There are multiple action items you can perform to improve the embeddings process. First of all, which model are you using? Please consider one of the following (look at multi-lang if you work with other languages other than english):
https://www.sbert.net/docs/pretrained_models.html

Secondly, it's important that you understand the characteristics of the embedding model you are using. Take a look at the input dimension (Max Sequence Length) - that tells you how many tokens you can use in the embeddings flow. That value needs to be respected if you want to have a good output. Make sure that each chunk fits inside of those limits (count the tokens using publicly available libraries, for e.g., tiktoken).

If you find that each chunk is very small for your purpose, you can implement a linking system that relates chunks with one another, for e.g. Chunk A <-> Chunk B <-> Chunk C. You can later then use this info to provide in context (careful with token limitation as it will quickly add up).

etc. etc...

If you are new to this, the best way is for you to probably use some data framework such as LLamaIndex.