Return a user-defined message if context is empty in GraphRAG

CHANGELOG.md

@@ -5,6 +5,7 @@
 ### Added
 
 - Added support for automatic schema extraction from text using LLMs. In the `SimpleKGPipeline`, when the user provides no schema, the automatic schema extraction is enabled by default.
+- Added ability to return a user-defined message if context is empty in GraphRAG (which skips the LLM call).
 
 ### Fixed
 

docs/source/user_guide_rag.rst

@@ -1,15 +1,15 @@
 .. _user-guide-rag:
 
 User Guide: RAG
-#################
+###############
 
 This guide provides a starting point for using the Neo4j GraphRAG package
 and configuring it according to specific requirements.
 
 
-************
+**********
 Quickstart
-************
+**********
 
 To perform a GraphRAG query using the `neo4j-graphrag` package, a few components are needed:
 
@@ -63,9 +63,9 @@ In practice, it's done with only a few lines of code:
 
 The following sections provide more details about how to customize this code.
 
-******************************
+**********************
 GraphRAG Configuration
-******************************
+**********************
 
 Each component can be configured individually: the LLM and the prompt.
 
@@ -775,7 +775,7 @@ See :ref:`hybridcypherretriever`.
 .. _text2cypher-retriever-user-guide:
 
 Text2Cypher Retriever
-------------------------------------
+---------------------
 
 This retriever first asks an LLM to generate a Cypher query to fetch the exact
 information required to answer the question from the database. Then this query is
@@ -853,7 +853,7 @@ See :ref:`text2cypherretriever`.
 .. _custom-retriever:
 
 Custom Retriever
-===================
+================
 
 If the application requires very specific retrieval strategy, it is possible to implement
 a custom retriever using the `Retriever` interface:
@@ -883,14 +883,60 @@ a custom retriever using the `Retriever` interface:
 See :ref:`rawsearchresult` for a description of the returned type.
 
 
-******************************
+***********************
+GraphRAG search options
+***********************
+
+Return context
+==============
+
+By default, the search method only returns the final answer. It is possible to see
+what was retrieved as part of the context by setting `return_context=True`:
+
+.. code:: python
+
+    rag.search("my question", return_context=True)
+
+
+Return a custom message if context is empty
+===========================================
+
+If the retriever is not able to find any context, the LLM will return an answer anyway.
+It is possible to skip the LLM call if the context is empty and return a user-defined message
+instead:
+
+.. code:: python
+
+    rag.search(
+        "my question",
+        response_fallback="I can not answer this question because I have no relevant context."
+    )
+
+
+Pass configuration to the retriever search method
+=================================================
+
+The retrievers search method have a bunch of configuration options (see above),
+which can also be configured through the GraphRAG search method using the `retriever_config`
+argument. For instance, the following code snippet illustrates how to define the `top_k`
+parameter for the retriever:
+
+.. code:: python
+
+    rag.search(
+        "my question",
+        retriever_config={"top_k": 2}
+    )
+
+
+**************
 DB Operations
-******************************
+**************
 
 See :ref:`database-interaction-section`.
 
 Create a Vector Index
-========================
+=====================
 
 .. code:: python
 
@@ -918,7 +964,7 @@ Create a Vector Index
 
 
 Populate a Vector Index
-==========================
+=======================
 
 .. code:: python
 
@@ -950,7 +996,7 @@ This property will also be added to the vector index.
 
 
 Drop a Vector Index
-========================
+===================
 
 .. warning::
 

examples/question_answering/graphrag.py

@@ -56,6 +56,8 @@ def formatter(record: neo4j.Record) -> RetrieverResultItem:
 result = rag.search(
     "Tell me more about Avatar movies",
     return_context=True,
+    # optional
+    response_fallback="I can't answer this question without context",
 )
 print(result.answer)
 # print(result.retriever_result)

src/neo4j_graphrag/generation/graphrag.py

@@ -89,6 +89,7 @@ def search(
         examples: str = "",
         retriever_config: Optional[dict[str, Any]] = None,
         return_context: bool | None = None,
+        response_fallback: str | None = None,
     ) -> RagResultModel:
         """
         .. warning::
@@ -109,6 +110,7 @@ def search(
             retriever_config (Optional[dict]): Parameters passed to the retriever.
                 search method; e.g.: top_k
             return_context (bool): Whether to append the retriever result to the final result (default: False).
+            response_fallback (Optional[str]): If not null, will return this message instead of calling the LLM if context comes back empty.
 
         Returns:
             RagResultModel: The LLM-generated answer.
@@ -126,6 +128,7 @@ def search(
                 examples=examples,
                 retriever_config=retriever_config or {},
                 return_context=return_context,
+                response_fallback=response_fallback,
             )
         except ValidationError as e:
             raise SearchValidationError(e.errors())
@@ -135,18 +138,22 @@ def search(
         retriever_result: RetrieverResult = self.retriever.search(
             query_text=query, **validated_data.retriever_config
         )
-        context = "\n".join(item.content for item in retriever_result.items)
-        prompt = self.prompt_template.format(
-            query_text=query_text, context=context, examples=validated_data.examples
-        )
-        logger.debug(f"RAG: retriever_result={prettify(retriever_result)}")
-        logger.debug(f"RAG: prompt={prompt}")
-        answer = self.llm.invoke(
-            prompt,
-            message_history,
-            system_instruction=self.prompt_template.system_instructions,
-        )
-        result: dict[str, Any] = {"answer": answer.content}
+        if len(retriever_result.items) == 0 and response_fallback is not None:
+            answer = response_fallback
+        else:
+            context = "\n".join(item.content for item in retriever_result.items)
+            prompt = self.prompt_template.format(
+                query_text=query_text, context=context, examples=validated_data.examples
+            )
+            logger.debug(f"RAG: retriever_result={prettify(retriever_result)}")
+            logger.debug(f"RAG: prompt={prompt}")
+            llm_response = self.llm.invoke(
+                prompt,
+                message_history,
+                system_instruction=self.prompt_template.system_instructions,
+            )
+            answer = llm_response.content
+        result: dict[str, Any] = {"answer": answer}
         if return_context:
             result["retriever_result"] = retriever_result
         return RagResultModel(**result)

src/neo4j_graphrag/generation/types.py

@@ -43,6 +43,7 @@ class RagSearchModel(BaseModel):
     examples: str = ""
     retriever_config: dict[str, Any] = {}
     return_context: bool = False
+    response_fallback: str | None = None
 
 
 class RagResultModel(BaseModel):

tests/unit/test_graphrag.py

@@ -59,9 +59,9 @@ def test_graphrag_happy_path(retriever_mock: MagicMock, llm: MagicMock) -> None:
     )
     llm.invoke.return_value = LLMResponse(content="llm generated text")
 
-    res = rag.search("question")
+    res = rag.search("question", retriever_config={"top_k": 111})
 
-    retriever_mock.search.assert_called_once_with(query_text="question")
+    retriever_mock.search.assert_called_once_with(query_text="question", top_k=111)
     llm.invoke.assert_called_once_with(
         """Context:
 item content 1
@@ -263,6 +263,23 @@ def test_graphrag_happy_path_custom_system_instruction(
     assert res.answer == "llm generated text"
 
 
+def test_graphrag_happy_path_response_fallback(
+    retriever_mock: MagicMock, llm: MagicMock
+) -> None:
+    rag = GraphRAG(
+        retriever=retriever_mock,
+        llm=llm,
+    )
+    retriever_mock.search.return_value = RetrieverResult(items=[])
+    res = rag.search(
+        "question",
+        response_fallback="I can't answer this question without context",
+    )
+
+    assert llm.invoke.call_count == 0
+    assert res.answer == "I can't answer this question without context"
+
+
 def test_graphrag_initialization_error(llm: MagicMock) -> None:
     with pytest.raises(RagInitializationError) as excinfo:
         GraphRAG(