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}',"