feat: Add result ranking prompt for NLDataframeRetriever

This commit adds a new result ranking prompt to the NLDataframeRetriever class. The prompt allows users to provide a schema and query, and asks them to rate the relevance of the schema in modeling the domain of the query. The relevance must be a number between 0 and 1, where 1 indicates high relevance and 0 indicates low relevance.

The significant changes include:
- Added DEFAULT_RESULT_RANKING_TMPL constant for the result ranking template
- Added DEFAULT_RESULT_RANKING_PROMPTROMPT constant for the result ranking prompt template
- Updated NLDataframeRetriever constructor to accept a result_ranking_prompt parameter
- Initialized self._result_ranking_prompt with either the provided parameter or the default prompt template
- Modified NLDataframeRetriever.complete() method to use self._result_ranking_prompt as part of the LLM completion request

These changes allow users of NLDataframeRetriever to easily rank the relevance of schemas in modeling their queries, providing more accurate results.

llama-index-experimental/llama_index/experimental/retrievers/llama-index-retrievers-natural-language/nl_data_frame_retierver.py

@@ -52,6 +52,21 @@
     prompt_type=PromptType.CUSTOM,
 )
 
+DEFAULT_RESULT_RANKING_TMPL = """\
+given the schema:{schema}\
+and the query: {query}\
+how relevant is the schema?\
+the relevance must be a number between 0 and 1 where 1 indicates that the schema is able to model the domain of the query and 0 indicates that the schema is not able to model the domain of the query.\
+
+produce only the numeric value and nothing else.\
+relevance:
+"""
+
+DEFAULT_RESULT_RANKING_PROMPTROMPT = PromptTemplate(
+    DEFAULT_RESULT_RANKING_TMPL,
+    prompt_type=PromptType.CUSTOM,
+)
+
 
 class NLDataframeRetriever(BaseRetriever):
     def __init__(
@@ -62,13 +77,17 @@ def __init__(
         text_to_sql_prompt: Optional[BasePromptTemplate] = None,
         schema_to_owl_prompt: Optional[BasePromptTemplate] = None,
         schema_use_detection_prompt: Optional[BasePromptTemplate] = None,
+        result_ranking_prompt: Optional[BasePromptTemplate] = None,
         similarity_top_k: int = DEFAULT_SIMILARITY_TOP_K,
         callback_manager: Optional[CallbackManager] = None,
         verbose: bool = False,
     ):
         self._llm = resolve_llm(llm)
         self._similarity_top_k = similarity_top_k
         self._text_to_sql_prompt = text_to_sql_prompt or DEFAULT_TEXT_TO_SQL_PROMPT
+        self._result_ranking_prompt = (
+            result_ranking_prompt or DEFAULT_RESULT_RANKING_PROMPTROMPT
+        )
         self._schema_to_owl_prompt = (
             schema_to_owl_prompt or DEFAULT_OWL_GENERATOR_PROMPT
         )
@@ -142,16 +161,9 @@ async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
         )
 
         rank = self._llm.complete(
-            f"""
-given the schema:{tables_desc_str}
-and the query: {query_bundle.query_str}
-how relevant is the schema?
-the relevance must be a number between 0 and 1 where 1 indicates that the schema is able to model the domain of the query and 0 indicates that the schema is not able to model the domain of the query.
-
-produce only the numeric value and nothing else.
-relevance:
-
-        """
+            self._result_ranking_prompt,
+            query=query_bundle.query_str,
+            schema=tables_desc_str,
         )
 
         score = 1.0