fix(pgvector): handle case where distance is 0 by setting score to infinity (#2416)

# What does this PR do?
Fixes provider pgvector `query_vector` function for when the distance
between the query embedding and an embedding within the vector db is 0
(identical vectors). Catches `ZeroDivisionError` and then sets `score`
to infinity, which represent maximum similarity.

<!-- If resolving an issue, uncomment and update the line below -->
Closes [#2381]

## Test Plan
Checkout this PR

Execute this code and there will no longer be a `ZeroDivisionError`
exception
```
from llama_stack_client import LlamaStackClient

base_url = "http://localhost:8321"
client = LlamaStackClient(base_url=base_url)

models = client.models.list()
embedding_model = (
    em := next(m for m in models if m.model_type == "embedding")
).identifier
embedding_dimension = 384

_ = client.vector_dbs.register(
    vector_db_id="foo_db",
    embedding_model=embedding_model,
    embedding_dimension=embedding_dimension,
    provider_id="pgvector",
)

chunk = {
    "content": "foo",
    "mime_type": "text/plain",
    "metadata": {
        "document_id": "foo-id"
    }
}

client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk])
client.vector_io.query(vector_db_id="foo_db", query="foo")
```

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -116,7 +116,7 @@ class PGVectorIndex(EmbeddingIndex):
             scores = []
             for doc, dist in results:
                 chunks.append(Chunk(**doc))
-                scores.append(1.0 / float(dist))
+                scores.append(1.0 / float(dist) if dist != 0 else float("inf"))
 
             return QueryChunksResponse(chunks=chunks, scores=scores)