fix: Fix hybrid search bugs (#257)

This PR fixes the 2nd Major issue in
#234, related
to Configuration and Querying Issues

Issue breakdown:
-
**Inconsistent k Parameter:** For hybrid searches, the dense search
LIMIT was tied to the final k parameter, while the sparse search used
its own secondary_top_k.
**Late Initialization of HybridSearchConfig:** The HybridSearchConfig
was initialized after the k parameter was calculated, which could lead
to incorrect behavior if the config was passed dynamically via `kwargs`.

Overview of the changes:
-
1. The __query_collection method now uses a separate `dense_limit` for
the dense search query.
If HybridSearchConfig is active, dense_limit is set to primary_top_k.
For dense-only searches, dense_limit defaults to the final k value,
preserving the existing behavior.

2. The hybrid_search_config is now initialized at the beginning of
`__query_collection`, ensuring that any `kwargs` overrides are handled
before `k` or `dense_limit` are calculated.

Author: dishaprakash

langchain_postgres/v2/async_vectorstore.py

@@ -580,16 +580,16 @@ async def __query_collection(
         For best hybrid search performance, consider creating a TSV column
         and adding GIN index.
         """
-        if not k:
-            k = (
-                max(
-                    self.k,
-                    self.hybrid_search_config.primary_top_k,
-                    self.hybrid_search_config.secondary_top_k,
-                )
-                if self.hybrid_search_config
-                else self.k
-            )
+        hybrid_search_config = kwargs.get(
+            "hybrid_search_config", self.hybrid_search_config
+        )
+
+        final_k = k if k is not None else self.k
+
+        dense_limit = final_k
+        if hybrid_search_config:
+            dense_limit = hybrid_search_config.primary_top_k
+
         operator = self.distance_strategy.operator
         search_function = self.distance_strategy.search_function
 
@@ -617,9 +617,9 @@ async def __query_collection(
             embedding_data_string = ":query_embedding"
         where_filters = f"WHERE {safe_filter}" if safe_filter else ""
         dense_query_stmt = f"""SELECT {column_names}, {search_function}("{self.embedding_column}", {embedding_data_string}) as distance
-        FROM "{self.schema_name}"."{self.table_name}" {where_filters} ORDER BY "{self.embedding_column}" {operator} {embedding_data_string} LIMIT :k;
+        FROM "{self.schema_name}"."{self.table_name}" {where_filters} ORDER BY "{self.embedding_column}" {operator} {embedding_data_string} LIMIT :dense_limit;
         """
-        param_dict = {"query_embedding": query_embedding, "k": k}
+        param_dict = {"query_embedding": query_embedding, "dense_limit": dense_limit}
         if filter_dict:
             param_dict.update(filter_dict)
         if self.index_query_options:
@@ -637,16 +637,13 @@ async def __query_collection(
                 result_map = result.mappings()
                 dense_results = result_map.fetchall()
 
-        hybrid_search_config = kwargs.get(
-            "hybrid_search_config", self.hybrid_search_config
-        )
         fts_query = (
             hybrid_search_config.fts_query
             if hybrid_search_config and hybrid_search_config.fts_query
             else kwargs.get("fts_query", "")
         )
         if hybrid_search_config and fts_query:
-            hybrid_search_config.fusion_function_parameters["fetch_top_k"] = k
+            hybrid_search_config.fusion_function_parameters["fetch_top_k"] = final_k
             # do the sparse query
             lang = (
                 f"'{hybrid_search_config.tsv_lang}',"