node-redis VS marqo

Probably redis.

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.

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.

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).

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.

> 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));

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

The easiest way to connect Redis with Upstash is to use the redis-client as described here.

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.

We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that comes with that. I would love to chat about 1 and 2 so feel free to email me (email is in my profile). What we have done so far is here -> https://github.com/marqo-ai/marqo

Marqo.ai (https://github.com/marqo-ai/marqo) is doing some interesting stuff and is oss. We handle embedding generation as well as retrieval (full disclosure, I work for Marqo.ai)

Take a look here https://github.com/marqo-ai/local-image-search-demo. It is based on https://github.com/marqo-ai/marqo. We do a lot of image search applications. Feel free to reach out if you have other questions (email in profile).

That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo (https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestration is included (no api calls) and many open-source or fine-tuned models can be used.

Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images in a single retrieval step.

As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has vector functionality doesn’t mean it will actually service anything beyond “hello world” type semantic search applications. IMHO these have questionable value, much like the simple Q and A RAG applications that have proliferated. The elephant in the room with these systems is that if you are relying on machine learning models to produce the vectors you are going to need to invest heavily in the ML components of the system. Domain specific models are a must if you want to be a serious contender to an existing search system and all the usual considerations still apply regarding frequent retraining and monitoring of the models. Currently this is left as an exercise to the reader - and a very large one at that. We (https://github.com/marqo-ai/marqo, I am a co-founder) are investing heavily into making the ML production worthy and continuous learning from feedback of the models as part of the system. Lots of other things to think about in how you represent documents with multiple vectors, multimodality, late interactions, the interplay between embedding quality and HNSW graph quality (i.e. recall) and much more.

Marqo provides automatic, configurable chunking (for example with overlap) and can allow you to bring your own model or choose from a wide range of opensource models. I think e5-large would be a good one to try. https://github.com/marqo-ai/marqo

Marqo has a similar API to Meilisearch's standard API but uses vector search in the background: https://github.com/marqo-ai/marqo

Have you tried Marqo? check the repo : https://github.com/marqo-ai/marqo