operator system where fetch_log retrieves candidate records, knn_hnsw performs similarity search, and metadata filters are applied before final result ranking. This architecture enables horizontal scaling by adding more segments without query latency degradation.","intents":["I want to query across millions of vectors and get results in <100ms","I need to parallelize search across multiple segments without managing coordination myself","I want to scale query throughput by adding more worker nodes"],"best_for":["high-throughput RAG systems serving thousands of concurrent queries","large-scale semantic search applications with >100M vectors","teams building distributed LLM inference pipelines requiring fast context retrieval"],"limitations":["Segment merging during compaction may cause temporary query latency spikes as HNSW indices are rebuilt","No query result caching — identical queries executed twice will both traverse HNSW graphs, wasting CPU","Distributed query execution requires network round-trips to worker nodes, adding 10-50ms latency per query in Kubernetes deployments","Result merging across segments is O(k * num_segments) where k is the number of results requested, so very large k values may cause memory overhead"],"requires":["worker service running (Rust-based, one or more instances)","log service for fetching candidate records","blockstore for accessing segment data"],"input_types":["query_embeddings (float32/float64 vector)","n_results (integer, typically 1-1000)","where (metadata filter expression)","include (list of fields to return: 'documents', 'metadatas', 'distances', 'embeddings')"],"output_types":["ids (list of document IDs, ranked by similarity)","distances (list of similarity scores)","documents (optional, list of document texts)","metadatas (optional, list of metadata objects)","embeddings (optional, list of query result vectors)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_6","uri":"capability://memory.knowledge.persistent.storage.abstraction.with.s3.and.local.blockstore.backends","name":"persistent storage abstraction with s3 and local blockstore backends","description":"Chroma abstracts storage through a blockstore interface supporting both S3 (with admission control for rate limiting) and local filesystem backends. Compacted segments are serialized to Arrow format and stored as immutable blocks, with a block cache layer providing in-memory caching of frequently accessed blocks. The storage layer is decoupled from the query execution layer, allowing segments to be fetched on-demand from S3 without loading entire collections into memory. The admission control mechanism prevents S3 request throttling by queuing requests and enforcing rate limits.","intents":["I want to store vector data in S3 for durability without keeping everything in memory","I need to avoid S3 throttling errors when querying large collections with many concurrent requests","I want to use local filesystem for development and S3 for production without code changes"],"best_for":["cloud-native deployments on AWS/GCP/Azure with S3-compatible storage","cost-sensitive applications that want to minimize memory usage by caching only hot segments","teams managing large collections (>100GB) that don't fit in memory"],"limitations":["S3 latency (100-500ms per block fetch) is much higher than local SSD access (1-10ms), so queries on cold segments may take seconds","Block cache is in-memory and not shared across worker nodes, so cache hits are limited to single-node deployments","Admission control adds queuing latency during high-concurrency periods — requests may wait seconds before S3 fetch begins","No built-in data compression — Arrow segments are stored uncompressed, increasing S3 storage costs and transfer time"],"requires":["S3 bucket with appropriate IAM permissions (for S3 backend)","AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) or IAM role","local filesystem with sufficient disk space (for local backend)","blockstore configuration in chroma_config.yaml"],"input_types":["block_id (string identifier for segment block)","block_data (Arrow-serialized segment bytes)"],"output_types":["block_data (Arrow-serialized segment bytes from cache or storage)","cache_hit (boolean indicating if block was served from cache)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_7","uri":"capability://tool.use.integration.python.and.javascript.client.libraries.with.synchronous.and.asynchronous.apis","name":"python and javascript client libraries with synchronous and asynchronous apis","description":"Chroma provides language-specific client libraries (Python and JavaScript) that abstract the underlying deployment mode (embedded, HTTP server, or Rust service). The Python client uses a factory pattern (chromadb.Client()) to instantiate the appropriate backend based on configuration, supporting both synchronous (blocking) and asynchronous (async/await) APIs. The JavaScript client provides similar abstractions for Node.js and browser environments. Both clients handle serialization/deserialization of embeddings, metadata, and query results, and provide type hints (Python) or TypeScript definitions (JavaScript) for IDE support.","intents":["I want to use Chroma in Python with type hints and IDE autocompletion","I need async/await support for non-blocking I/O in my Python application","I want to build a JavaScript/Node.js application that uses Chroma without managing HTTP clients"],"best_for":["Python developers building LLM applications with LangChain or LlamaIndex integration","Node.js/JavaScript developers building full-stack RAG applications","teams wanting language-native APIs without writing HTTP client boilerplate"],"limitations":["Python async API (chromadb.api.async_client) is less mature than synchronous API and may have edge cases","JavaScript client is Node.js-only; browser support requires a separate HTTP proxy","Type hints in Python are incomplete for complex nested types (e.g., metadata dicts), limiting IDE autocompletion","Client libraries add ~50-200ms latency per request in server mode due to HTTP serialization and network round-trips"],"requires":["Python 3.8+ (for Python client)","Node.js 14+ (for JavaScript client)","chromadb package installed (pip install chromadb or npm install chromadb)"],"input_types":["collection operations (add, query, delete, update, get)","embedding vectors (float32/float64 lists)","metadata dicts (string/int/float/bool values)","query expressions (where clauses)"],"output_types":["collection objects with methods for CRUD operations","query results (ids, distances, documents, metadatas)","operation confirmations"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_8","uri":"capability://safety.moderation.authentication.and.rate.limiting.for.multi.tenant.deployments","name":"authentication and rate limiting for multi-tenant deployments","description":"Chroma's FastAPI server layer implements authentication via API keys and rate limiting via token bucket algorithms to enforce per-user/per-tenant quotas. The authentication middleware validates API keys against a configured key store (in-memory or external), and the rate limiter tracks request counts per key and enforces configurable limits (requests per second, queries per minute, etc.). These mechanisms are applied at the HTTP layer before requests reach the core query execution pipeline, protecting against abuse and ensuring fair resource allocation in shared deployments.","intents":["I want to require API keys for access to my Chroma instance","I need to rate-limit queries per user to prevent resource exhaustion","I want to track usage per tenant for billing purposes"],"best_for":["SaaS platforms serving multiple customers with shared Chroma infrastructure","teams deploying Chroma as a managed service requiring access control","applications needing usage-based billing or fair-share resource allocation"],"limitations":["Authentication is API-key-only; no support for OAuth2, JWT, or SAML","Rate limiting is per-key, not per-user or per-tenant — requires external identity mapping","No built-in audit logging — API key usage is not logged for compliance purposes","Rate limit state is in-memory and not shared across multiple server instances, so distributed deployments may have inconsistent enforcement"],"requires":["FastAPI server deployment (not available in embedded mode)","API keys configured in environment or configuration file","rate limit configuration (requests per second, burst size)"],"input_types":["api_key (string, passed in HTTP Authorization header)","request (HTTP request to any Chroma endpoint)"],"output_types":["authentication result (success/failure)","rate limit status (remaining quota, reset time)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chroma-core--chroma__cap_9","uri":"capability://tool.use.integration.rust.based.frontend.service.with.grpc.api.for.high.performance.access","name":"rust-based frontend service with grpc api for high-performance access","description":"Chroma provides a Rust frontend service (rust/frontend/src/) that implements the core Chroma API via gRPC, offering lower latency and higher throughput than the Python FastAPI server. The Rust frontend handles collection management, query routing, and result serialization, delegating compute-intensive operations (kNN search, compaction) to worker services. The gRPC API uses Protocol Buffers for efficient serialization and supports streaming responses for large result sets. This service is the default backend for distributed Kubernetes deployments and can be used directly by Rust clients or via gRPC proxies.","intents":["I want to minimize latency for vector search queries by using a compiled language backend","I need to handle high query throughput (>10k queries/second) without Python GIL contention","I want to build a Rust application that directly calls Chroma's gRPC API"],"best_for":["high-performance RAG systems requiring <50ms query latency","teams building Rust-based LLM applications needing native vector search","deployments requiring >10k queries/second throughput"],"limitations":["Rust frontend requires separate deployment and operational overhead compared to embedded mode","gRPC API has higher complexity than HTTP REST — requires Protocol Buffer knowledge","No built-in gRPC load balancing — requires external load balancer (Envoy, nginx) for multi-instance deployments","Rust client library is less mature than Python client and has fewer integrations with LLM frameworks"],"requires":["Rust toolchain for building from source","gRPC client library (tonic for Rust, grpcio for Python, etc.)","Protocol Buffer compiler (protoc) for code generation"],"input_types":["gRPC messages (Protocol Buffer format)","collection operations (add, query, delete, update)","embedding vectors and metadata"],"output_types":["gRPC response messages","query results (ids, distances, documents, metadatas)","streaming responses for large result sets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ for Python client library","Node.js 14+ for JavaScript client (optional)","Rust toolchain if building from source","PostgreSQL 12+ for distributed SysDB (only in Kubernetes mode)","Kubernetes 1.20+ for distributed deployment","embeddings as float32 or float64 vectors (typically 384-1536 dimensions)","Rust worker service running for distributed deployments","S3 or local filesystem for persistent blockstore (optional but recommended for production)","SQLite (embedded in Python client, no external dependency)","PostgreSQL 12+ (for distributed deployments)"],"failure_modes":["Embedded mode uses SQLite which is not optimized for concurrent writes — suitable for read-heavy or single-writer workloads only","Distributed Kubernetes deployment requires external PostgreSQL for SysDB (system database) coordination, adding operational complexity","No built-in multi-region replication — requires external tooling for geo-distributed deployments","Python client adds ~50-200ms latency per request in server mode due to HTTP/gRPC serialization overhead","HNSW index is built in-memory and not persisted to disk — requires full rebuild on restart unless using S3 blockstore","Metadata filtering is applied post-ranking (after kNN), not pre-filtering, so large result sets with selective filters are inefficient","No support for hybrid search combining keyword matching with semantic similarity in a single query","HNSW construction is single-threaded in current implementation — index building scales linearly with vector count","SysDB is a separate system from the vector index — metadata and vectors can become inconsistent if not carefully managed","SQLite backend is not suitable for concurrent writes — embedded deployments with multiple writers may experience lock contention","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7591708918378587,"quality":0.35,"ecosystem":0.68,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-02T23:48:40Z"},"community":{"stars":27779,"forks":2230,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=chroma-core--chroma","compare_url":"https://unfragile.ai/compare?artifact=chroma-core--chroma"}},"signature":"01TB1/e8hgf9s97cPM4nxFkmi2Y8lBvzvplt8edgFi/CJFHPFnD9/+47AkyKYRqSQ3Cju3/NaQK5aTX5yku9Bg==","signedAt":"2026-06-16T14:08:17.751Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chroma-core--chroma","artifact":"https://unfragile.ai/chroma-core--chroma","verify":"https://unfragile.ai/api/v1/verify?slug=chroma-core--chroma","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"}}