| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 12 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Automatically extracts text content from PDF documents while preserving structural information like headings, paragraphs, and formatting. Uses vision models to handle scanned PDFs and complex layouts that traditional text extraction tools fail on.
Identifies and extracts tabular data from PDFs and images, converting tables into structured formats like CSV or JSON. Preserves table relationships and cell content accurately even in complex multi-column layouts.
Allows teams to fine-tune parsing models for specialized document types like medical forms, legal contracts, or industry-specific formats. Improves accuracy on custom document types through training.
Analyzes extracted content to assess quality and identify potential issues like incomplete extraction, OCR errors, or structural problems. Provides confidence scores and validation reports.
Performs optical character recognition on image files and scanned documents to extract readable text. Uses vision models to understand document layout and preserve context beyond simple character recognition.
Automatically breaks down large documents into semantically meaningful chunks optimized for LLM processing and vector database storage. Respects document structure to avoid splitting related content.
Automatically identifies and extracts metadata from documents including title, author, creation date, and document type. Classifies documents into categories based on content and structure.
Analyzes document visual layout including spatial relationships between elements, preserving information about positioning, hierarchy, and visual structure. Maintains context that would be lost in simple text extraction.
+4 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Unstructured Technologies at 49/100. Pinecone also has a free tier, making it more accessible.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities