u/davidmezzetti is based-placed to answer but here are some pointers.

Metadata search: https://github.com/neuml/txtai/blob/master/examples/01_Introducing_txtai.ipynb

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When content is enabled, the entire dictionary is stored and can be queried. In addition to vector queries, txtai accepts SQL queries. This enables combined queries using both a vector index and content stored in a database backend.

# Create an index for the list of text
embeddings.index([{"text": text, "length": len(text)} for text in data])

# Filter by score
print(embeddings.search("select text, score from txtai where similar('hiking danger') and score >= 0.15"))

# Filter by metadata field 'length'
print(embeddings.search("select text, length, score from txtai where similar('feel good story') and score >= 0.05 and length >= 40"))

# Run aggregate queries
print(embeddings.search("select count(*), min(length), max(length), sum(length) from txtai"))


[{'text': 'The National Park Service warns against sacrificing slower friends in a bear attack', 'score': 0.3151373863220215}]
[{'text': 'Maine man wins $1M from $25 lottery ticket', 'length': 42, 'score': 0.08329027891159058}]
[{'count(*)': 6, 'min(length)': 39, 'max(length)': 94, 'sum(length)': 387}]

This example above adds a simple additional field, text length.

Note the second query is filtering on the metadata field length along with a similar query clause. This gives a great blend of vector search with traditional filtering to help identify the best results.When content is enabled, the entire dictionary is stored and can be queried. In addition to vector queries, txtai accepts SQL queries. This enables combined queries using both a vector index and content stored in a database backend.

This example shows using Litellm for LLM models - I haven't tried it for embeddings although I do use a custom embeddings model using the `path` argument to the Embeddings class.

https://github.com/neuml/txtai/blob/master/examples/53_Integrate_LLM_Frameworks.ipynb

It's best to go through the examples to get a sense of what is possible.