{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-colbert-ai","slug":"pypi-colbert-ai","name":"colbert-ai","type":"repo","url":"https://github.com/stanford-futuredata/ColBERT","page_url":"https://unfragile.ai/pypi-colbert-ai","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-colbert-ai__cap_0","uri":"capability://data.processing.analysis.token.level.document.encoding.with.contextual.bert.embeddings","name":"token-level document encoding with contextual bert embeddings","description":"Encodes documents as matrices of token-level embeddings rather than single vectors, using a fine-tuned BERT backbone to capture rich contextual information for each token. The encoder processes documents through the BERT transformer stack, producing a [num_tokens, embedding_dim] matrix per document that preserves fine-grained semantic relationships. This matrix representation enables late-interaction matching where query tokens can interact with individual document tokens rather than comparing aggregate vectors.","intents":["I need to encode large document collections with rich contextual information for semantic search","I want document representations that preserve token-level granularity for fine-grained matching","I need to fine-tune a retrieval model on domain-specific query-document pairs"],"best_for":["information retrieval engineers building large-scale passage search systems","researchers implementing neural ranking models with token-level interactions","teams deploying dense retrieval systems requiring both speed and accuracy"],"limitations":["Matrix representations require more memory than single-vector models — approximately 128x more storage per document for typical embedding dimensions","Encoding speed depends on document length; longer documents incur proportional computational cost","Requires GPU for practical throughput; CPU encoding is prohibitively slow for large collections"],"requires":["PyTorch 1.9+","CUDA 11.0+ for GPU acceleration","Pre-trained BERT model checkpoint or HuggingFace model identifier","Python 3.7+"],"input_types":["raw text documents (strings)","tokenized text (list of tokens)","document IDs with associated text"],"output_types":["dense embedding matrices [num_tokens, 128]","compressed binary representations for storage","checkpoint files with model weights"],"categories":["data-processing-analysis","neural-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_1","uri":"capability://search.retrieval.maxsim.late.interaction.similarity.computation","name":"maxsim late-interaction similarity computation","description":"Implements efficient maximum similarity matching between query and document token embeddings using a specialized MaxSim operation that computes the maximum cosine similarity for each query token across all document tokens, then aggregates these maxima. This operation is implemented with CUDA kernels and optimized tensor operations to achieve sub-millisecond latency per query-document pair. The late-interaction design defers similarity computation until search time rather than pre-computing fixed document representations, enabling dynamic query-specific matching.","intents":["I need to rank documents against queries with fine-grained token-level matching in milliseconds","I want to compute relevance scores that capture partial token matches and semantic overlap","I need efficient similarity computation that scales to millions of documents"],"best_for":["search infrastructure teams implementing real-time retrieval at scale","researchers studying interaction-based ranking models","production systems requiring sub-100ms query latency with high precision"],"limitations":["MaxSim computation is O(query_tokens × document_tokens) — longer queries and documents increase latency quadratically","Requires candidate pre-filtering to avoid computing MaxSim against all documents; typically uses approximate nearest neighbor search to reduce candidate set","GPU memory constraints limit batch sizes for large document collections; typical batch size is 100-1000 documents per query"],"requires":["CUDA 11.0+ with compute capability 7.0+","Pre-computed document embeddings in memory or on disk","Query embeddings from the same ColBERT model","Sufficient GPU memory (8GB+ recommended for typical workloads)"],"input_types":["query embedding matrix [num_query_tokens, 128]","document embedding matrices [num_doc_tokens, 128]","candidate document indices (pre-filtered set)"],"output_types":["relevance scores (float32)","ranked document lists with scores","similarity matrices [num_queries, num_candidates]"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_10","uri":"capability://data.processing.analysis.cuda.accelerated.tensor.operations.for.efficiency","name":"cuda-accelerated tensor operations for efficiency","description":"Implements performance-critical operations as custom CUDA kernels and optimized PyTorch operations, including MaxSim computation, embedding compression, and similarity aggregation. These kernels are fused to minimize memory bandwidth and kernel launch overhead, achieving 10-100x speedup over naive PyTorch implementations. Mixed-precision computation (FP16) is used throughout to reduce memory usage and increase throughput on modern GPUs.","intents":["I need to achieve sub-100ms search latency on large document collections","I want to maximize GPU utilization and throughput for batch operations","I need to reduce memory usage for large-scale indexing and search"],"best_for":["production search systems with strict latency requirements","teams deploying retrieval at scale with limited GPU resources","organizations optimizing for cost and energy efficiency"],"limitations":["CUDA kernels are GPU-specific; CPU fallbacks are slow and not recommended for production","Mixed-precision computation can introduce numerical instability in edge cases; requires careful validation","Kernel optimization is hardware-specific; performance varies across GPU architectures"],"requires":["NVIDIA GPU with CUDA compute capability 7.0+","CUDA 11.0+","cuDNN library","PyTorch with CUDA support"],"input_types":["query embeddings (FP16 or FP32)","document embeddings (FP16 or FP32)"],"output_types":["similarity scores (FP32)","ranked results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_11","uri":"capability://automation.workflow.model.checkpoint.management.and.versioning","name":"model checkpoint management and versioning","description":"Manages saving and loading of trained model checkpoints, including model weights, configuration, and training metadata. The checkpoint system saves checkpoints at regular intervals during training, tracks best checkpoints based on validation metrics, and enables resuming training from checkpoints. Checkpoints include model state dict, optimizer state, learning rate scheduler state, and training configuration for full reproducibility.","intents":["I need to save trained models for later use in production","I want to resume training from a checkpoint if training is interrupted","I need to track which checkpoint achieved the best validation performance"],"best_for":["research teams running long training jobs that may be interrupted","teams managing multiple model versions and variants","organizations deploying models to production with version tracking"],"limitations":["Checkpoint files are large (500MB-2GB per checkpoint); storing many checkpoints requires significant disk space","Loading checkpoints requires exact model architecture match; cannot load checkpoints into different model variants","No built-in checkpoint compression; checkpoints are stored in full precision"],"requires":["Disk space for checkpoint storage (10-50GB for typical training runs)","PyTorch checkpoint format compatibility"],"input_types":["trained model","optimizer state","training metadata"],"output_types":["checkpoint files (PyTorch format)","checkpoint metadata (metrics, training step)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_12","uri":"capability://automation.workflow.distributed.training.with.data.parallelism","name":"distributed training with data parallelism","description":"Enables training across multiple GPUs using PyTorch's distributed data parallelism, where each GPU processes a different batch of data and gradients are synchronized across GPUs. The distributed training setup handles gradient synchronization, loss aggregation, and checkpoint saving across processes. Training speed scales approximately linearly with number of GPUs (with some overhead for synchronization).","intents":["I need to train models faster by using multiple GPUs","I want to train on larger batch sizes than fit on a single GPU","I need to reduce training time from days to hours"],"best_for":["teams with access to multi-GPU clusters","organizations training on large datasets (100k+ query-document pairs)","research groups running multiple training experiments in parallel"],"limitations":["Distributed training adds synchronization overhead; scaling efficiency decreases with more GPUs (typically 80-90% efficiency with 8 GPUs)","Requires careful batch size tuning; larger batch sizes may hurt convergence","Debugging distributed training is difficult; errors in one process can cause hangs or cryptic failures"],"requires":["Multiple GPUs (2+) on same machine or connected via high-speed network","PyTorch distributed training utilities","NCCL library for GPU communication","Proper environment variable setup (MASTER_ADDR, MASTER_PORT, etc.)"],"input_types":["training data (distributed across processes)","number of GPUs/processes"],"output_types":["trained model checkpoint","training logs with loss curves"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_2","uri":"capability://automation.workflow.distributed.indexing.pipeline.with.compression","name":"distributed indexing pipeline with compression","description":"Processes large document collections across multiple GPUs and machines using a distributed indexing pipeline that encodes documents in batches, compresses token embeddings using product quantization or other compression schemes, and stores compressed representations in an inverted index structure. The pipeline manages memory efficiently by streaming documents through the encoder, compressing embeddings on-the-fly, and writing compressed vectors to disk in sharded index files. Configuration system allows tuning of batch sizes, compression rates, and number of indexing processes.","intents":["I need to index millions of documents efficiently across multiple GPUs","I want to reduce index size from gigabytes to manageable disk footprint without sacrificing search quality","I need to parallelize document encoding to complete indexing in hours rather than days"],"best_for":["infrastructure teams building large-scale search systems (10M+ documents)","organizations with limited GPU memory requiring distributed processing","teams deploying retrieval systems with strict storage constraints"],"limitations":["Compression introduces quantization error that reduces ranking precision by 1-3% depending on compression rate","Distributed indexing requires careful synchronization; index sharding can complicate incremental updates","Memory overhead during indexing is substantial — requires 2-3x the final index size in temporary buffers during compression"],"requires":["Multiple GPUs (2+) or distributed compute cluster","Sufficient disk space (typically 10-50GB for 1M documents depending on compression)","PyTorch distributed training utilities (torch.distributed)","Document collection in text format or database"],"input_types":["document collection (text files, JSONL, or database)","document IDs and content","compression configuration parameters"],"output_types":["compressed index files (binary format)","index metadata (document ID mappings, shard information)","index statistics (compression ratio, memory usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_3","uri":"capability://search.retrieval.approximate.nearest.neighbor.search.with.index.based.candidate.retrieval","name":"approximate nearest neighbor search with index-based candidate retrieval","description":"Retrieves candidate documents for a query using approximate nearest neighbor (ANN) search over compressed document embeddings, typically implemented with FAISS or similar ANN libraries. The system builds an ANN index over the compressed document embeddings during indexing, then uses the query embedding to retrieve top-k candidates (typically 1000-10000) in milliseconds. These candidates are then re-ranked using exact MaxSim computation to produce final results. The ANN search trades small precision loss for dramatic latency improvements, enabling sub-100ms end-to-end query latency.","intents":["I need to retrieve relevant documents from millions of candidates in under 100ms","I want to avoid computing exact similarity against all documents while maintaining high recall","I need to balance search latency and accuracy through configurable candidate set sizes"],"best_for":["production search systems with strict latency SLAs (< 100ms)","large-scale retrieval systems with 10M+ documents","teams deploying retrieval as a service with variable query loads"],"limitations":["ANN search introduces recall loss — typically 1-5% of relevant documents are missed in the candidate set","Index size grows with document count; FAISS indices can require 10-100GB for 100M documents","ANN index must be rebuilt or updated incrementally; full reindexing is expensive for large collections","Candidate set size must be tuned per use case; too small reduces recall, too large increases latency"],"requires":["FAISS library (Facebook AI Similarity Search) or equivalent ANN library","Compressed document embeddings from indexing pipeline","Query embeddings from ColBERT encoder","Sufficient RAM to load ANN index (typically 10-50GB for large collections)"],"input_types":["query embedding vector [num_query_tokens, 128]","ANN index (FAISS index file)","document ID mappings"],"output_types":["candidate document indices (top-k, typically k=1000)","approximate similarity scores from ANN","final ranked results after MaxSim re-ranking"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_4","uri":"capability://planning.reasoning.model.training.with.contrastive.learning.on.query.document.pairs","name":"model training with contrastive learning on query-document pairs","description":"Trains the ColBERT model end-to-end using contrastive learning objectives on query-document training pairs, where positive pairs are relevant documents and negative pairs are non-relevant documents. The trainer implements in-batch negatives, hard negative mining, and other techniques to improve training efficiency. Training uses mixed-precision computation (FP16) and gradient accumulation to fit large batch sizes on available GPUs. The trainer manages checkpoint saving, learning rate scheduling, and evaluation on validation sets during training.","intents":["I need to fine-tune a retrieval model on my domain-specific query-document pairs","I want to improve ranking accuracy for my specific use case beyond pre-trained models","I need to train a model that understands my domain's terminology and relevance judgments"],"best_for":["information retrieval teams with labeled query-document datasets","organizations building domain-specific search systems (legal, medical, scientific)","researchers experimenting with retrieval model architectures and training objectives"],"limitations":["Requires substantial labeled training data — typically 10k-100k query-document pairs for effective fine-tuning","Training time is significant — 24-72 hours on 8 GPUs for typical datasets","Convergence depends heavily on hyperparameter tuning (learning rate, batch size, negative sampling strategy)","Hard negative mining requires multiple passes over data, increasing training time by 2-3x"],"requires":["PyTorch 1.9+","Multiple GPUs (4+ recommended for reasonable training time)","CUDA 11.0+","Labeled query-document pairs in JSONL or similar format","Sufficient disk space for checkpoints (10-50GB)"],"input_types":["query-document pairs with relevance labels","query text (strings)","document text (strings)","optional: hard negative documents"],"output_types":["trained model checkpoint (PyTorch state dict)","training logs with loss curves","evaluation metrics on validation set","model configuration file"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_5","uri":"capability://automation.workflow.incremental.index.updates.without.full.reindexing","name":"incremental index updates without full reindexing","description":"Allows adding, removing, or updating documents in an existing index without reindexing the entire collection. The IndexUpdater component manages delta operations by encoding new documents, compressing them with the same compression scheme as the original index, and merging them into the index structure. For deletions, it marks documents as deleted in metadata without physically removing them. For updates, it re-encodes the document and replaces its compressed representation. This capability enables continuous index maintenance for evolving document collections.","intents":["I need to add new documents to my search index without reindexing everything","I want to update document content when it changes without downtime","I need to remove documents from the index while keeping the system running"],"best_for":["production search systems with continuously evolving document collections","teams managing real-time indexing pipelines (news, social media, e-commerce)","organizations that cannot afford full reindexing downtime"],"limitations":["Incremental updates are slower per-document than batch indexing — typically 10-100x slower","Index fragmentation increases over time with many updates; periodic full reindexing is recommended every 1-3 months","Deleted documents are marked but not physically removed, causing index bloat if deletion rate is high","Update operations require re-encoding documents, which is GPU-bound and cannot be batched efficiently"],"requires":["Existing ColBERT index","Document encoder (same model as original index)","GPU for encoding new documents","Write access to index files"],"input_types":["new documents (text with IDs)","document IDs to delete","updated documents (text with IDs)"],"output_types":["updated index files","metadata updates (document ID mappings)","operation logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_6","uri":"capability://data.processing.analysis.query.encoding.with.token.level.embeddings","name":"query encoding with token-level embeddings","description":"Encodes search queries into token-level embedding matrices using the same BERT encoder as documents, producing a [num_query_tokens, embedding_dim] matrix. The query encoder applies the same tokenization and contextualization as the document encoder, ensuring compatible representations for MaxSim matching. Query encoding is fast (typically < 10ms) because queries are short, making it practical to encode queries at search time rather than pre-computing them.","intents":["I need to convert search queries into embeddings compatible with my document index","I want query representations that preserve token-level semantic information","I need fast query encoding to support interactive search with sub-100ms latency"],"best_for":["search system builders implementing query processing pipelines","teams building interactive search interfaces with real-time query encoding","researchers studying query representation in neural retrieval"],"limitations":["Query encoding requires GPU for practical latency; CPU encoding adds 50-200ms per query","Very long queries (> 512 tokens) are truncated by BERT tokenizer, losing information","Query encoding is not cached because queries are typically unique; caching provides minimal benefit"],"requires":["ColBERT model checkpoint (same as document encoder)","BERT tokenizer","GPU for sub-10ms latency","PyTorch runtime"],"input_types":["query text (string)","optional: pre-tokenized query"],"output_types":["query embedding matrix [num_query_tokens, 128]","token IDs","attention masks"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_7","uri":"capability://search.retrieval.batch.search.with.multi.query.processing","name":"batch search with multi-query processing","description":"Processes multiple queries in a single batch operation, encoding queries together and computing similarities against the index in vectorized operations. The batch search implementation uses PyTorch's batched matrix operations to compute MaxSim for all query-document pairs simultaneously, achieving higher throughput than sequential per-query search. Batch sizes are configurable and limited by GPU memory; typical batch sizes are 32-128 queries.","intents":["I need to process multiple search queries efficiently in a single operation","I want to maximize GPU utilization by batching query processing","I need to evaluate retrieval quality on large test sets quickly"],"best_for":["batch evaluation systems processing hundreds or thousands of queries","offline retrieval pipelines for document ranking and re-ranking","research teams evaluating retrieval models on benchmark datasets"],"limitations":["Batch size is limited by GPU memory — larger batches require more VRAM","Batching adds latency for interactive use cases; single-query latency is lower than batch latency per query","All queries in a batch must use the same index; cannot mix queries against different indices"],"requires":["Multiple queries (typically 10+)","GPU with sufficient memory for batch size","Pre-computed index"],"input_types":["batch of query texts","batch size parameter"],"output_types":["batch of ranked result lists","batch of relevance scores","timing information per query"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_8","uri":"capability://automation.workflow.configuration.management.with.hierarchical.settings","name":"configuration management with hierarchical settings","description":"Provides a hierarchical configuration system that manages settings for model architecture, training hyperparameters, indexing parameters, and search settings. Configuration can be specified via Python objects, YAML files, or command-line arguments, with clear precedence rules for overrides. The configuration system validates settings, provides defaults, and enables reproducible experiments by capturing all hyperparameters in configuration files.","intents":["I need to manage complex hyperparameters across training, indexing, and search","I want to reproduce experiments by saving and loading configurations","I need to experiment with different settings without modifying code"],"best_for":["research teams running hyperparameter sweeps and ablation studies","production teams managing multiple model variants and configurations","organizations standardizing retrieval system configurations across teams"],"limitations":["Configuration validation is basic; invalid combinations of settings may not be caught until runtime","Configuration files can become large and difficult to manage for complex systems","No built-in configuration versioning or change tracking"],"requires":["Python 3.7+","PyYAML for YAML configuration files (optional)"],"input_types":["Python configuration objects","YAML configuration files","command-line arguments"],"output_types":["validated configuration objects","configuration files (YAML or JSON)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-colbert-ai__cap_9","uri":"capability://data.processing.analysis.evaluation.metrics.computation.for.retrieval.quality","name":"evaluation metrics computation for retrieval quality","description":"Computes standard information retrieval evaluation metrics including Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), Recall@k, and Mean Average Precision (MAP) on ranked retrieval results. The evaluation module compares predicted rankings against ground truth relevance judgments and aggregates metrics across queries. Metrics are computed efficiently using vectorized operations and support both binary and graded relevance judgments.","intents":["I need to measure retrieval quality on my test set","I want to compare different retrieval models using standard metrics","I need to track model performance improvements during training"],"best_for":["research teams evaluating retrieval models on benchmark datasets","teams validating model improvements before production deployment","organizations comparing retrieval systems using standard metrics"],"limitations":["Metrics assume binary or graded relevance judgments; cannot handle soft relevance scores","Evaluation requires ground truth relevance labels; cannot evaluate on unlabeled data","Metrics are sensitive to ranking cutoff (k); results vary significantly with different k values"],"requires":["Ranked retrieval results (list of document IDs per query)","Ground truth relevance judgments (binary or graded)","Query-document mapping"],"input_types":["ranked results (list of document IDs per query)","relevance judgments (binary or graded)","optional: ranking cutoff k"],"output_types":["metric values (MRR, NDCG, Recall@k, MAP)","per-query metric values","aggregated statistics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","CUDA 11.0+ for GPU acceleration","Pre-trained BERT model checkpoint or HuggingFace model identifier","Python 3.7+","CUDA 11.0+ with compute capability 7.0+","Pre-computed document embeddings in memory or on disk","Query embeddings from the same ColBERT model","Sufficient GPU memory (8GB+ recommended for typical workloads)","NVIDIA GPU with CUDA compute capability 7.0+","CUDA 11.0+"],"failure_modes":["Matrix representations require more memory than single-vector models — approximately 128x more storage per document for typical embedding dimensions","Encoding speed depends on document length; longer documents incur proportional computational cost","Requires GPU for practical throughput; CPU encoding is prohibitively slow for large collections","MaxSim computation is O(query_tokens × document_tokens) — longer queries and documents increase latency quadratically","Requires candidate pre-filtering to avoid computing MaxSim against all documents; typically uses approximate nearest neighbor search to reduce candidate set","GPU memory constraints limit batch sizes for large document collections; typical batch size is 100-1000 documents per query","CUDA kernels are GPU-specific; CPU fallbacks are slow and not recommended for production","Mixed-precision computation can introduce numerical instability in edge cases; requires careful validation","Kernel optimization is hardware-specific; performance varies across GPU architectures","Checkpoint files are large (500MB-2GB per checkpoint); storing many checkpoints requires significant disk space","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:18.280Z","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-colbert-ai","compare_url":"https://unfragile.ai/compare?artifact=pypi-colbert-ai"}},"signature":"xf4PwLIUSN5FpwyvKBqgUz4HFJaFVlM5jcyJZunyjA08ZFbrwy8X0yE8LZ+awCJxDZfHr8kvaRyRmg7ubTnDAA==","signedAt":"2026-06-21T02:59:32.553Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-colbert-ai","artifact":"https://unfragile.ai/pypi-colbert-ai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-colbert-ai","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"}}