resolve conflicts

examples/similarity_search_for_text.py

@@ -26,7 +26,6 @@ def embed_query(self, text: str) -> List[float]:
 # Initialize the client
 client = GenAIClient(driver, embedder)
 
-client.drop_index(INDEX_NAME)
 # Creating the index
 client.create_index(
     INDEX_NAME,

src/neo4j_genai/client.py

@@ -1,8 +1,8 @@
-from typing import List, Optional
+from typing import List, Optional, Any
 from pydantic import ValidationError
 from neo4j import Driver
 from .embedder import Embedder
-from .types import CreateIndexModel, SimilaritySearchModel, Neo4jRecord
+from .types import CreateIndexModel, SimilaritySearchModel
 
 
 class GenAIClient:
@@ -114,7 +114,8 @@ def similarity_search(
         query_vector: Optional[List[float]] = None,
         query_text: Optional[str] = None,
         top_k: int = 5,
-    ) -> List[Neo4jRecord]:
+        custom_retrieval_query: Optional[str] = None,
+    ) -> Any:
         """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
         See the following documentation for more details:
 
@@ -126,20 +127,23 @@ def similarity_search(
             query_vector (Optional[List[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None.
             query_text (Optional[str], optional): The text to get the closest neighbors of. Defaults to None.
             top_k (int, optional): The number of neighbors to return. Defaults to 5.
+            custom_retrieval_query (Optional[str], optional: Custom query to use as suffix for retrieval query. Defaults to None
 
         Raises:
             ValueError: If validation of the input arguments fail.
             ValueError: If no embedder is provided.
 
         Returns:
-            List[Neo4jRecord]: The `top_k` neighbors found in vector search with their nodes and scores.
+            Any: The `top_k` neighbors found in vector search with their nodes and scores.
+            If custom_retrieval_query is provided, this is changed.
         """
         try:
             validated_data = SimilaritySearchModel(
                 index_name=name,
                 top_k=top_k,
                 query_vector=query_vector,
                 query_text=query_text,
+                custom_retrieval_query=custom_retrieval_query,
             )
         except ValidationError as e:
             error_details = e.errors()
@@ -154,19 +158,16 @@ def similarity_search(
             parameters["query_vector"] = query_vector
             del parameters["query_text"]
 
-        db_query_string = """
+        query_prefix = """
         CALL db.index.vector.queryNodes($index_name, $top_k, $query_vector)
         YIELD node, score
         """
-        records, _, _ = self.driver.execute_query(db_query_string, parameters)
 
-        try:
-            return [
-                Neo4jRecord(node=record["node"], score=record["score"])
-                for record in records
-            ]
-        except ValidationError as e:
-            error_details = e.errors()
-            raise ValueError(
-                f"Validation failed while constructing output: {error_details}"
-            )
+        if parameters.get("custom_retrieval_query") is not None:
+            search_query = query_prefix + parameters["custom_retrieval_query"]
+            del parameters["custom_retrieval_query"]
+        else:
+            search_query = query_prefix
+
+        records, _, _ = self.driver.execute_query(search_query, parameters)
+        return records

src/neo4j_genai/types.py

@@ -1,11 +1,7 @@
-from typing import List, Any, Literal, Optional
+from typing import List, Literal, Optional
 from pydantic import BaseModel, PositiveInt, model_validator
 
 
-Neo4jRecord = dict[str, Any]
-"""Type alias for data items returned from Neo4j queries"""
-
-
 class EmbeddingVector(BaseModel):
     vector: List[float]
 
@@ -23,6 +19,7 @@ class SimilaritySearchModel(BaseModel):
     top_k: PositiveInt = 5
     query_vector: Optional[List[float]] = None
     query_text: Optional[str] = None
+    custom_retrieval_query: Optional[str] = None
 
     @model_validator(mode="before")
     def check_query(cls, values):