terraform-aws-lambda VS qdrant

There are just too many required parameters to create a single handler. And then you need to do that N times for each handler. Take a look at a complete Terraform example for a lambda: https://github.com/terraform-aws-modules/terraform-aws-lambd...

For a personal project it's just a bit much in my experience, especially since most personal projects can easily be served by a t3.micro.

https://github.com/terraform-aws-modules/terraform-aws-lambda/ - this modules has it all. There are various examples and submodules in that repository to help you achieve all your serverless tasks the same way.

It’s also weird not to know about terraform modules. There are a lot of things you _can_ configure because there are a lot of things people need to configure but if you’re using something like https://github.com/terraform-aws-modules/terraform-aws-lambd... it’s only a couple of lines of config for a Lambda.

I use the Community Terraform Lambda Module to define the lambda. It allows me to define a lambda in just a few lines that are configured with a role and can also be easily extended with policies. The cool thing is that cdktf supports a type import. To do this, simply add the following module to the cdktf.json file:

As somebody already mentioned in the comments, you can use the terraform-aws-lambda module as well as many other modules for typical serverless services like API Gateway v2, AppSync, EventBridge, StepFunctions, AppConfig, DynamoDB, and many more.

You can containerize the lambda functions and when you deploy each lambda function, you can overwrite the CMD for each function in the resource block to match what you need. I'm doing something similar but only deploy 2 lambda functions which use the same source code but do very different tasks. I also strongly suggest you use the terraform-aws-lambda module since it will build out all related resources you need.

Have a look at this module: https://github.com/terraform-aws-modules/terraform-aws-lambda

I can't find any open issues on github or anyone asking for it other than in the modules that are provided. https://github.com/terraform-aws-modules/terraform-aws-lambda/issues/128

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.

I'm currently looking to implement locally, using QDrant [1] for instance.

I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].

[1]. https://qdrant.tech/

Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb

For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!

Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.

Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.

Qdrant serves as a vector database, optimized for handling high-dimensional data typically found in AI and ML applications. It's designed for efficient storage and retrieval of vectors, making it an ideal solution for managing the data produced and consumed by AI models like Mistral 7B. In our setup, Qdrant handles the storage of vectors generated by the language model, facilitating quick and accurate retrievals.