enhancement: Optimizing embedding calls in brute_force_search (#1101)

@Vasilije1990

- Use query_vector instead of query_text in brute_force_search

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## Description

[Here](ef1aecd835/cognee/modules/retrieval/utils/brute_force_triplet_search.py (L163))
brute_force_search uses the vector engine to perform the same search —
with the same query text — across multiple collections, making the
embedding calls unnecessarily proportional to the number of collections
being searched.

Since the
[search](ef1aecd835/cognee/infrastructure/databases/vector/vector_db_interface.py (L85))
interface is already designed to accept precomputed query vectors, I’m
submitting an optimization to brute_force_search to take advantage of
this.

If this is considered good practice, it might be worth implementing a
direct query_vector argument in
[map_vector_distances_to_graph_edges](ef1aecd835/cognee/modules/graph/cognee_graph/CogneeGraph.py (L135))
, and using it both
[here](ef1aecd835/cognee/modules/retrieval/utils/brute_force_triplet_search.py (L179))
and in any future uses of map_vector_distances_to_graph_edges.

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

---------

Co-authored-by: Pedro Henrique Thompson Furtado <pedrothompson@petrobras.com.br>
Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>
Co-authored-by: Daulet Amirkhanov <damirkhanov01@gmail.com>
Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>

cognee/modules/retrieval/utils/brute_force_triplet_search.py

@@ -155,12 +155,14 @@ async def brute_force_search(
         logger.error("Failed to initialize vector engine: %s", e)
         raise RuntimeError("Initialization error") from e
 
+    query_vector = (await vector_engine.embedding_engine.embed_text([query]))[0]
+
     send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
 
     async def search_in_collection(collection_name: str):
         try:
             return await vector_engine.search(
-                collection_name=collection_name, query_text=query, limit=0
+                collection_name=collection_name, query_vector=query_vector, limit=0
             )
         except CollectionNotFoundError:
             return []