feat: Added Hybrid Search Config and Tests [1/N]

langchain_postgres/v2/engine.py

@@ -9,6 +9,8 @@
 from sqlalchemy.engine import URL
 from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
 
+from .hybrid_search_config import HybridSearchConfig
+
 T = TypeVar("T")
 
 
@@ -156,6 +158,7 @@ async def _ainit_vectorstore_table(
         id_column: Union[str, Column, ColumnDict] = "langchain_id",
         overwrite_existing: bool = False,
         store_metadata: bool = True,
+        hybrid_search_config: Optional[HybridSearchConfig] = None,
     ) -> None:
         """
         Create a table for saving of vectors to be used with PGVectorStore.
@@ -178,6 +181,8 @@ async def _ainit_vectorstore_table(
             overwrite_existing (bool): Whether to drop existing table. Default: False.
             store_metadata (bool): Whether to store metadata in the table.
                 Default: True.
+            hybrid_search_config (HybridSearchConfig): Hybrid search configuration.
+                Default: None.
 
         Raises:
             :class:`DuplicateTableError <asyncpg.exceptions.DuplicateTableError>`: if table already exists.
@@ -186,6 +191,7 @@ async def _ainit_vectorstore_table(
 
         schema_name = self._escape_postgres_identifier(schema_name)
         table_name = self._escape_postgres_identifier(table_name)
+        hybrid_search_default_column_name = content_column + "_tsv"
         content_column = self._escape_postgres_identifier(content_column)
         embedding_column = self._escape_postgres_identifier(embedding_column)
         if metadata_columns is None:
@@ -226,10 +232,22 @@ async def _ainit_vectorstore_table(
             id_data_type = id_column["data_type"]
             id_column_name = id_column["name"]
 
+        hybrid_search_column = ""  # Default is no TSV column for hybrid search
+        if hybrid_search_config:
+            hybrid_search_column_name = (
+                hybrid_search_config.tsv_column or hybrid_search_default_column_name
+            )
+            hybrid_search_column_name = self._escape_postgres_identifier(
+                hybrid_search_column_name
+            )
+            hybrid_search_config.tsv_column = hybrid_search_column_name
+            hybrid_search_column = f',"{self._escape_postgres_identifier(hybrid_search_column_name)}" TSVECTOR NOT NULL'
+
         query = f"""CREATE TABLE "{schema_name}"."{table_name}"(
             "{id_column_name}" {id_data_type} PRIMARY KEY,
             "{content_column}" TEXT NOT NULL,
-            "{embedding_column}" vector({vector_size}) NOT NULL"""
+            "{embedding_column}" vector({vector_size}) NOT NULL
+            {hybrid_search_column}"""
         for column in metadata_columns:
             if isinstance(column, Column):
                 nullable = "NOT NULL" if not column.nullable else ""
@@ -258,6 +276,7 @@ async def ainit_vectorstore_table(
         id_column: Union[str, Column, ColumnDict] = "langchain_id",
         overwrite_existing: bool = False,
         store_metadata: bool = True,
+        hybrid_search_config: Optional[HybridSearchConfig] = None,
     ) -> None:
         """
         Create a table for saving of vectors to be used with PGVectorStore.
@@ -280,6 +299,10 @@ async def ainit_vectorstore_table(
             overwrite_existing (bool): Whether to drop existing table. Default: False.
             store_metadata (bool): Whether to store metadata in the table.
                 Default: True.
+            hybrid_search_config (HybridSearchConfig): Hybrid search configuration.
+                Note that queries might be slow if the hybrid search column does not exist.
+                For best hybrid search performance, consider creating a TSV column and adding GIN index.
+                Default: None.
         """
         await self._run_as_async(
             self._ainit_vectorstore_table(
@@ -293,6 +316,7 @@ async def ainit_vectorstore_table(
                 id_column=id_column,
                 overwrite_existing=overwrite_existing,
                 store_metadata=store_metadata,
+                hybrid_search_config=hybrid_search_config,
             )
         )
 
@@ -309,6 +333,7 @@ def init_vectorstore_table(
         id_column: Union[str, Column, ColumnDict] = "langchain_id",
         overwrite_existing: bool = False,
         store_metadata: bool = True,
+        hybrid_search_config: Optional[HybridSearchConfig] = None,
     ) -> None:
         """
         Create a table for saving of vectors to be used with PGVectorStore.
@@ -331,6 +356,10 @@ def init_vectorstore_table(
             overwrite_existing (bool): Whether to drop existing table. Default: False.
             store_metadata (bool): Whether to store metadata in the table.
                 Default: True.
+            hybrid_search_config (HybridSearchConfig): Hybrid search configuration.
+                Note that queries might be slow if the hybrid search column does not exist.
+                For best hybrid search performance, consider creating a TSV column and adding GIN index.
+                Default: None.
         """
         self._run_as_sync(
             self._ainit_vectorstore_table(
@@ -344,6 +373,7 @@ def init_vectorstore_table(
                 id_column=id_column,
                 overwrite_existing=overwrite_existing,
                 store_metadata=store_metadata,
+                hybrid_search_config=hybrid_search_config,
             )
         )
 
@@ -354,7 +384,7 @@ async def _adrop_table(
         schema_name: str = "public",
     ) -> None:
         """Drop the vector store table"""
-        query = f'DROP TABLE "{schema_name}"."{table_name}";'
+        query = f'DROP TABLE IF EXISTS "{schema_name}"."{table_name}";'
         async with self._pool.connect() as conn:
             await conn.execute(text(query))
             await conn.commit()

langchain_postgres/v2/hybrid_search_config.py

@@ -0,0 +1,149 @@
+from abc import ABC
+from dataclasses import dataclass, field
+from typing import Any, Callable, Optional, Sequence
+
+from sqlalchemy import RowMapping
+
+
+def weighted_sum_ranking(
+    primary_search_results: Sequence[RowMapping],
+    secondary_search_results: Sequence[RowMapping],
+    primary_results_weight: float = 0.5,
+    secondary_results_weight: float = 0.5,
+    fetch_top_k: int = 4,
+) -> Sequence[dict[str, Any]]:
+    """
+    Ranks documents using a weighted sum of scores from two sources.
+
+    Args:
+        primary_search_results: A list of (document, distance) tuples from
+            the primary search.
+        secondary_search_results: A list of (document, distance) tuples from
+            the secondary search.
+        primary_results_weight: The weight for the primary source's scores.
+            Defaults to 0.5.
+        secondary_results_weight: The weight for the secondary source's scores.
+            Defaults to 0.5.
+        fetch_top_k: The number of documents to fetch after merging the results.
+            Defaults to 4.
+
+    Returns:
+        A list of (document, distance) tuples, sorted by weighted_score in
+        descending order.
+    """
+
+    # stores computed metric with provided distance metric and weights
+    weighted_scores: dict[str, dict[str, Any]] = {}
+
+    # Process results from primary source
+    for row in primary_search_results:
+        values = list(row.values())
+        doc_id = str(values[0])  # first value is doc_id
+        distance = float(values[-1])  # type: ignore # last value is distance
+        row_values = dict(row)
+        row_values["distance"] = primary_results_weight * distance
+        weighted_scores[doc_id] = row_values
+
+    # Process results from secondary source,
+    # adding to existing scores or creating new ones
+    for row in secondary_search_results:
+        values = list(row.values())
+        doc_id = str(values[0])  # first value is doc_id
+        distance = float(values[-1])  # type: ignore # last value is distance
+        primary_score = (
+            weighted_scores[doc_id]["distance"] if doc_id in weighted_scores else 0.0
+        )
+        row_values = dict(row)
+        row_values["distance"] = distance * secondary_results_weight + primary_score
+        weighted_scores[doc_id] = row_values
+
+    # Sort the results by weighted score in descending order
+    ranked_results = sorted(
+        weighted_scores.values(), key=lambda item: item["distance"], reverse=True
+    )
+    return ranked_results[:fetch_top_k]
+
+
+def reciprocal_rank_fusion(
+    primary_search_results: Sequence[RowMapping],
+    secondary_search_results: Sequence[RowMapping],
+    rrf_k: float = 60,
+    fetch_top_k: int = 4,
+) -> Sequence[dict[str, Any]]:
+    """
+    Ranks documents using Reciprocal Rank Fusion (RRF) of scores from two sources.
+
+    Args:
+        primary_search_results: A list of (document, distance) tuples from
+            the primary search.
+        secondary_search_results: A list of (document, distance) tuples from
+            the secondary search.
+        rrf_k: The RRF parameter k.
+            Defaults to 60.
+        fetch_top_k: The number of documents to fetch after merging the results.
+            Defaults to 4.
+
+    Returns:
+        A list of (document_id, rrf_score) tuples, sorted by rrf_score
+        in descending order.
+    """
+    rrf_scores: dict[str, dict[str, Any]] = {}
+
+    # Process results from primary source
+    for rank, row in enumerate(
+        sorted(primary_search_results, key=lambda item: item["distance"], reverse=True)
+    ):
+        values = list(row.values())
+        doc_id = str(values[0])
+        row_values = dict(row)
+        primary_score = rrf_scores[doc_id]["distance"] if doc_id in rrf_scores else 0.0
+        primary_score += 1.0 / (rank + rrf_k)
+        row_values["distance"] = primary_score
+        rrf_scores[doc_id] = row_values
+
+    # Process results from secondary source
+    for rank, row in enumerate(
+        sorted(
+            secondary_search_results, key=lambda item: item["distance"], reverse=True
+        )
+    ):
+        values = list(row.values())
+        doc_id = str(values[0])
+        row_values = dict(row)
+        secondary_score = (
+            rrf_scores[doc_id]["distance"] if doc_id in rrf_scores else 0.0
+        )
+        secondary_score += 1.0 / (rank + rrf_k)
+        row_values["distance"] = secondary_score
+        rrf_scores[doc_id] = row_values
+
+    # Sort the results by rrf score in descending order
+    # Sort the results by weighted score in descending order
+    ranked_results = sorted(
+        rrf_scores.values(), key=lambda item: item["distance"], reverse=True
+    )
+    # Extract only the RowMapping for the top results
+    return ranked_results[:fetch_top_k]
+
+
+@dataclass
+class HybridSearchConfig(ABC):
+    """
+    AlloyDB Vector Store Hybrid Search Config.
+
+    Queries might be slow if the hybrid search column does not exist.
+    For best hybrid search performance, consider creating a TSV column
+    and adding GIN index.
+    """
+
+    tsv_column: Optional[str] = ""
+    tsv_lang: Optional[str] = "pg_catalog.english"
+    fts_query: Optional[str] = ""
+    fusion_function: Callable[
+        [Sequence[RowMapping], Sequence[RowMapping], Any], Sequence[Any]
+    ] = weighted_sum_ranking  # Updated default
+    fusion_function_parameters: dict[str, Any] = field(default_factory=dict)
+    primary_top_k: int = 4
+    secondary_top_k: int = 4
+    index_name: str = "langchain_tsv_index"
+    index_type: str = "GIN"