{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-nomic-embed-text","slug":"nomic-embed-text","name":"Nomic Embed Text (137M)","type":"model","url":"https://ollama.com/library/nomic-embed-text","page_url":"https://unfragile.ai/nomic-embed-text","categories":["rag-knowledge"],"tags":["ollama","open-source","embeddings","nomic","embedding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-nomic-embed-text__cap_0","uri":"capability://memory.knowledge.dense.vector.embedding.generation.for.semantic.search","name":"dense vector embedding generation for semantic search","description":"Converts input text into fixed-dimensional dense vectors (embeddings) using a 137M-parameter encoder-only transformer architecture optimized for semantic similarity tasks. The model processes text up to 2,048 tokens and outputs numerical vectors suitable for cosine similarity, nearest-neighbor search, and vector database indexing. Embeddings capture semantic meaning rather than lexical patterns, enabling retrieval of contextually relevant documents regardless of exact keyword matches.","intents":["I need to convert documents into vectors for semantic search in a RAG pipeline","I want to find similar documents or passages without keyword matching","I need to build a vector index for fast retrieval-augmented generation","I want to measure semantic similarity between text pairs programmatically"],"best_for":["Developers building local RAG systems without cloud dependencies","Teams deploying embedding infrastructure on-premises for privacy-sensitive data","Researchers comparing embedding model performance across open-source alternatives","Solo developers prototyping semantic search without OpenAI API costs"],"limitations":["Context window limited to 2,048 tokens — longer documents must be chunked before embedding","Embedding dimensionality not documented in provided materials — integration requires reverse-engineering or consulting HuggingFace model card","No fine-tuning capability exposed — cannot adapt embeddings to domain-specific vocabulary or tasks","Single-purpose model — cannot be repurposed for text generation, classification, or other downstream tasks","Inference latency and throughput benchmarks not provided — performance characteristics unknown without benchmarking"],"requires":["Ollama runtime 0.1.26 or later (local or cloud deployment)","Text input as string (no binary or structured data support)","Network connectivity to Ollama API endpoint (localhost:11434 for local, cloud URL for managed)","Sufficient disk space for 274MB model download and storage"],"input_types":["plain text (strings)","document passages","query strings"],"output_types":["dense numerical vectors (floating-point arrays)","embedding arrays compatible with vector databases (Pinecone, Weaviate, Milvus, etc.)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_1","uri":"capability://tool.use.integration.local.vector.embedding.via.ollama.rest.api","name":"local vector embedding via ollama rest api","description":"Exposes embedding generation through a standardized REST API endpoint (POST /api/embeddings) that accepts JSON payloads with text input and returns JSON arrays of embedding vectors. The API abstracts the underlying transformer inference, handling tokenization, padding, and vector normalization transparently. Supports streaming and batch processing patterns through standard HTTP semantics, integrating seamlessly with vector databases, LLM frameworks, and custom applications without SDK dependencies.","intents":["I want to call an embedding API from any HTTP client without language-specific SDKs","I need to integrate embeddings into a microservice architecture via REST","I want to batch-process thousands of documents through a standard HTTP endpoint","I need to embed text from a web application or non-Python environment"],"best_for":["Backend engineers building polyglot systems (Node.js, Go, Java, etc.)","DevOps teams deploying embeddings as a containerized microservice","Web developers integrating embeddings from browser-based applications","Data engineers building ETL pipelines that require HTTP-based embedding services"],"limitations":["REST API adds network latency compared to in-process library calls — typical round-trip ~50-200ms depending on hardware","Requires Ollama service to be running and accessible at configured endpoint — no built-in failover or load balancing","No authentication mechanism documented — assumes trusted network or requires external reverse proxy for security","Batch processing not explicitly documented — may require sequential requests or custom batching logic in client code","Response payload size grows linearly with embedding dimensionality and batch size — potential memory constraints for large-scale operations"],"requires":["Ollama service running locally (localhost:11434) or accessible at configured URL","HTTP client library (curl, requests, fetch, etc.)","JSON serialization/deserialization capability in calling application","Network connectivity to Ollama endpoint"],"input_types":["JSON payload with 'prompt' field containing text string"],"output_types":["JSON response with 'embedding' field containing numerical array","HTTP status codes (200 for success, 4xx/5xx for errors)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_10","uri":"capability://search.retrieval.recommendation.and.content.discovery.via.embedding.similarity","name":"recommendation and content discovery via embedding similarity","description":"Embeddings enable content recommendation by finding semantically similar items (documents, articles, products, etc.) to a user's current selection. Given a user's viewed/liked item, the system embeds it, searches the vector index for similar items, and recommends top-k results. This approach captures semantic relevance (e.g., recommending articles on related topics) without explicit collaborative filtering or user behavior tracking. Applications include: article recommendations, related product suggestions, similar document discovery, content discovery feeds.","intents":["I want to recommend articles similar to one a user is reading","I need to suggest related products based on current item","I want to build a content discovery system based on semantic similarity","I need to find related documents in a knowledge base"],"best_for":["Content platforms (news, blogs, documentation) building recommendation features","E-commerce teams implementing product recommendation systems","Knowledge base platforms suggesting related articles","Developers building content discovery features without ML expertise"],"limitations":["Recommendations based solely on semantic similarity — ignores user preferences, popularity, or engagement signals","Cold-start problem for new items — no embeddings until item is indexed","Diversity not built-in — top-k similar items may all be from same topic cluster","Embedding model bias propagates to recommendations — systematic biases in training data affect suggestion quality","No personalization — same item always recommends same neighbors regardless of user context"],"requires":["Vector database indexed with all recommendable items","Embedding model compatible with item content (text, descriptions, etc.)","Application code to retrieve and rank recommendations","Optional: diversity or filtering logic to improve recommendation quality"],"input_types":["current item (text or embedding)"],"output_types":["ranked list of similar items with similarity scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_2","uri":"capability://tool.use.integration.language.agnostic.embedding.sdk.integration.python.javascript.go","name":"language-agnostic embedding sdk integration (python, javascript, go)","description":"Provides native client libraries for Python (ollama.embeddings), JavaScript/Node.js (ollama.embed), and Go that abstract REST API calls and handle request/response serialization. SDKs manage connection pooling, error handling, and response parsing, allowing developers to embed text with single function calls. Libraries expose consistent interfaces across languages while delegating actual inference to the local Ollama runtime, enabling rapid prototyping in preferred languages without learning REST semantics.","intents":["I want to embed text in Python without managing HTTP requests manually","I need to integrate embeddings into a Node.js application with minimal boilerplate","I want to use embeddings in a Go backend service with type-safe interfaces","I need consistent embedding APIs across multiple language codebases"],"best_for":["Python developers building RAG applications with LangChain, LlamaIndex, or custom frameworks","Node.js/TypeScript teams integrating embeddings into full-stack applications","Go backend engineers deploying embeddings in high-performance services","Teams with polyglot codebases requiring consistent embedding interfaces"],"limitations":["SDK maturity and feature parity unknown — Python SDK may have different capabilities than JavaScript or Go implementations","No async/await patterns documented for JavaScript SDK — may block event loop on large embedding operations","Error handling and retry logic not specified — applications must implement custom resilience patterns","Connection pooling and concurrency limits not documented — high-concurrency scenarios may require tuning","SDK versions must be kept in sync with Ollama runtime version — version mismatches may cause compatibility issues"],"requires":["Python 3.7+ (for Python SDK) OR Node.js 14+ (for JavaScript SDK) OR Go 1.16+ (for Go SDK)","Ollama runtime 0.1.26+ running and accessible","SDK package installed via pip (Python), npm (JavaScript), or go get (Go)","Network connectivity to Ollama endpoint"],"input_types":["text strings (Python: str, JavaScript: string, Go: string)"],"output_types":["embedding vectors as native arrays (Python: list/numpy array, JavaScript: array, Go: []float64)","structured responses with metadata (model name, embedding dimensions, etc.)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_3","uri":"capability://tool.use.integration.cloud.hosted.embedding.inference.via.ollama.cloud","name":"cloud-hosted embedding inference via ollama cloud","description":"Deploys the Nomic Embed Text model on Ollama's managed cloud infrastructure, eliminating local hardware requirements and providing auto-scaling, uptime guarantees, and usage monitoring. Cloud deployment uses the same API contract as local Ollama (REST endpoint, SDK integration) but routes requests to Ollama's servers instead of local hardware. Pricing tiers (Free/Pro/Max) control concurrent sessions, weekly request limits, and feature access, enabling pay-as-you-go embedding without infrastructure management.","intents":["I want to use embeddings without managing local GPU or CPU infrastructure","I need auto-scaling embeddings for variable traffic patterns","I want to avoid local hardware costs while maintaining API compatibility","I need embedding service uptime guarantees and monitoring dashboards"],"best_for":["Startups and small teams without dedicated infrastructure budgets","Applications with variable embedding workloads that benefit from auto-scaling","Teams prioritizing operational simplicity over cost optimization","Developers prototyping embedding-based features before committing to infrastructure"],"limitations":["Pricing tiers impose hard limits on concurrent sessions and weekly requests — production workloads may require Pro or Max tier with associated costs","Free tier limits not specified in provided materials — actual usage constraints unknown without documentation review","Network latency higher than local inference — cloud round-trip typically 100-500ms depending on geographic distance and network conditions","Data transmission to Ollama's servers — may violate privacy requirements for sensitive documents (HIPAA, PCI-DSS, etc.)","Vendor lock-in risk — migrating from Ollama Cloud to self-hosted requires code changes if API contract diverges"],"requires":["Ollama Cloud account (free signup at ollama.com)","API key or authentication token for cloud endpoint","Internet connectivity to Ollama Cloud servers","Acceptance of Ollama's terms of service and data handling policies"],"input_types":["text strings via REST API or SDK"],"output_types":["embedding vectors via REST API or SDK","usage metrics and billing information via dashboard"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_4","uri":"capability://memory.knowledge.vector.database.integration.for.semantic.search.indexing","name":"vector database integration for semantic search indexing","description":"Embeddings generated by Nomic Embed Text are compatible with major vector databases (Pinecone, Weaviate, Milvus, Chroma, Qdrant, etc.) that store and index embeddings for fast similarity search. The model outputs fixed-dimensional vectors that can be directly inserted into vector stores without transformation, enabling approximate nearest-neighbor (ANN) search with sub-millisecond latency on large document collections. Integration typically involves: (1) batch embedding documents, (2) upserting vectors with metadata into vector store, (3) querying with embedded search terms to retrieve top-k similar results.","intents":["I want to index millions of documents for semantic search without full-text indexing","I need to build a knowledge base that retrieves relevant context for LLM prompts","I want to implement similarity-based recommendations or document clustering","I need to search across documents in multiple languages or domains simultaneously"],"best_for":["Data engineers building large-scale RAG systems with millions of documents","ML teams implementing semantic search for knowledge bases or documentation","Developers building recommendation engines based on content similarity","Organizations migrating from keyword search to semantic search infrastructure"],"limitations":["Vector database selection and configuration is application responsibility — Nomic Embed Text provides only embedding generation, not database management","Embedding dimensionality must match vector database schema — unknown dimensionality requires reverse-engineering or consulting model card","Semantic search quality depends on document chunking strategy — poor chunking (too long/short) degrades retrieval relevance","Vector databases require separate infrastructure, monitoring, and backup strategies — adds operational complexity beyond embedding model","Embedding updates require re-indexing — changing models or retraining necessitates full vector store refresh, which is expensive at scale"],"requires":["Vector database instance (Pinecone, Weaviate, Milvus, Chroma, Qdrant, etc.) deployed and accessible","Database schema configured with matching embedding dimensionality","Batch embedding pipeline to process documents before insertion","Application code to handle upsert operations and query execution"],"input_types":["text documents or passages to be embedded and indexed"],"output_types":["vector embeddings inserted into database","similarity search results (top-k documents with scores)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_5","uri":"capability://data.processing.analysis.batch.embedding.processing.for.document.collections","name":"batch embedding processing for document collections","description":"Processes multiple text inputs sequentially or in batches through the embedding model, generating vectors for entire document collections without individual API calls. While Ollama's REST API and SDKs don't explicitly document batch endpoints, applications can implement batching by: (1) collecting multiple texts, (2) issuing parallel requests to the embedding endpoint, (3) aggregating results. The 137M-parameter model size enables CPU-based inference for batch processing without GPU constraints, making large-scale embedding feasible on commodity hardware.","intents":["I need to embed thousands of documents for initial vector database population","I want to process document collections efficiently without per-document API overhead","I need to re-embed documents after model updates or fine-tuning","I want to parallelize embedding across multiple CPU cores or machines"],"best_for":["Data engineers performing one-time bulk embedding of document collections","Teams building ETL pipelines that embed documents as part of data ingestion","Researchers comparing embedding quality across document sets","Organizations migrating from keyword search to semantic search at scale"],"limitations":["Batch processing not natively supported by Ollama API — requires custom client-side batching logic or external orchestration","Parallel request handling depends on Ollama server configuration — default settings may not support high concurrency","Memory usage scales with batch size — very large batches may cause OOM errors on resource-constrained systems","No built-in progress tracking or fault tolerance — failed requests require manual retry logic","Embedding latency per document unknown — throughput characteristics must be benchmarked for specific hardware"],"requires":["Ollama runtime with sufficient concurrency configuration","Client application with batching logic (custom code or framework like LangChain)","Sufficient memory for batch accumulation before API submission","Document collection in accessible format (files, database, etc.)"],"input_types":["collections of text documents or passages"],"output_types":["arrays of embedding vectors corresponding to input documents","metadata mapping documents to embeddings for database insertion"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_6","uri":"capability://memory.knowledge.multi.language.semantic.search.language.support.unknown","name":"multi-language semantic search (language support unknown)","description":"The model is intended to support semantic search across text in multiple languages, enabling cross-lingual document retrieval and similarity matching. However, specific language support is not documented in provided materials. The embedding space presumably maps semantically equivalent phrases across languages to nearby vectors, enabling queries in one language to retrieve documents in others. Actual language coverage and cross-lingual performance characteristics require consultation of the HuggingFace model card or empirical testing.","intents":["I want to search across documents in multiple languages with a single query","I need to find semantically similar content regardless of language","I want to build a multilingual knowledge base with unified search","I need to match user queries in one language against documents in others"],"best_for":["Global organizations with multilingual document collections","Teams building international applications requiring cross-lingual search","Researchers studying multilingual semantic similarity","Content platforms serving users in multiple languages"],"limitations":["Language support not documented — actual supported languages unknown without model card review","Cross-lingual performance likely degrades compared to single-language embeddings — no benchmarks provided","Language detection not built into embedding model — applications must handle language identification separately","Mixing languages in single documents may produce suboptimal embeddings — chunking strategy must account for language boundaries","Character encoding and script support unknown — may have issues with non-Latin scripts or special characters"],"requires":["Text input in supported languages (specific list unknown)","Potential language detection preprocessing in application code","Vector database supporting multilingual queries"],"input_types":["text in multiple languages"],"output_types":["embeddings in shared vector space enabling cross-lingual similarity"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_7","uri":"capability://memory.knowledge.rag.context.retrieval.for.llm.prompt.augmentation","name":"rag context retrieval for llm prompt augmentation","description":"Embeddings enable retrieval-augmented generation (RAG) workflows where user queries are embedded, matched against a vector index of documents, and top-k results are injected into LLM prompts as context. The embedding model serves as the retrieval component, enabling LLMs to access external knowledge without fine-tuning. Typical workflow: (1) user query → embedding, (2) similarity search in vector database, (3) retrieve top-k documents, (4) format documents into prompt context, (5) send augmented prompt to LLM. This pattern reduces hallucination and enables knowledge cutoff updates without model retraining.","intents":["I want to augment LLM responses with relevant documents from a knowledge base","I need to reduce hallucination by grounding LLM answers in retrieved context","I want to build a question-answering system over custom documents","I need to update knowledge without retraining the language model"],"best_for":["Teams building question-answering systems over proprietary documents","Developers implementing chatbots grounded in knowledge bases","Organizations reducing LLM hallucination through context injection","Researchers exploring retrieval-augmented generation architectures"],"limitations":["Retrieval quality depends on embedding model and document chunking — poor chunks or weak embeddings degrade RAG performance","Context window limits how much retrieved context can fit in LLM prompt — trade-off between retrieval relevance and context size","No ranking or relevance filtering beyond similarity score — all top-k results included regardless of actual relevance","Embedding model must be compatible with LLM's knowledge domain — generic embeddings may miss domain-specific semantics","RAG latency includes embedding query + vector search + LLM inference — total latency typically 500ms-2s depending on components"],"requires":["Vector database populated with embedded documents","LLM for prompt completion (local or cloud-based)","Application code orchestrating retrieval → prompt formatting → LLM call","Document collection with appropriate chunking strategy"],"input_types":["user queries (text strings)"],"output_types":["LLM responses augmented with retrieved context","citations or references to source documents"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_8","uri":"capability://data.processing.analysis.document.similarity.and.clustering.analysis","name":"document similarity and clustering analysis","description":"Embeddings enable unsupervised document analysis by computing pairwise similarity scores (cosine distance, Euclidean distance) between embedded documents and performing clustering (k-means, hierarchical clustering, DBSCAN) in embedding space. This capability supports exploratory analysis of document collections without labeled training data. Applications include: (1) identifying duplicate or near-duplicate documents, (2) discovering document clusters by topic, (3) analyzing semantic drift in document collections over time, (4) finding outlier documents with unusual semantic properties.","intents":["I want to find duplicate or near-duplicate documents in a collection","I need to automatically cluster documents by topic without manual labeling","I want to analyze semantic similarity patterns in my document collection","I need to identify outlier documents that don't fit common themes"],"best_for":["Data scientists performing exploratory analysis of document collections","Content teams identifying duplicate or similar articles","Researchers analyzing semantic structure of large text corpora","Organizations deduplicating documents before indexing"],"limitations":["Clustering quality depends on embedding model and distance metric — no single best approach for all domains","Computational complexity grows quadratically with document count — pairwise similarity computation infeasible for millions of documents","Cluster interpretation requires domain expertise — embeddings don't provide human-readable explanations of similarity","Hyperparameter tuning (number of clusters, distance thresholds) requires experimentation — no automatic selection","Embedding model bias may propagate to clustering results — systematic biases in training data affect document groupings"],"requires":["Document collection embedded using Nomic Embed Text","Clustering library (scikit-learn, scipy, etc.) for analysis","Visualization tools for exploring clusters (matplotlib, plotly, etc.)","Computational resources for pairwise similarity computation"],"input_types":["collections of embedded documents (vectors)"],"output_types":["similarity matrices (pairwise distances)","cluster assignments (document → cluster ID)","cluster centroids and statistics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-nomic-embed-text__cap_9","uri":"capability://data.processing.analysis.semantic.deduplication.and.near.duplicate.detection","name":"semantic deduplication and near-duplicate detection","description":"Uses embeddings to identify duplicate or near-duplicate documents by computing similarity scores and applying thresholds. Unlike lexical deduplication (which requires exact or near-exact string matches), semantic deduplication finds documents with equivalent meaning despite different wording. Process: (1) embed all documents, (2) compute pairwise similarities, (3) apply threshold (e.g., cosine similarity > 0.95), (4) identify and remove duplicates. This approach handles paraphrasing, summarization, and translation variants that lexical methods miss.","intents":["I want to remove duplicate documents from a collection before indexing","I need to find paraphrased or summarized versions of the same content","I want to identify documents that are translations of each other","I need to clean a dataset by removing near-duplicate entries"],"best_for":["Data engineers cleaning document collections before RAG indexing","Content platforms deduplicating user-generated content","Research teams preparing datasets for analysis","Organizations consolidating documents from multiple sources"],"limitations":["Similarity threshold selection is domain-dependent — no universal threshold works for all document types","Computational complexity O(n²) for pairwise comparison — infeasible for millions of documents without approximation","False positives/negatives depend on embedding model quality — weak embeddings may miss true duplicates or flag dissimilar documents","Deduplication is lossy — removing documents may discard valuable variations or context","Threshold tuning requires manual validation — no automatic method to determine optimal similarity cutoff"],"requires":["Document collection embedded using Nomic Embed Text","Similarity computation library (numpy, scipy, etc.)","Threshold selection strategy (manual tuning, statistical analysis, etc.)","Computational resources for pairwise similarity computation"],"input_types":["collections of embedded documents"],"output_types":["duplicate groups (sets of similar documents)","deduplicated document collection","similarity scores for each pair"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime 0.1.26 or later (local or cloud deployment)","Text input as string (no binary or structured data support)","Network connectivity to Ollama API endpoint (localhost:11434 for local, cloud URL for managed)","Sufficient disk space for 274MB model download and storage","Ollama service running locally (localhost:11434) or accessible at configured URL","HTTP client library (curl, requests, fetch, etc.)","JSON serialization/deserialization capability in calling application","Network connectivity to Ollama endpoint","Vector database indexed with all recommendable items","Embedding model compatible with item content (text, descriptions, etc.)"],"failure_modes":["Context window limited to 2,048 tokens — longer documents must be chunked before embedding","Embedding dimensionality not documented in provided materials — integration requires reverse-engineering or consulting HuggingFace model card","No fine-tuning capability exposed — cannot adapt embeddings to domain-specific vocabulary or tasks","Single-purpose model — cannot be repurposed for text generation, classification, or other downstream tasks","Inference latency and throughput benchmarks not provided — performance characteristics unknown without benchmarking","REST API adds network latency compared to in-process library calls — typical round-trip ~50-200ms depending on hardware","Requires Ollama service to be running and accessible at configured endpoint — no built-in failover or load balancing","No authentication mechanism documented — assumes trusted network or requires external reverse proxy for security","Batch processing not explicitly documented — may require sequential requests or custom batching logic in client code","Response payload size grows linearly with embedding dimensionality and batch size — potential memory constraints for large-scale operations","builder identity is not verified yet","no observed match outcomes 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