towhee
node-redis
Our great sponsors
towhee | node-redis | |
---|---|---|
26 | 12 | |
2,989 | 16,681 | |
2.4% | 0.5% | |
8.6 | 7.9 | |
3 months ago | 8 days ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
-
Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
-
What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
-
[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
-
Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
-
A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
-
Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
-
Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
-
A python framework for unstructured data processing
You can check the result from the tutorial.
node-redis
- Vector database built for scalable similarity search
-
JavaScript + Database ?
Probably redis.
-
Superfast search with RediSearch
Did you have an overdose of theory? Let us now taste some code that can help us apply some concepts. This example focuses on the text search. Redis provides us with a straightforward command line interface, along with useful SDK modules in most common languages. Below is a JavaScript code that uses Node Redis module to communicate with the Redis Server. Along with the JavaScript code, we can see the corresponding CLI commands. We need a text-rich dataset to save in our database and demonstrate the search functionality. For this, we will use a dump of poems obtained from Kaggle. The JSON chunk can be found on this link.
-
client is closed
You need to call and await the connect method on your clients before you can send commands. For an example, see the sample usage code in the project README.
-
IP Visualizer, development process or from total jank to less jank ;)
First thing to deal with is getting the data (using the GeoLite2 free geolocation data from MaxMind) into Redis so can actually query it. This was easier said than done. I used the node-redis lib and well, all the geo stuff in this lib are broken af (mildly speaking).
-
Using Redis Cloud in your NextJS application
30 maximum connections may not seem like an issue as long as you are not building an application that has specific requirements for concurrency. This statement could be true if we are establishing connections between a Node server and a Redis cache since it is recommended that only one or two Redis client would be instantiated then reused in the Node server. In this case, there is a limited number of connections (clients are connections in Redis) needed when the server is running and communicating with Redis.
-
Show HN: Postgres.js – Fastest Full-Featured PostgreSQL Client for Node and Deno
> Sure, the c++ is going to require you to do some sanitizing as you force your data into v8
it's not just sanitizing, there's a lot more to the object creation inside v8 itself. but, even if it were just sanitizing, that mechanism has become a lot more complicated than it ever was in v8 3.1 (timeframe around node 0.4) or 3.6 (timeframe around node 0.6). when interacting with c++, v8 makes no assumptions, whereas when interacting with javascript, a large number of assumptions can be made (e.g. which context and isolate is it being executed in, etc).
> but as we noted that's inevitable no matter how you slice it.
yes, from c++ to javascript and back, but when you need to make that trip multiple times, instead of once, that interchange adds up to quite a bit of extra code executed, values transformed, values checked, etc. sure, banging your head against a wall might not hurt once, but do it 40 times in a row and you're bound to be bloodied.
> Now maybe in some cases the v8 internals offer some advantages the generic c++ api can't access
by a fairly large margin, as it turns out, especially as v8 has evolved from the early 3.1 days to the current 9.8: 11 years. there has been significant speedup to javascript dealing with javascript objects compared to c++ dealing with javascript objects. see below.
> My memories of the redis client is different than yours so I'd be quite interested to see those conversations / benchmarks.
super easy to find, all of that was done in public: https://github.com/redis/node-redis/pull/242 - there are multiple benchmarks done by multiple people, and the initial findings were 15-20% speedup, but were improved upon. the speedup was from the decoding of the binary packet, which was passed as a single buffer, as opposed to parsing it externally and passing in each object through the membrane.
> As a simple thought experiment, in the scenario you're describing we should see a javascript implementation of a JSON parser to beat the pants off the v8 engine implementation, but this doesn't seem to the case.
that's a bit of a straw man argument. especially given that JSON.parse() is a single call and does not require any additional tooling/isolates/contexts to execute, it's just straight c++ code with very fast access into the v8 core:
Local result = Local::New(isolate, JSON.Parse(jsonString));
-
Release 0.4: Progressing
I actually found 2 resources which might be useful to help me in setting the ttl expire period for the key: Redis-doc and issue-100 and I wil be dig in to it in couple days to figure it out
-
How to create LinkedIn-like reactions with Serverless Redis
The easiest way to connect Redis with Upstash is to use the redis-client as described here.
-
Host and Use Redis for Free
After filling out the project details, cd into your project and install redis, a Node.js client for Redis, and dotenv, an environment variable loader.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
redis-modules-sdk-ts - A Software development kit for easier connection and execution of Redis Modules commands.
Milvus - A cloud-native vector database, storage for next generation AI applications
dotenv - Loads environment variables from .env for nodejs projects.
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
RedisInsight - Redis GUI by Redis
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
upstash-redis - HTTP based Redis Client for Serverless and Edge Functions
AI - Artificial Intelligence Projects
bull-board - 🎯 Queue background jobs inspector
pgvector - Open-source vector similarity search for Postgres