Guides
Vendor-neutral, engineer-written guides to the concepts behind multimodal AI: perception, retrieval, embeddings, and the infrastructure agents use to see, hear, and search unstructured data. Learn the idea first; then see how Mixpeek applies it.
141 guides across 21 topics
Why Do I Get Different Search Results Every Time I Run the Same Query?
Almost every vector search engine is approximate: it walks a fraction of the index instead of comparing your query against every vector, and the fraction it walks can differ between runs. Five things produce run-to-run variation, they leave different fingerprints, and telling them apart takes about ten minutes. This is how to find which one you have and what to change.
Why Does My Image Search Return Similar-Looking Items Instead of the Exact One?
An embedding model is trained to place things that look alike near each other, so a different shoe in the same colourway scores almost as well as the shoe you photographed. Similarity and identity are different questions. This explains why one embedding cannot answer both, how to tell which one you are asking, and what to add so the exact item comes back first.
How Do I Find Out Where a Video Came From?
Someone sent you a clip and you want to know who filmed it, when, and whether it is what it claims to be. Reverse image search on a frame is the first move and it fails more often on video than people expect. This covers what each method actually catches, how to pull the right frames, how to spot recycled footage and AI-generated video, and what to do when the clip is cropped or re-encoded.
Why Do My Photos Have No Date or Location, and Can I Get It Back?
Almost every platform strips EXIF when you upload, so the photo you downloaded back has no date, no GPS and no camera. This explains which fields survive, which are gone for good, how to tell a stripped file from one that never had the data, and what you can still recover from the picture itself.
Why Can't My Search Find Exact Part Numbers, Codes or Names?
Semantic search compares meanings, and a part number, an error code or an unusual surname carries almost none, so the index returns records that look alike instead of the one that matches. Run a keyword (BM25) search beside the vector search, merge the two lists by rank, and use an exact filter when the identifier lives in its own field.
Multi-Index Search Architecture: How to Combine Visual, Audio, and Text Embeddings for Rich Media
A systems-design guide to building search over rich media by decomposing assets into multiple feature streams, storing them in separate indexes, routing queries, and fusing scores. Covers index-per-modality vs. fused-space design, RRF and weighted fusion, query routing heuristics, and production trade-offs.
Object Decomposition and Layered Indexing for AI Agent Perception
A practical architecture guide for turning video, audio, images, and documents into searchable evidence that agents can inspect, cite, filter, and reason over.
Hybrid Search Fusion: How to Combine Dense and Lexical Retrieval Without Breaking Ranking
An agent searching transcripts, OCR text, and captions needs both meaning (dense vectors) and exact terms (BM25), but the two return scores on incompatible scales that you cannot simply add. This guide teaches the real fusion mechanics: why score distributions make naive normalization fail, the exact math of Reciprocal Rank Fusion and how its k parameter behaves, weighted convex combination with proper normalization, and how to choose and tune a fusion method against a labeled set.
BM25 and the Inverted Index: The Lexical Retriever Every Hybrid Search Treats as a Black Box
Every hybrid search pipeline pairs dense vectors with BM25, but almost no one can say where the BM25 number actually comes from, which is exactly why fusion, tuning, and exact-match failures stay mysterious. This guide opens the box: how an inverted index turns transcripts and OCR text into posting lists, the precise BM25 scoring formula with its term-frequency saturation and length normalization, what the k1 and b parameters really do, and why the tokenizer is the silent decider of whether an agent ever finds a serial number.
Why Do My Search Results Get Worse as I Add More Data?
Search that worked on ten thousand items and fails on ten million usually has one of five causes: a candidate budget that is a count rather than a fraction, near-duplicates filling the top ten, a coarse index trained on data that no longer looks like yours, a score floor calibrated on a smaller corpus, or a filter starving the candidate pool. This explains each mechanism, the single test that identifies it, and the order to run the tests in.
Marengo 3.0 on Amazon Bedrock: Architecture, Benchmarks and Vector Storage
TwelveLabs Marengo Embed 3.0 is generally available as an embedding model in Amazon Bedrock Knowledge Bases. What the managed path gives you, what it takes away, and when to hold the vectors yourself. Includes the 1024 to 512 dimension change that breaks an in-place upgrade.
Why Does My Search Only Work in One Language?
Search over your media library works in English, then a user types the same question in Japanese and gets nothing, or gets confident nonsense. Three separate causes produce that. Each leaves a different fingerprint in the results, and a better embedding model fixes only one of them.
All guides
Every guide in one place, 141 in total.
- Why Do I Get Different Search Results Every Time I Run the Same Query?
- Why Does My Image Search Return Similar-Looking Items Instead of the Exact One?
- How Do I Find Out Where a Video Came From?
- Why Do My Photos Have No Date or Location, and Can I Get It Back?
- Why Can't My Search Find Exact Part Numbers, Codes or Names?
- Why Do My Search Results Get Worse as I Add More Data?
- Why Your Video Search Can't Find Words That Appear On Screen
- How to Prove Who Changed a Configuration, When, and Why
- Why Does My Search Find the Right Document but the Wrong Part of It?
- How Do You Search a 3D World You Generated?
- Search Every World You Generate with Atlas
- Search Every World You Generate with Runway
- Search Everything You Generate on fal
- Search Every Image You Generate with ChatGPT
- Why Does My Podcast Search Find the Right Episode but Not the Moment?
- Why Does My Search Only Work in One Language?
- Search Every Conversation You Have with GPT Live
- What Is a Semantic Contract? The Interface Between Extraction and Retrieval
- What Does a Semantic Layer Compile To When the Target Is Not SQL?
- How Should an AI Agent Choose Between Retrieval Capabilities?
- Should Semantic Features Be Materialized or Computed at Query Time?
- How Do You Model Unstructured Data From Raw Objects to Serving Contracts?
- What a Semantic Layer Looks Like in Production
- Compute Pushdown: Why Your Vector Search Over Object Storage Is Slow
- How to Merge Data Sources With Different Schemas Into One Searchable Index
- Write-Ahead Logs on Object Storage: How S3 Becomes a Database's Durability Layer
- Query Planning for Multimodal Retrieval: Stage Order, Filter Pushdown, and Candidate Budgets
- Multi-Stage Retrieval: Retrieve, Filter, Rerank
- Change Data Capture for a Search Index: Keeping Downstream Systems in Sync
- Query Rewriting, Expansion and HyDE: Fixing Retrieval Before It Searches
- Cross-Lingual Retrieval: Searching Content in Languages You Did Not Query In
- C2PA Content Credentials: How Media Provenance Actually Works
- How Do You Detect and Redact PII in Images, Video, and Audio?
- How Do You Delete Data From a Vector Index? Embeddings, Tombstones, and the Right to Be Forgotten
- Do You Need a Vector Database? When Brute Force, pgvector, and a Dedicated Store Each Win
- How to Build a Taxonomy From Unlabeled Data: Clustering, Labeling, and Promotion
- What Is Hybrid Search? BM25, Vector Retrieval, and How to Fuse Their Rankings
- What Is MUVERA? Turning Multi-Vector Retrieval Into a Single-Vector Search
- How Do You Run Your Own Model Inside a Managed Search Pipeline?
- How Do You Isolate Tenants in a Vector Index?
- What Does It Cost to Make a Video Library Searchable?
- How to Search, Deduplicate, and Moderate AI-Generated Video and Images (FLUX 3, Kling, Veo, Runway)
- How Does LoRA Fine-Tuning Work? (Adapters, QLoRA, DoRA, and Fine-Tuning Retrieval Models)
- How Do I Evaluate a RAG Pipeline? (Faithfulness, Answer Relevance, Hallucination, and Context Metrics)
- How Do I Ingest Millions of Files into a Search Index? (Images, Video, Documents at Scale)
- How Do AI Agents Search Big Datasets by Navigating Clusters? (Hierarchical Cluster Search)
- What Is Composite Clustering? Clustering Across Multiple Feature Spaces (and Clusters of Clusters)
- How Do I Filter Vector Search Results by Location? (Radius, Bounding Box, Polygon)
- How Do I Automatically Classify Content Against a Taxonomy?
- Brand Safety vs Brand Suitability: How AI Classifies Video for Advertisers
- How Do I Debug Bad Retrieval Results in RAG and Vector Search?
- Can You Run a Vector Database on S3? Object-Storage-Backed Vector Search, Explained
- How Do I Build a Deep Research Agent Over My Own Data?
- What Concepts Exist in My Data That Nobody Has Labeled Yet?
- Marengo 3.0 on Amazon Bedrock: Architecture, Benchmarks and Vector Storage
- How to Store TwelveLabs Marengo Embeddings in Your Own Vector Store
- Video Highlight Detection: How AI Finds the Best Moments
- How Much Does a Vector Database Cost? A 2026 Pricing Comparison
- How to Switch Embedding Models Without Re-Embedding Everything
- Reverse Video Search: How It Works and How to Build One
- Semantic Caching: How Agents Skip Work They Have Already Done
- Efficient Attention: How Models Read Hour-Long Video and Book-Length Documents
- Optical Context Compression: Reading Documents as Images, Not Text
- Multi-Object Tracking: How Agents Follow Objects Across Video Frames
- Monocular Depth Estimation: How Models Infer 3D From a Single Image
- Instance-Level Visual Matching: Finding the Same Object, Not Just Similar Ones
- Face Recognition and Identity Clustering: How Agents Recognize and Group People in Video
- Reasoning Rerankers: How Listwise LLM Rerankers Reorder Retrieval Results
- Retrieval Feedback Loops: Learning to Rank from Clicks, Outcomes, and Agent Interactions
- Matryoshka Representation Learning: Nested Embeddings for Adaptive Multimodal Retrieval
- Creative Ad Analysis for AI Agents: JEPA, Multi-Vector Retrieval, and Signal Fusion
- How to Check if an Image Is Copyrighted
- How Do I Find Out If Someone Reposted My Video?
- How to Check if a Video Is Copyrighted
- How to Check if a Song Is Copyrighted on YouTube
- How to Check if a Picture Is Copyrighted Before Using It
- The Complete Guide to Pre-Publication IP Clearance
- What Is a Multimodal Data Warehouse?
- How to Build a Multimodal Data Warehouse
- Multimodal Data Warehouse Architecture Deep Dive
- Vector Storage Tiering: The Architecture Guide to Hot, Warm, and Cold Vector Data
- How to Build MCP Tools for Multimodal AI Agents
- Embedding Portability and Versioning: Why Your Vectors Are Not as Portable as You Think
- How to Build a Multimodal RAG Pipeline
- Context Engineering for AI Agents
- Video Temporal Grounding: How AI Agents Find Specific Moments in Video
- Multimodal Perception for AI Agents: How to Give Your Agent Eyes, Ears, and Memory
- Open-Vocabulary Object Detection: Teaching AI to Find Anything You Describe
- Audio Feature Extraction: How AI Agents Learn to Hear
- Contrastive Learning: How CLIP, SigLIP, and CLAP Actually Work
- Multi-Stage Retrieval: How AI Agents Search Unstructured Data at Scale
- Visual Document Retrieval: How AI Agents Search Documents Without OCR
- Video Scene Segmentation: How AI Decomposes Continuous Video into Searchable Segments
- Omnimodal Embeddings: One Model for Text, Image, Audio, and Video Retrieval
- Speaker Diarization: How AI Agents Know Who Said What in Audio and Video
- Cross-Encoder Reranking: Why Two-Stage Retrieval Beats One-Stage Search
- MCP Tool Design for Multimodal Search
- Long-Context Video Understanding for Agent Perception
- Structured Extraction from Unstructured Documents: How Vision-Language Models Replace OCR Pipelines
- Late Interaction Retrieval: How ColBERT, ColPali, and ColQwen Search Without Losing Token-Level Detail
- Agentic Retrieval: How AI Agents Search Differently Than Humans
- Embedding Quantization & Compression: Making Vector Search Practical at Billion Scale
- Video RAG: Building Retrieval-Augmented Generation Over Video Corpora
- Evaluating Multimodal Retrieval: Metrics, Benchmarks, and Ground Truth
- Multimodal Chunking Strategies: How to Decompose Video, Audio, Images, and Documents for Search
- How to Build a Video Perception Layer for AI Agents
- Approximate Nearest Neighbor Search: The Algorithms Behind Fast Vector Retrieval
- Instruction-Tuned Embeddings: How Task Prompts Transform Retrieval Quality
- Multi-Index Search Architecture: How to Combine Visual, Audio, and Text Embeddings for Rich Media
- Embedding Space Geometry: Why Cosine Similarity Doesn't Always Mean What You Think
- Object Decomposition and Layered Indexing for AI Agent Perception
- Retrieval Control Planes for AI Agents: Streaming, Cancellation, and Budgets
- Video Anomaly Detection for AI Agents: From Signals to Searchable Events
- Agent Perception Evals: Testing Whether AI Agents Can See, Hear, and Search
- Audio-Visual Retrieval for AI Agents: How to Search What Happened, Not Just What Was Said
- Computer-Use Agent Memory: How to Search Screens, Tools, and UI State
- Production Ingestion Reliability for Agent Perception: Ledgers, Backfills, and Recall Checks
- Mask-Aware Retrieval for AI Agents: Segment First, Search Crops, Then Reason
- Forced Alignment for AI Agents: Word Timestamps, Diarization, and Audio Evidence Search
- Payload Projection for Agentic Vector Search: Field Selection, Evidence Handles, and Context Budgets
- Adaptive Indexing for Agentic Search: Query Logs, Payload Indexes, and Retrieval Routing
- Video Frame Sampling: How Many Frames to Embed and Which Ones to Keep
- Calibrating Similarity Scores: What Cosine Similarity Actually Means for Retrieval
- Chunk Contextualization: How Late Chunking and Contextual Retrieval Fix Broken RAG
- Budget-Aware Multi-Vector Retrieval: How to Make Late Interaction Affordable at Scale
- Diversity-Aware Retrieval: How Agents Avoid Twenty Near-Duplicate Results
- Query Transformation Pipelines: Turning a Raw Agent Question Into Searchable Intent
- Learned Sparse Retrieval and Dense-Sparse Hybrid: Why Agents Need Both Vocabularies
- The Modality Gap: Why Text-to-Image Search Underperforms and How Agents Fix It
- How OCR Actually Works: Detection, Recognition, Reading Order, and Tables
- How ASR Decoding Actually Works: Beam Search, LM Fusion, and Confidence
- Index Freshness and Incremental Updates: How Just-Ingested Content Becomes Searchable
- Filtered Vector Search: How Agents Combine Similarity with Hard Constraints
- How Vision-Language Models Fuse Image and Text Tokens
- Hybrid Search Fusion: How to Combine Dense and Lexical Retrieval Without Breaking Ranking
- Audio Fingerprinting: How Agents Recognize a Specific Recording in Noise
- Embedding Fine-Tuning and Distillation: Teaching an Agent to See and Hear Your Domain
- BM25 and the Inverted Index: The Lexical Retriever Every Hybrid Search Treats as a Black Box
- Perceptual Image Hashing: How Agents Recognize the Same Picture After It Has Been Re-Encoded, Cropped, and Recolored
- Streaming Video Understanding: How Agents Watch an Unbounded Live Feed in Real Time
- Why Does My Document Search Miss the Answer That Is in a Chart or Table?
From concept to production
These guides explain how multimodal perception and retrieval actually work. Mixpeek is the platform that runs them: point it at your storage and get back relevant, timestamped results.