FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology. Our solution is purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments.
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
FalkorDB's answer
C, Rust, Next.js
FalkorDB's answer
An ultra-low latency Graph Database
FalkorDB's answer
x100 faster than the leading solutions
FalkorDB's answer
Developers, Architects, Data scientists, CTOs
FalkorDB's answer
An ultra-low latency Graph Database that perfects the Knowledge Graph for KG-RAG. Effectively overcoming the existing limitations of RAG for Large Language Models (LLM).
FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph.
Based on our record, Redis seems to be a lot more popular than FalkorDB. While we know about 216 links to Redis, we've tracked only 3 mentions of FalkorDB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Use a low-latency graph database: Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions. - Source: dev.to / about 2 months ago
In vector databases, data is stored as high-dimensional vector embeddings, which are numerical representations generated by machine learning models to capture the features of data. When querying, the input is converted into a vector embedding, and similarity searches are performed between the query vector and stored embeddings using distance metrics like cosine similarity or Euclidean distance to retrieve the most... - Source: dev.to / about 2 months ago
For AI architects, integrating graph-native storage with LLMs isn’t optional—it’s imperative for building systems capable of robust, multi-hop reasoning at scale. - Source: dev.to / 2 months ago
Of course, these examples are just toys. A more proper use for asynchronous generators is handling things like reading files, accessing network services, and calling slow running things like AI models. So, I'm going to use an asynchronous generator to access a networked service. That service is Redis and we'll be using Node Redis and Redis Query Engine to find Bigfoot. - Source: dev.to / about 1 hour ago
Slap on some Redis, sprinkle in a few set() calls, and boom—10x faster responses. - Source: dev.to / about 1 hour ago
Real-time serving: Many push processed data into low-latency serving layers like Redis to power applications needing instant responses (think fraud detection, live recommendations, financial dashboards). - Source: dev.to / 13 days ago
Redis® Cluster is a fully distributed implementation with automated sharding capabilities (horizontal scaling capabilities), designed for high performance and linear scaling up to 1000 nodes. . - Source: dev.to / about 1 month ago
Instead of spinning up Redis, use an unlogged table in PostgreSQL for fast, ephemeral storage. - Source: dev.to / about 2 months ago
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