Vector Loader

Load unstructured data into vector databases. Convert documents, images, and multimedia into embeddings for semantic search, RAG pipelines, and AI applications.

Building AI applications requires converting unstructured data (documents, images, audio, video) into embeddings that can be searched semantically. Vector databases like Pinecone, Weaviate, and Qdrant power semantic search and RAG (Retrieval-Augmented Generation) applications. Vector Loader automates the process of loading your data into vector databases.

What Does Vector Loader Do?

This actor processes various data formats, generates embeddings (converting text to semantic vectors), and loads them into vector databases. Perfect for building semantic search systems, RAG applications, and AI-powered knowledge bases.

Key Capabilities:

• Document loading (PDF, DOCX, TXT, Markdown, HTML)
• Chunking strategy implementation (split large documents into searchable chunks)
• Embedding generation (using OpenAI, HuggingFace, or local models)
• Vector database loading (Pinecone, Weaviate, Qdrant, Milvus, Chroma)
• Metadata preservation (store original document metadata with embeddings)
• Batch processing (load 1000s of documents in single run)
• Update and deletion support (modify or remove vectors after loading)
• Duplicate detection (prevent re-loading identical content)

Key Features (8 Features)

1. Multi-Format Support - Load PDFs, images, documents, web pages, videos
2. Smart Chunking - Automatically split documents into optimal semantic chunks
3. Embedding Selection - Choose from 50+ embedding models
4. Vector DB Integration - Native support for major vector databases
5. Metadata Preservation - Keep original document metadata with vectors
6. Batch Operations - Load 1000s of documents efficiently
7. Update Management - Modify or delete vectors without full reload
8. Preprocessing Automation - Auto-clean and normalize text before embedding

How to Use (Step by Step)

Step 1: Prepare Your Data

Gather documents to load:

• PDFs, documents, web pages, images
• Organize in folder or provide URLs
• Optional metadata (source, category, date)

Step 2: Configure Loading Parameters

Specify loading preferences:

• Source data location (folder, URLs, S3, etc.)
• Target vector database (Pinecone, Weaviate, Qdrant)
• Embedding model to use
• Chunking strategy (chunk size, overlap)
• Metadata fields to preserve

Step 3: Run Vector Loader

Execute the actor to load data:

• System processes documents
• Generates embeddings
• Loads into vector database
• Returns loading report with success/failure stats

Step 4: Query Your Vectors

Use your vector database for semantic search:

• Query with natural language
• Get semantically similar results
• Build RAG applications on top
• Feed into AI/LLM applications

Input Parameters (Brief Table)

Parameter	Type	Required	Description
dataSource	string	Yes	Data source (folder, s3, urls, api)
vectorDb	string	Yes	Target vector database (pinecone, weaviate, qdrant)
embeddingModel	string	No	Model for embeddings (openai, huggingface, etc.)
chunkSize	number	No	Characters per chunk (default: 1024)
chunkOverlap	number	No	Overlap between chunks (default: 256)

Output Data (Brief Table)

Field	Type	Description
documentsProcessed	number	Total documents loaded
vectorsCreated	number	Embeddings generated
failedDocuments	array	Documents that failed to load
loadingTime	number	Total time in seconds
vectorDbId	string	ID in target vector database

Pricing & Performance

Cost per operation:

• Small batch (10 documents): $0.10-0.50
• Medium batch (100 documents): $1.00-5.00
• Large batch (1000 documents): $10.00-50.00
• Depends on embedding model and document size

Performance:

• Small batch (10 docs): 1-2 minutes
• Medium batch (100 docs): 5-10 minutes
• Large batch (1000 docs): 30-60 minutes

FAQ (2-3 Questions)

Q: What embedding model should I use? A: OpenAI embeddings are high quality. HuggingFace offers free/cheap local options. Choose based on your budget and performance needs.

Q: Can I update vectors after loading? A: Yes—use delete/update operations. Provide document IDs to modify existing vectors.

Q: Which vector database is best? A: Pinecone (managed, easy), Weaviate (open source, flexible), Qdrant (fast, efficient). Choose based on your infrastructure preferences.

Data Quality & Limitations

• Chunk Size: Affects search quality (smaller = more specific, larger = broader context)
• Embedding Latency: Time to generate embeddings depends on model size
• Database Limits: Check vector DB quotas and limits
• Cost: Embedding generation can be expensive at scale

Integrations & Automation

LLM Applications: Feed vectors into LLMs for RAG.

Search Interfaces: Build semantic search on top of vectors.

Custom Apps: Access vectors via vector DB APIs for custom applications.

Works Great With

• LLM Applications - RAG (Retrieval-Augmented Generation) systems
• Semantic Search - Build search that understands meaning, not just keywords
• Knowledge Bases - AI-powered knowledge management
• Recommendation Systems - Content recommendations based on semantic similarity

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Convert Knowledge Into Vectors. Power AI Applications.

📧 Support · 📚 Documentation · 📡 REST API

Built for AI engineers and RAG application developers.