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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Answer natural language questions about document content through a chat interface. Users can ask follow-up questions and receive contextual answers extracted from the document without hallucination.
Analyze and extract insights from documents in 50+ languages while preserving original formatting, table structures, and language-specific formatting. Handles true multilingual content, not just surface-level translation.
Process multiple documents in sequence or bulk, extracting insights and answers across a document collection without manual per-document handling.
Extract information from documents using a 'deduce' methodology that reduces confident fabrication and false information compared to standard LLM approaches.
Extract and maintain complex document structures including tables, formatting, and layout during analysis and extraction processes.
Access core document analysis and question-answering capabilities without credit card or payment, enabling testing and light-duty workflows at no cost.
Search within analyzed documents and retrieve specific passages or answers from large document collections.
Specialized analysis of legal contracts, agreements, and compliance documents to identify clauses, risks, obligations, and key terms.
+1 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 MapDeduce at 47/100. MapDeduce leads on ecosystem, while Pinecone is stronger on adoption and quality.
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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