chuckhend

8 ms·

31.
by chuckhend 2y ago
This is exactly how pgmq is implemented, + the usage of VT.
32.
by chuckhend 2y ago
I agree. But it can be useful to have a guarantee, even for a specified period of time, that the message will only be seen once. For example, if the processing of that message is very expensive, such as if that message results in API reques
33.
by chuckhend 2y ago
IMO, it is most valuable when you are looking for ways of reducing complexity. For a lot of projects, if you're already running Postgres then it is maybe not worth the added complexity of bringing in another technology.
34.
by chuckhend 2y ago
If the message never reaches the queue (network error, database is down, app is down, etc), then yes that is a 0 delivery scenario. Once the message reaches the queue though, it is guaranteed that only a single consumer can read the message
35.
by chuckhend 2y ago
pgmq.archive() gives us an API to retain messages on your queue, its an alternative to pgmq.delete(). For me as a long-time Redis user, message retention was always important and was always extra work to implement. DLQ isn't a built-in
36.
by chuckhend 2y ago
This is exactly how we do this in our SaaS at Tembo.io. We check read_ct, and move the message if >= N. I think it would be awesome if this were a built-in feature though.
37.
by chuckhend 2y ago
We talk a little bit in https://tembo.io/blog/managed-postgres-rust about how we use PGMQ to run our SaaS at Tembo.io. We could have ran a Redis instance and used RSMQ, but it simplified our architecture to stick with
38.
by chuckhend 2y ago
PGMQ doesn't give you a way to deliver the same message to concurrent consumers the same way that you can with Kafka via consumer groups. To get this with PGMQ, you'd need to do something like creating multiple queues, then send m
39.
by chuckhend 2y ago
Simplicity is one of the reasons we started this project. IMO, far less maintenance overhead to running Postgres compared to RabbitMQ, especially if you are already running Postgres in your application stack. If PGMQ fits your requirements,
40.
by chuckhend 2y ago
I think it would be tough to compare. There are client libraries for several languages, but the project is mostly a SQL API to the queue operations like send, read, archive, delete using the same semantics as SQS/RSMQ. Any language tha
41.
Show HN: An SQS Alternative on Postgres(github.com)
241 points by chuckhend 2y ago|109 comments
42.
Kan: Kolmogorov-Arnold Networks(arxiv.org)
28 points by chuckhend 2y ago|4 comments
43.
About Talk Selection for Posette: An Event for Postgres 2024(citusdata.com)
1 points by chuckhend 2y ago|0 comments
44.
by chuckhend 2y ago
Take a look at https://github.com/tembo-io/pg_vectorize . It makes it a lot easier to get started. It runs on pgvector, but as a user, its completely abstracted from you. It also provides you with a way to auto-update e
45.
by chuckhend 2y ago
check out https://github.com/tembo-io/pg_vectorize - we're taking it a little bit beyond just the storage and index. The project uses pgvector for the indices and distance operators, but also adds a simpler API, h
46.
MLOps vs. Eng: Misaligned Incentives and Failure to Launch?(heavybit.com)
1 points by chuckhend 2y ago|0 comments
47.
Operationalizing Vector Databases on Postgres(tembo.io)
1 points by chuckhend 2y ago|0 comments
48.
The CPU's role in generative AI(datacenterdynamics.com)
2 points by chuckhend 3y ago|0 comments
49.
AWS Latency Monitoring(cloudping.co)
1 points by chuckhend 3y ago|0 comments
50.
What Are the Changes to Section 174, and Do They Affect the R&D Tax Credit?(gusto.com)
1 points by chuckhend 3y ago|0 comments
51.
by chuckhend 3y ago
I've heard of people having success with methods like this. Would be awesome if we found a way to build that into this project :)
52.
by chuckhend 3y ago
We are working on a 'self-hosted' alternative to OpenAI. The project already has that for the embeddings. i.e. you specify an open-source model from hugging face/sentence-transformers, then API calls get routed to that servic
53.
by chuckhend 3y ago
There's an API that abstracts vector search only. vectorize.search() and that part is not unique to LLMs but it does require selection of an embedding model. Some people have called embedding models LLMs. vectorize.rag() requires selec
54.
by chuckhend 3y ago
One difference between the two projects is that pg_vectorize does not run the embedding or chat models on the same host as postgres, rather they are always separate. The extension makes http requests to those models, and provides background
55.
by chuckhend 3y ago
To oversimplify, its more like sending a block of text to an LLM and asking it to answer a query based on that block of text.
56.
by chuckhend 3y ago
Its certainly up for debate and there is a lot of nuance. I think it can simplify the system's architecture quite a bit of all the consumers of data do not need to keep track of which transformer model to use. After all, once the embed
57.
by chuckhend 3y ago
There is a RAG example here https://github.com/tembo-io/pg_vectorize?tab=readme-ov-file#... You can provide your own prompts by adding them to the `vectorize.prompts` table. There's an API for this in the works. I
58.
by chuckhend 3y ago
There is no chunking built into the postgres extension yet, but we are working on it. It does check the context length of the request against the limits of the chat model before sending the request, and optionally allows you to auto-trim th
59.
by chuckhend 3y ago
pg_vectorize is a wrapper around pgvector. In addition to what pgvector provides, vectorize provides hooks into many methods to generate your embeddings, implements several methods for keeping embeddings updated as your data grows or change
60.
by chuckhend 3y ago
RAG can cost a lot of money if not done thoughtfully. Most embedding and chat completion model providers charge by the token (think number of words in the request). You'll pay to have the data in the database transformed into embedding

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