gkorland

10 ms·

1.
by gkorland 3mo ago
If you want to read more about it check https://www.falkordb.com/blog/beyond-in-memory-graphs/
2.
ActiveGraph v1.2.0 is live – x30 speedup(activegraph.ai)
8 points by gkorland 3mo ago|3 comments
3.
by gkorland 3mo ago
ActiveGraph.ai v1.2.0 is live * Graph projection is now pluggable falkordb = native edges + cypher pushdown * 2-hop query: ~8.9s → ~300ms * community-built core arc
4.
by gkorland 3mo ago
graphify is a great project you should try it with FalkorDB. ``` /graphify ./raw --falkordb # generate cypher.txt for FalkorDB /graphify ./raw --falkordb-push falkordb://localhost:6379 ```
5.
by gkorland 7mo ago
Where do you store the Knowledge Graph? Are you supporting any Graph Database like FalkorDB?
6.
by gkorland 7mo ago
Did you consider using a Graph Database? e.g. FalkorDB? If you want to keep it lite we released a lite version that can run embedded in your nodejs.
7.
by gkorland 8mo ago
That’s exactly why we released FalkorDBLite,an embedded graph engine designed for local, edge, and GenAI use cases (GraphRAG, agentic memory, semantic layers, code graphs, etc.). https://www.falkordb.com/blog/falkordbli
8.
by gkorland 9mo ago
Nice! Do you plan to add support for other Graph Database (e.g. FalkorDB)?
9.
by gkorland 1y ago
We're building an open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Allowing you to ask your database questions in plain English, QueryWeaver handles the "weaving".
10.
by gkorland 1y ago
can you share a link?
11.
by gkorland 1y ago
The fact that GQL is now supported by some of the relational Database, doesn't mean they'll become an alternative to native Graph Databases.
12.
by gkorland 1y ago
You should check FalkorDB https://github.com/falkordb/falkordb
13.
by gkorland 1y ago
Did you try to run redis-benchmark? (Compared to Redis)
14.
by gkorland 1y ago
We built QueryWeaver exactly to solve those pain points—SQL generation that actually understands your business context and keeps the conversation alive across follow-ups. The graph layer is fully extensible, so you can define things like wh
15.
by gkorland 1y ago
You can find the release notes here: https://github.com/FalkorDB/FalkorDB/releases/tag/v4.10.0
16.
by gkorland 1y ago
Much more than that, FalkorDB added many features on top of RedisGraph.
17.
by gkorland 2y ago
Your graph DB frustrations mirror what many experienced with Neo4j. If you refresh your project, consider including FalkorDB (formerly RedisGraph) - it uses sparse adjacency matrices and GraphBLAS for much better performance while supportin
18.
by gkorland 2y ago
The QirK paper is super interesting https://arxiv.org/pdf/2408.07494 . We took that one step further with the GraphRAG-SDK - https://github.com/FalkorDB/GraphRAG-SDK
19.
by gkorland 2y ago
It practically supports every models out there, LiteLLM supports over 100 large language model services, including OpenAI, Claude, Gemini, WatsonsX, Mistral, Azure OpenAI, Sagemaker....
20.
by gkorland 2y ago
We're excited about this version which add support for LiteLLM https://github.com/BerriAI/litellm . With LiteLLM the application can easily switch between different models to pick to most optimized model for Knowle
21.
Show HN: GraphRAG SDK 0.4.0: Simplify RAG with Graph Databases(falkordb.com)
2 points by gkorland 2y ago|3 comments
22.
by gkorland 2y ago
We see three major issues that different projects encounter: 1. Knowledge Graph quality - if you don't have a clean well defined Knowledge Graph then the end result will not be good. 2. Multi Graphs support - you want to break the larg
23.
by gkorland 2y ago
If it's your personal assassinate and is helping you for months it means pretty fast it will start forget the details and only have a vogue view of you and your preferences. So instead of being you personal assassinate it practically c
24.
by gkorland 2y ago
Did you try FalkorDB? If latency is a factor and you consider to store many Knowledge Graphs you should check it out.
25.
by gkorland 2y ago
It looks great! Utilizing Knowledge Graph to store long term memory is probably the most accurate solution compared to using only Vector Store (same as with GraphRAG vs Vector RAG). I think an important thing to point here that long term me
26.
by gkorland 2y ago
When using a graph database you can build a knowledge Graph out of the long term memory. Storing it only in a vector database means that you'll only find things that are similar to the user question and miss a lot of information that i
27.
The Dawn of Agentic AI:Transforming Enterprise Automation and the Future of Work(medium.com)
1 points by gkorland 2y ago|0 comments
28.
by gkorland 2y ago
Do you just hold this number of node in the database or also need to visualize them all in one view?
29.
by gkorland 2y ago
We've done a very similar procedure just with FalkorDB as a Graph Database. Notice if you already have a schema/ontology it might be easier might you might miss some entities in the text you did realize exist. So in our in GraphRA
30.
by gkorland 2y ago
Would love to hear your feedback once you do try GraphRAG.

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