{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-pinecone-client","slug":"pypi-pinecone-client","name":"pinecone-client","type":"platform","url":"https://www.pinecone.io","page_url":"https://unfragile.ai/pypi-pinecone-client","categories":["code-editors","rag-knowledge"],"tags":["Pinecone","vector","database","cloud"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"pypi_pypi-pinecone-client__cap_0","uri":"capability://search.retrieval.dense.vector.semantic.search.with.metadata.filtering","name":"dense-vector-semantic-search-with-metadata-filtering","description":"Executes approximate nearest neighbor (ANN) search over dense vector embeddings using optimized indexing algorithms (tree-based or graph-based structures like HNSW), returning top-K results filtered by JSON metadata predicates. The client sends a query vector and optional filter constraints to the Pinecone managed service, which applies filtering before or after ANN traversal depending on selectivity, returning ranked results with scores and metadata in real-time (<100ms latency for typical workloads).","intents":["Find semantically similar documents or records given a query embedding","Retrieve top-K nearest neighbors with business logic constraints (e.g., only results from 'technology' category)","Build semantic search features for RAG pipelines without managing vector indices locally","Implement multi-tenant search where different namespaces isolate user data"],"best_for":["Teams building RAG systems or semantic search features without infrastructure overhead","Enterprises requiring multi-tenant vector isolation via namespaces","Developers integrating embeddings from external models (OpenAI, Cohere) into production search"],"limitations":["Deprecated client library — no longer maintained; users must migrate to official Pinecone Python SDK","Requires live network connection to Pinecone managed service; no local/offline query capability","Metadata filtering performance degrades with high cardinality or complex boolean expressions; sparse filtering recommended for large result sets","Vector dimensionality fixed per index; changing dimensions requires index recreation","Query latency increases with index size and filter selectivity; no SLA on response times for Standard tier"],"requires":["Python runtime (version unspecified in deprecated docs)","Valid Pinecone API key from account setup","Network connectivity to Pinecone cloud service (AWS, GCP, or Azure region)","Pre-computed query vector (float array) from external embedding model or Pinecone hosted models"],"input_types":["dense vector (float array, e.g., [0.13, 0.45, 1.34, ...], dimensionality 128-3072)","metadata filter object (JSON with operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $exists)","namespace string (optional, for tenant isolation)","top_k integer (result limit, typically 1-100)"],"output_types":["ranked result list with match scores (float 0-1), vector IDs, and metadata objects","structured JSON response with matches array and query metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_1","uri":"capability://search.retrieval.sparse.vector.lexical.search.with.bm25.ranking","name":"sparse-vector-lexical-search-with-bm25-ranking","description":"Executes full-text search using sparse vector representations (token-based, typically BM25-weighted) to find lexically similar documents, complementing dense semantic search. The client sends sparse vectors (token IDs with weights) to Pinecone, which applies inverted index lookups and BM25 ranking, enabling hybrid search when combined with dense results. Sparse vectors are more interpretable than dense embeddings and excel at exact keyword matching.","intents":["Perform full-text keyword search alongside semantic search for hybrid retrieval","Retrieve documents matching exact terms or phrases with statistical relevance ranking","Combine sparse (lexical) and dense (semantic) results for improved recall in RAG systems","Implement search features that require both keyword precision and semantic understanding"],"best_for":["RAG pipelines requiring both keyword and semantic relevance (e.g., legal document retrieval)","Teams building search with interpretable ranking (BM25 scores are explainable vs dense embeddings)","Applications where exact term matching is critical (e.g., medical terminology, product SKUs)"],"limitations":["Sparse vector generation requires external tokenizer or BM25 implementation; Pinecone does not provide built-in tokenization","Sparse vectors less effective for synonyms or semantic variations compared to dense embeddings","Hybrid search combining sparse + dense results requires manual result merging/reranking logic in client code","Sparse vector storage overhead increases with vocabulary size; not suitable for extremely high-cardinality token spaces"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","External tokenizer and BM25 weighting library (e.g., scikit-learn, custom implementation)","Pre-computed sparse vectors (token ID → weight mappings) before upsert"],"input_types":["sparse vector (dict or list of token IDs with float weights, e.g., {0: 0.5, 42: 0.8, 1001: 0.3})","metadata filter object (same JSON format as dense search)","namespace string (optional)","top_k integer"],"output_types":["ranked result list with BM25 scores, vector IDs, and metadata","structured JSON response with sparse search matches"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_10","uri":"capability://data.processing.analysis.index.listing.and.vector.id.enumeration","name":"index-listing-and-vector-id-enumeration","description":"Lists vector IDs in an index or namespace, enabling pagination, auditing, or bulk operations. The client requests a list of IDs (optionally filtered by namespace or prefix); Pinecone returns paginated results. This is useful for understanding index contents or implementing cursor-based retrieval.","intents":["Enumerate all vectors in an index for auditing or validation","Implement pagination in search results using vector IDs as cursors","Identify duplicate or orphaned vectors","Export index contents for backup or migration"],"best_for":["Index maintenance and auditing workflows","Pagination implementation in RAG systems","Data migration and backup scenarios"],"limitations":["Listing large indices (millions of vectors) is slow; no efficient bulk enumeration API shown","Pagination requires manual cursor management; no built-in iterator abstraction in deprecated client","Listing does not return vector data or metadata; only IDs","Deprecated client library does not show list API; requires consulting newer SDK","No filtering by metadata in list operation; only namespace-level filtering"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Namespace string (optional)"],"input_types":["namespace string (optional)","pagination cursor or limit (optional)"],"output_types":["list of vector IDs (strings)","pagination cursor for next batch","structured JSON response with ID list"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_11","uri":"capability://safety.moderation.api.key.based.authentication.and.authorization","name":"api-key-based-authentication-and-authorization","description":"Authenticates client requests using API keys issued by Pinecone account setup. The client includes the API key in requests (via header or constructor parameter); Pinecone validates the key and authorizes operations. This is a simple, stateless authentication model suitable for server-to-server communication.","intents":["Authenticate client applications to Pinecone service","Authorize read/write operations based on API key permissions","Manage credentials for multiple environments (dev, staging, prod)"],"best_for":["Server-side applications with static credentials","Development and testing environments","Simple deployments without complex access control requirements"],"limitations":["API key is a shared secret; no fine-grained per-operation permissions","No tenant-level access control; all operations with same key have same permissions","API key rotation requires updating all clients; no automatic key expiration shown","No audit logging of which client made which request (API key does not identify client)","Deprecated client library does not show key management API; requires consulting newer SDK"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key from account setup","Network connectivity to Pinecone service"],"input_types":["API key string (passed to Pinecone constructor)"],"output_types":["authentication success/failure status","authorization error if key lacks permissions"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_12","uri":"capability://automation.workflow.cloud.region.and.provider.selection","name":"cloud-region-and-provider-selection","description":"Deploys Pinecone indices in specific cloud regions (AWS, GCP, Azure) and availability zones, enabling data residency compliance and latency optimization. The client connects to indices in the selected region; Pinecone handles replication and failover within that region. This is configured at index creation time, not per-query.","intents":["Ensure data residency compliance (e.g., EU data must stay in EU regions)","Optimize query latency by deploying indices close to users","Support multi-region deployments for high availability","Meet regulatory requirements (GDPR, HIPAA) for data location"],"best_for":["Enterprise applications with data residency requirements","Global applications requiring low-latency access from multiple regions","Compliance-driven deployments (GDPR, HIPAA, SOC 2)"],"limitations":["Region selection is static per index; cannot change region without recreating index","Cross-region replication not shown in deprecated client; requires separate infrastructure","No automatic failover to other regions; only within-region high availability","Deprecated client library does not show region configuration API; requires consulting newer SDK","Multi-region deployments require multiple indices and client-side routing logic"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Pinecone index created in desired region (AWS, GCP, or Azure)"],"input_types":["region identifier (e.g., 'us-west-2', 'europe-west1')","cloud provider (AWS, GCP, Azure)"],"output_types":["index endpoint in selected region","region confirmation in index metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_13","uri":"capability://automation.workflow.index.backup.and.restore.operations","name":"index-backup-and-restore-operations","description":"Creates backups of vector indices and restores them to recover from data loss or enable point-in-time recovery. Pinecone manages backups automatically or on-demand; the client can trigger restore operations to recover a previous index state. Backup and restore are asynchronous operations.","intents":["Recover from accidental data deletion or corruption","Implement disaster recovery procedures","Clone indices for testing or development","Maintain audit trails of index state changes"],"best_for":["Production RAG systems requiring high availability","Compliance-driven applications with data retention requirements","Teams implementing disaster recovery procedures"],"limitations":["Backup and restore APIs not shown in deprecated client; requires consulting newer SDK","Backup frequency and retention policies not documented in deprecated docs","Restore operation creates a new index; original index must be manually deleted","No point-in-time recovery granularity specified; may only support daily or weekly snapshots","Backup storage costs not transparent; may be included in Pinecone pricing or charged separately"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key with backup/restore permissions","Existing index to backup or restore"],"input_types":["index name (for backup)","backup ID or timestamp (for restore)"],"output_types":["backup confirmation (job ID, status)","restore confirmation (new index name, status)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_2","uri":"capability://search.retrieval.hybrid.search.combining.sparse.and.dense.vectors","name":"hybrid-search-combining-sparse-and-dense-vectors","description":"Executes simultaneous sparse (lexical) and dense (semantic) vector search in a single query, combining results via weighted fusion (e.g., reciprocal rank fusion or linear combination of scores). The client sends both sparse and dense vectors to Pinecone, which performs parallel ANN and inverted index lookups, then merges ranked results using configurable fusion strategies. This enables retrieval systems that benefit from both keyword precision and semantic understanding.","intents":["Retrieve documents matching both keyword and semantic relevance in one query","Improve RAG recall by combining lexical and semantic signals without multiple round-trips","Balance interpretability (sparse BM25 scores) with semantic understanding (dense embeddings)","Implement search that handles both exact matches and conceptual similarity"],"best_for":["Enterprise RAG systems requiring high recall and precision (e.g., legal, medical, financial search)","Teams building search where both keyword and semantic relevance matter equally","Applications with heterogeneous queries (some keyword-heavy, some semantic)"],"limitations":["Hybrid search requires pre-computing both sparse and dense vectors; doubles embedding computation cost","Result fusion strategy (weighting sparse vs dense) must be tuned per use case; no automatic optimization","Client library does not provide built-in fusion algorithms; manual score combination required in application code","Latency is sum of sparse + dense search times; slower than single-modality search","Sparse vector generation still requires external tokenizer; Pinecone does not provide end-to-end tokenization"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","External embedding model for dense vectors (OpenAI, Cohere, etc.)","External tokenizer and BM25 weighting for sparse vectors","Custom fusion logic to combine sparse and dense results (not provided by client)"],"input_types":["dense vector (float array, dimensionality 128-3072)","sparse vector (token ID → weight dict)","metadata filter object (JSON)","namespace string (optional)","top_k integer","fusion weights or strategy (custom, not exposed in API)"],"output_types":["merged ranked result list with combined scores, vector IDs, and metadata","structured JSON with hybrid search matches"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_3","uri":"capability://data.processing.analysis.real.time.vector.upsert.with.metadata.indexing","name":"real-time-vector-upsert-with-metadata-indexing","description":"Inserts or updates vectors with associated metadata in real-time, automatically indexing them for immediate search availability. The client sends upsert requests (vector ID, dense/sparse vector, metadata JSON) to Pinecone, which applies the vector to the ANN index and metadata to the filter index within milliseconds. Upserted vectors are queryable immediately without batch reindexing, enabling dynamic knowledge base updates in RAG systems.","intents":["Add new documents or embeddings to a vector index without downtime or batch reprocessing","Update existing vector records (e.g., refresh metadata or embedding) in-place","Build real-time RAG systems where knowledge base grows continuously (e.g., live news indexing)","Maintain vector indices for streaming data sources (logs, sensor data, chat history)"],"best_for":["Real-time RAG systems (e.g., live document ingestion, chat memory indexing)","Applications with frequently updated knowledge bases (e.g., product catalogs, news feeds)","Teams building multi-tenant systems where each tenant's data is upserted independently"],"limitations":["Upsert latency increases with batch size; single-vector upserts are faster than bulk operations","No transactional guarantees across multiple upserts; partial failures possible in bulk operations","Metadata indexing adds overhead; complex metadata schemas or high-cardinality fields degrade filter performance","No built-in deduplication; duplicate vector IDs overwrite previous records without warning","Upsert throughput limited by Pinecone tier (Standard: ~1K vectors/sec; Enterprise: higher)","Deprecated client library does not support streaming upserts; requires polling or manual batching"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key with write permissions","Network connectivity to Pinecone service","Vector ID (string), dense/sparse vector, and metadata JSON for each record"],"input_types":["vector ID (string, unique identifier)","dense vector (float array) or sparse vector (token dict) or both","metadata object (JSON with arbitrary key-value pairs for filtering)","namespace string (optional, for tenant isolation)"],"output_types":["upsert confirmation (success/failure status per vector)","structured JSON response with upsert statistics (vectors_upserted count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_4","uri":"capability://memory.knowledge.namespace.based.multi.tenant.data.isolation","name":"namespace-based-multi-tenant-data-isolation","description":"Partitions vector data within a single index into isolated namespaces, enabling multi-tenant deployments where each tenant's vectors and metadata are logically separated. The client specifies a namespace string in query and upsert operations; Pinecone enforces isolation at the storage and query layers, ensuring queries in namespace 'tenant-A' never return results from 'tenant-B'. Namespaces share the same index infrastructure but maintain separate vector spaces.","intents":["Build multi-tenant SaaS applications where each customer's data is isolated","Segment vector data by user, organization, or domain without creating separate indices","Implement role-based access control where queries are scoped to specific namespaces","Reduce infrastructure costs by consolidating multiple tenants into a single index"],"best_for":["SaaS platforms with multiple customers requiring data isolation (e.g., multi-tenant RAG)","Enterprise applications with department-level or project-level data segmentation","Teams building white-label search or recommendation systems"],"limitations":["Namespace isolation is logical, not cryptographic; relies on correct namespace specification in client code (no server-side enforcement of tenant identity)","Queries cannot span multiple namespaces; cross-tenant aggregation requires multiple queries and client-side merging","Namespace creation is implicit (first upsert to a namespace creates it); no explicit namespace management API shown in deprecated client","Metadata filtering applies within a namespace; cannot filter across namespaces","Namespace-level access control not exposed in client; authentication is API-key-based (all tenants share same key)"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Namespace identifier string (e.g., 'tenant-123', 'user-456') for each tenant","Application-level logic to track and enforce namespace boundaries"],"input_types":["namespace string (tenant identifier)","vector ID, vector, metadata (same as upsert)","query vector and filters (same as search)"],"output_types":["query results scoped to specified namespace","upsert confirmation within namespace"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_5","uri":"capability://data.processing.analysis.batch.vector.import.from.object.storage","name":"batch-vector-import-from-object-storage","description":"Ingests large volumes of vectors and metadata from cloud object storage (S3, GCS) in batch, avoiding the need to stream individual upserts through the client. Pinecone reads vector files directly from object storage, parses them (format unspecified in deprecated docs), and indexes them in bulk. This is more efficient than client-side upsert loops for large-scale data migrations or initial index population.","intents":["Migrate existing vector datasets from S3 or GCS into Pinecone without client-side processing","Perform large-scale batch indexing of pre-computed embeddings (e.g., from offline embedding pipeline)","Reduce network bandwidth by reading vectors directly from object storage instead of through client","Initialize a new index with millions of vectors in a single operation"],"best_for":["Data engineering teams performing bulk vector migrations","Organizations with pre-computed embeddings stored in cloud object storage","Large-scale RAG deployments requiring efficient initial index population"],"limitations":["Batch import format and API not documented in deprecated client; requires consulting Pinecone documentation or newer SDK","No progress tracking or resumable imports shown; failures may require re-uploading entire batch","Object storage credentials must be provided to Pinecone (security implications for sensitive data)","Batch import latency depends on file size and Pinecone service capacity; no SLA on completion time","No built-in validation of vector format or metadata schema before import; malformed data may cause silent failures"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Cloud object storage (S3 or GCS) with vector data files","Object storage credentials (AWS IAM role or GCS service account)","Vector file format compatible with Pinecone (unspecified in deprecated docs)"],"input_types":["object storage URI (s3://bucket/path or gs://bucket/path)","vector file format (JSON, Parquet, or other; unspecified)","namespace string (optional)"],"output_types":["batch import job status (queued, in-progress, completed, failed)","import statistics (vectors_imported count, errors)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_6","uri":"capability://data.processing.analysis.integrated.embedding.model.text.to.vector.conversion","name":"integrated-embedding-model-text-to-vector-conversion","description":"Converts raw text directly to vectors using Pinecone-hosted embedding models (e.g., OpenAI, Cohere) without requiring external embedding infrastructure. The client sends text strings to Pinecone, which applies the configured embedding model and returns dense vectors. This eliminates the need to manage separate embedding services or pre-compute embeddings offline.","intents":["Index raw text documents without pre-computing embeddings externally","Query with natural language text instead of pre-computed vectors","Simplify RAG pipelines by consolidating text-to-vector conversion into Pinecone","Avoid managing multiple embedding services (OpenAI API, Cohere API, etc.)"],"best_for":["Teams building RAG systems where text-to-vector conversion is a bottleneck","Prototypes and MVPs where simplicity is prioritized over cost optimization","Applications with variable embedding model requirements (can switch models without code changes)"],"limitations":["Integrated embedding models incur additional latency (embedding + indexing) compared to pre-computed vectors","Embedding model selection is limited to Pinecone's supported providers; cannot use custom or fine-tuned models","Embedding costs are opaque (bundled with Pinecone pricing); no per-embedding billing transparency","Text-to-vector conversion happens server-side; no local embedding option for offline or privacy-sensitive use cases","Deprecated client library does not show text embedding API; requires consulting newer SDK or documentation"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Pinecone index configured with an embedding model (OpenAI, Cohere, etc.)","Raw text input (string or list of strings)"],"input_types":["text string or list of text strings","metadata object (JSON, optional)","namespace string (optional)"],"output_types":["dense vector (float array) generated from text","upsert confirmation if text is indexed directly"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_7","uri":"capability://planning.reasoning.metadata.driven.result.reranking.and.post.processing","name":"metadata-driven-result-reranking-and-post-processing","description":"Reranks or filters search results after retrieval based on metadata attributes, enabling precision refinement beyond ANN scoring. The client can apply custom reranking logic (e.g., boost results with specific metadata values, sort by timestamp) to post-process Pinecone results. This is useful for business logic that cannot be expressed as pre-query filters (e.g., 'boost recent documents by 20%').","intents":["Apply business logic to search results (e.g., prioritize recent documents, boost premium content)","Implement multi-stage ranking (ANN score + metadata-driven reranking)","Filter results based on dynamic criteria determined after initial search","Customize result ordering for different user segments or contexts"],"best_for":["RAG systems with complex ranking requirements beyond vector similarity","E-commerce or content platforms where metadata-driven ranking is critical","Teams implementing personalization or context-aware search"],"limitations":["Reranking happens in client code, not server-side; adds latency for large result sets","No built-in reranking algorithms provided by deprecated client; requires custom implementation","Reranking logic is not optimized by Pinecone; inefficient for high-throughput scenarios","Metadata-driven reranking cannot leverage index statistics (e.g., term frequency); purely application-level"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Custom reranking logic implemented in application code","Metadata fields present in search results for reranking criteria"],"input_types":["search results from Pinecone (list of matches with metadata)","reranking criteria (custom logic, e.g., lambda function)"],"output_types":["reranked result list with updated ordering","filtered result list (subset of original results)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_8","uri":"capability://data.processing.analysis.record.fetch.by.vector.id","name":"record-fetch-by-vector-id","description":"Retrieves specific vectors and metadata by their IDs without performing a search. The client sends a list of vector IDs to Pinecone, which returns the corresponding vectors and metadata. This is useful for retrieving known records or validating that vectors exist in the index.","intents":["Retrieve a specific document's embedding and metadata by ID","Validate that a vector exists in the index before performing operations","Fetch vectors for post-processing or analysis without search","Implement pagination or cursor-based retrieval in RAG systems"],"best_for":["Applications requiring direct record access by ID","Debugging and validation workflows","Pagination or cursor-based result fetching"],"limitations":["Fetch operation does not return search scores or ranking; only raw vectors and metadata","Fetching large numbers of vectors (>10K) may be slow; not optimized for bulk retrieval","Deprecated client library does not show fetch API; requires consulting newer SDK","No filtering or conditional fetch; must know exact vector IDs in advance"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key","Vector IDs (strings) to fetch"],"input_types":["list of vector IDs (strings)","namespace string (optional)"],"output_types":["list of vector records with ID, vector, and metadata","structured JSON response with fetched vectors"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-pinecone-client__cap_9","uri":"capability://data.processing.analysis.vector.deletion.by.id.or.metadata.filter","name":"vector-deletion-by-id-or-metadata-filter","description":"Removes vectors from the index by vector ID or by metadata filter criteria. The client sends delete requests specifying either exact IDs or filter predicates; Pinecone removes matching vectors from both the ANN index and metadata index. Deleted vectors are immediately unavailable for search.","intents":["Remove outdated or irrelevant documents from the knowledge base","Implement data retention policies (e.g., delete documents older than 30 days)","Clean up test or duplicate vectors during development","Support user-initiated content removal (e.g., 'forget me' requests)"],"best_for":["RAG systems with evolving knowledge bases","Compliance-driven applications requiring data deletion (GDPR, CCPA)","Multi-tenant systems where tenants can remove their own data"],"limitations":["Deletion by metadata filter requires scanning the index; slow for large indices or complex filters","No soft-delete option; deletion is permanent and cannot be undone","Deprecated client library does not show delete API; requires consulting newer SDK","Batch deletion latency increases with number of vectors; no async deletion option shown","No transactional guarantees; partial failures possible in bulk deletes"],"requires":["Python runtime (version unspecified)","Valid Pinecone API key with delete permissions","Vector IDs or metadata filter criteria for deletion"],"input_types":["vector ID (string) or list of IDs for exact deletion","metadata filter object (JSON) for filter-based deletion","namespace string (optional)"],"output_types":["deletion confirmation (success/failure status)","structured JSON response with deletion statistics (vectors_deleted count)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Python runtime (version unspecified in deprecated docs)","Valid Pinecone API key from account setup","Network connectivity to Pinecone cloud service (AWS, GCP, or Azure region)","Pre-computed query vector (float array) from external embedding model or Pinecone hosted models","Python runtime (version unspecified)","Valid Pinecone API key","External tokenizer and BM25 weighting library (e.g., scikit-learn, custom implementation)","Pre-computed sparse vectors (token ID → weight mappings) before upsert","Namespace string (optional)","Network connectivity to Pinecone service"],"failure_modes":["Deprecated client library — no longer maintained; users must migrate to official Pinecone Python SDK","Requires live network connection to Pinecone managed service; no local/offline query capability","Metadata filtering performance degrades with high cardinality or complex boolean expressions; sparse filtering recommended for large result sets","Vector dimensionality fixed per index; changing dimensions requires index recreation","Query latency increases with index size and filter selectivity; no SLA on response times for Standard tier","Sparse vector generation requires external tokenizer or BM25 implementation; Pinecone does not provide built-in tokenization","Sparse vectors less effective for synonyms or semantic variations compared to dense embeddings","Hybrid search combining sparse + dense results requires manual result merging/reranking logic in client code","Sparse vector storage overhead increases with vocabulary size; not suitable for extremely high-cardinality token spaces","Listing large indices (millions of vectors) is slow; no efficient bulk enumeration API shown","builder identity is not verified yet","artifact is still pending review"],"rank_breakdown":{"adoption":0.05,"quality":0.25,"ecosystem":0.52,"match_graph":0.25,"freshness":0.25,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"pending_review","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:16.568Z","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-pinecone-client","compare_url":"https://unfragile.ai/compare?artifact=pypi-pinecone-client"}},"signature":"+APgeTT30B73rvQIPNXVQ7ph9X2Q9T9aIqXUlx9f2IWeIGMPU3JFlF/jgLuodVYjirrspb/oz979qBzbXdgxAw==","signedAt":"2026-06-22T05:36:29.162Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-pinecone-client","artifact":"https://unfragile.ai/pypi-pinecone-client","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-pinecone-client","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"}}