feat: aggregations in structured views

docs/how-to/views/custom_views_code.py

@@ -66,6 +66,7 @@ def execute(self, dry_run: bool = False) -> ViewExecutionResult:
 
         return ViewExecutionResult(results=filtered_data, context={})
 
+
 class CandidateView(FilteredIterableBaseView):
     def get_data(self) -> Iterable:
         return [

docs/quickstart/quickstart_code.py

@@ -6,7 +6,6 @@
 from sqlalchemy import create_engine
 from sqlalchemy.ext.automap import automap_base
 
-import dbally
 from dbally import decorators, SqlAlchemyBaseView
 from dbally.audit.event_handlers.cli_event_handler import CLIEventHandler
 from dbally.llms.litellm import LiteLLM
@@ -55,6 +54,13 @@ def from_country(self, country: str) -> sqlalchemy.ColumnElement:
         """
         return Candidate.country == country
 
+    @decorators.view_aggregation()
+    def count_by_column(self, filtered_query: sqlalchemy.Select, column_name: str) -> sqlalchemy.Select:  # pylint: disable=W0602, C0116, W9011
+        select = sqlalchemy.select(getattr(filtered_query.c, column_name),
+                                   sqlalchemy.func.count(filtered_query.c.name).label("count")) \
+            .group_by(getattr(filtered_query.c, column_name))
+        return select
+
 
 async def main():
     llm = LiteLLM(model_name="gpt-3.5-turbo")
@@ -63,7 +69,8 @@ async def main():
     collection = dbally.create_collection("recruitment", llm)
     collection.add(CandidateView, lambda: CandidateView(engine))
 
-    result = await collection.ask("Find me French candidates suitable for a senior data scientist position.")
+    result = await collection.ask("Could you find French candidates suitable for a senior data scientist position"
+                                  "and count the candidates university-wise?")
 
     print(f"The generated SQL query is: {result.context.get('sql')}")
     print()

src/dbally/exceptions.py

@@ -2,3 +2,10 @@ class DbAllyError(Exception):
     """
     Base class for all exceptions raised by db-ally.
     """
+
+
+class UnsupportedAggregationError(DbAllyError):
+    """
+    Error raised when AggregationFormatter is unable to construct a query
+    with given aggregation.
+    """

src/dbally/iql_generator/iql_generator.py

@@ -30,6 +30,7 @@ def __init__(self, llm: LLM, prompt_template: Optional[PromptTemplate[IQLGenerat
 
         Args:
             llm: LLM used to generate IQL
+            prompt_template: If not provided by the users is set to `default_iql_template`
         """
         self._llm = llm
         self._prompt_template = prompt_template or IQL_GENERATION_TEMPLATE
@@ -62,6 +63,7 @@ async def generate_iql(
             filters=filters,
             examples=examples,
         )
+
         formatted_prompt = self._prompt_template.format_prompt(prompt_format)
 
         for _ in range(n_retries + 1):
@@ -76,7 +78,7 @@ async def generate_iql(
                 # TODO: Move IQL query parsing to prompt response parser
                 return await IQLQuery.parse(
                     source=iql,
-                    allowed_functions=filters,
+                    allowed_functions=filters or [],
                     event_tracker=event_tracker,
                 )
             except IQLError as exc:

src/dbally/iql_generator/prompt.py

@@ -53,28 +53,29 @@ def __init__(
             question: Question to be asked.
             filters: List of filters exposed by the view.
             examples: List of examples to be injected into the conversation.
+            aggregations: List of aggregations exposed by the view.
         """
         super().__init__(examples)
         self.question = question
-        self.filters = "\n".join([str(filter) for filter in filters])
+        self.filters = "\n".join([str(condition) for condition in filters]) if filters else []
 
 
 IQL_GENERATION_TEMPLATE = PromptTemplate[IQLGenerationPromptFormat](
     [
         {
             "role": "system",
             "content": (
-                "You have access to API that lets you query a database:\n"
+                "You have access to an API that lets you query a database:\n"
                 "\n{filters}\n"
-                "Please suggest which one(s) to call and how they should be joined with logic operators (AND, OR, NOT).\n"
+                "Suggest which one(s) to call and how they should be joined with logic operators (AND, OR, NOT).\n"
                 "Remember! Don't give any comments, just the function calls.\n"
                 "The output will look like this:\n"
                 'filter1("arg1") AND (NOT filter2(120) OR filter3(True))\n'
                 "DO NOT INCLUDE arguments names in your response. Only the values.\n"
                 "You MUST use only these methods:\n"
                 "\n{filters}\n"
                 "It is VERY IMPORTANT not to use methods other than those listed above."
-                """If you DON'T KNOW HOW TO ANSWER DON'T SAY \"\", SAY: `UNSUPPORTED QUERY` INSTEAD! """
+                """If you DON'T KNOW HOW TO ANSWER DON'T SAY anything other than `UNSUPPORTED QUERY`"""
                 "This is CRUCIAL, otherwise the system will crash. "
             ),
         },

src/dbally/prompt/aggregation.py

@@ -0,0 +1,122 @@
+from typing import List, Optional
+
+from dbally.audit import EventTracker
+from dbally.exceptions import UnsupportedAggregationError
+from dbally.iql import IQLQuery
+from dbally.llms.base import LLM
+from dbally.llms.clients import LLMOptions
+from dbally.prompt.template import PromptFormat, PromptTemplate
+from dbally.views.exposed_functions import ExposedFunction
+
+
+def _validate_agg_response(llm_response: str) -> str:
+    """
+    Validates LLM response to IQL
+
+    Args:
+        llm_response: LLM response
+
+    Returns:
+        A string containing aggregations.
+
+    Raises:
+        UnsupportedAggregationError: When IQL generator is unable to construct a query
+        with given aggregation.
+    """
+    if "unsupported query" in llm_response.lower():
+        raise UnsupportedAggregationError
+    return llm_response
+
+
+class AggregationPromptFormat(PromptFormat):
+    """
+    Aggregation prompt format, providing a question and aggregation to be used in the conversation.
+    """
+
+    def __init__(
+        self,
+        question: str,
+        aggregations: List[ExposedFunction] = None,
+    ) -> None:
+        super().__init__()
+        self.question = question
+        self.aggregations = "\n".join([str(aggregation) for aggregation in aggregations]) if aggregations else []
+
+
+class AggregationFormatter:
+    """
+    Class used to manage choice and formatting of aggregation based on natural language question.
+    """
+
+    def __init__(self, llm: LLM, prompt_template: Optional[PromptTemplate[AggregationPromptFormat]] = None) -> None:
+        """
+        Constructs a new AggregationFormatter instance.
+
+        Args:
+            llm: LLM used to generate IQL
+            prompt_template: If not provided by the users is set to `AGGREGATION_GENERATION_TEMPLATE`
+        """
+        self._llm = llm
+        self._prompt_template = prompt_template or AGGREGATION_GENERATION_TEMPLATE
+
+    async def format_to_query_object(
+        self,
+        question: str,
+        event_tracker: EventTracker,
+        aggregations: List[ExposedFunction] = None,
+        llm_options: Optional[LLMOptions] = None,
+    ) -> IQLQuery:
+        """
+        Generates IQL in text form using LLM.
+
+        Args:
+            question: User question.
+            event_tracker: Event store used to audit the generation process.
+            aggregations: List of aggregations exposed by the view.
+            llm_options: Options to use for the LLM client.
+
+        Returns:
+            Generated aggregation query.
+        """
+        prompt_format = AggregationPromptFormat(
+            question=question,
+            aggregations=aggregations,
+        )
+
+        formatted_prompt = self._prompt_template.format_prompt(prompt_format)
+
+        response = await self._llm.generate_text(
+            prompt=formatted_prompt,
+            event_tracker=event_tracker,
+            options=llm_options,
+        )
+        # TODO: Move response parsing to llm generate_text method
+        agg = formatted_prompt.response_parser(response)
+        # TODO: Move IQL query parsing to prompt response parser
+        return await IQLQuery.parse(
+            source=agg,
+            allowed_functions=aggregations or [],
+            event_tracker=event_tracker,
+        )
+
+
+AGGREGATION_GENERATION_TEMPLATE = PromptTemplate[AggregationPromptFormat](
+    [
+        {
+            "role": "system",
+            "content": "You have access to an API that lets you query a database supporting a SINGLE aggregation.\n"
+            "When prompted for an aggregation, use the following methods: \n"
+            "{aggregations}"
+            "DO NOT INCLUDE arguments names in your response. Only the values.\n"
+            "You MUST use only these methods:\n"
+            "\n{aggregations}\n"
+            "It is VERY IMPORTANT not to use methods other than those listed above."
+            """If you DON'T KNOW HOW TO ANSWER DON'T SAY anything other than `UNSUPPORTED QUERY`"""
+            "This is CRUCIAL to put `UNSUPPORTED QUERY` text only, otherwise the system will crash. "
+            "Structure output to resemble the following pattern:\n"
+            'aggregation1("arg1", arg2)\n',
+        },
+        {"role": "user", "content": "{question}"},
+    ],
+    response_parser=_validate_agg_response,
+)

src/dbally/view_selection/prompt.py

@@ -35,7 +35,7 @@ def __init__(
             "role": "system",
             "content": (
                 "You are a very smart database programmer. "
-                "You have access to API that lets you query a database:\n"
+                "You have access to an API that lets you query a database:\n"
                 "First you need to select a class to query, based on its description and the user question. "
                 "You have the following classes to choose from:\n"
                 "{views}\n"

src/dbally/views/decorators.py

@@ -14,3 +14,18 @@ def wrapped(func: typing.Callable) -> typing.Callable:  # pylint: disable=missin
         return func
 
     return wrapped
+
+
+def view_aggregation() -> typing.Callable:
+    """
+    Decorator for marking a method as an aggregation
+
+    Returns:
+        Function that returns the decorated method
+    """
+
+    def wrapped(func: typing.Callable) -> typing.Callable:  # pylint: disable=missing-return-doc
+        func._methodDecorator = view_aggregation  # type:ignore # pylint: disable=protected-access
+        return func
+
+    return wrapped

src/dbally/views/methods_base.py

@@ -15,7 +15,10 @@ class MethodsBaseView(BaseStructuredView, metaclass=abc.ABCMeta):
     """
 
     # Method arguments that should be skipped when listing methods
-    HIDDEN_ARGUMENTS = ["self", "select", "return"]
+    HIDDEN_ARGUMENTS = ["self", "select", "return", "filtered_query"]
+
+    def __init__(self):
+        self._filtered_query = None
 
     @classmethod
     def list_methods_by_decorator(cls, decorator: Callable) -> List[ExposedFunction]:
@@ -56,6 +59,15 @@ def list_filters(self) -> List[ExposedFunction]:
         """
         return self.list_methods_by_decorator(decorators.view_filter)
 
+    def list_aggregations(self) -> List[ExposedFunction]:
+        """
+        List aggregations in the given view
+
+        Returns:
+            Aggregations defined inside the View and decorated with `decorators.view_aggregation`.
+        """
+        return self.list_methods_by_decorator(decorators.view_aggregation)
+
     def _method_with_args_from_call(
         self, func: syntax.FunctionCall, method_decorator: Callable
     ) -> Tuple[Callable, list]:
@@ -64,7 +76,8 @@ def _method_with_args_from_call(
 
         Args:
             func: IQL FunctionCall node
-            method_decorator: The decorator that thhe method should have
+            method_decorator: The decorator that the method should have
+                            (currently allows discrimination between filters and aggregations)
 
         Returns:
             Tuple with the method object and its arguments
@@ -99,3 +112,19 @@ async def call_filter_method(self, func: syntax.FunctionCall) -> Any:
         if inspect.iscoroutinefunction(method):
             return await method(*args)
         return method(*args)
+
+    async def call_aggregation_method(self, func: syntax.FunctionCall) -> Any:
+        """
+        Converts a IQL FunctonCall aggregation to a method call. If the method is a coroutine, it will be awaited.
+
+        Args:
+            func: IQL FunctionCall node
+
+        Returns:
+            The result of the method call
+        """
+        method, args = self._method_with_args_from_call(func, decorators.view_aggregation)
+
+        if inspect.iscoroutinefunction(method):
+            return await method(self._filtered_query, *args)
+        return method(self._filtered_query, *args)

src/dbally/views/pandas_base.py

@@ -36,6 +36,15 @@ async def apply_filters(self, filters: IQLQuery) -> None:
         """
         self._filter_mask = await self.build_filter_node(filters.root)
 
+    async def apply_aggregation(self, aggregation: IQLQuery) -> None:
+        """
+        Applies the aggregation of choice to the view.
+
+        Args:
+            aggregation: IQLQuery object representing the aggregation to apply
+        """
+        # TODO - to be covered in a separate ticket.
+
     async def build_filter_node(self, node: syntax.Node) -> pd.Series:
         """
         Converts a filter node from the IQLQuery to a Pandas Series representing

src/dbally/views/sqlalchemy_base.py

@@ -17,6 +17,7 @@ def __init__(self, sqlalchemy_engine: sqlalchemy.engine.Engine) -> None:
         super().__init__()
         self._select = self.get_select()
         self._sqlalchemy_engine = sqlalchemy_engine
+        self._filtered_query = None
 
     @abc.abstractmethod
     def get_select(self) -> sqlalchemy.Select:
@@ -27,6 +28,15 @@ def get_select(self) -> sqlalchemy.Select:
         which will be used to build the query.
         """
 
+    def _get_filtered_query(self) -> sqlalchemy.Subquery:
+        """
+        Creates the initial sqlalchemy.Subquery object, which will be used to build the query.
+
+        Returns:
+            The sqlalchemy.Subquery object based on private _select attribute.
+        """
+        return self._select.subquery("subquery")
+
     async def apply_filters(self, filters: IQLQuery) -> None:
         """
         Applies the chosen filters to the view.
@@ -64,6 +74,16 @@ async def _build_filter_bool_op(self, bool_op: syntax.BoolOp) -> sqlalchemy.Colu
             return alchemy_op(await self._build_filter_node(bool_op.child))
         raise ValueError(f"BoolOp {bool_op} has no children")
 
+    async def apply_aggregation(self, aggregation: syntax.FunctionCall) -> None:
+        """
+        Creates a subquery based on existing and calls the aggregation method.
+
+        Args:
+            aggregation: IQLQuery object representing the filters to apply
+        """
+        self._filtered_query = self._get_filtered_query()
+        self._select = await self.call_aggregation_method(aggregation)
+
     def execute(self, dry_run: bool = False) -> ViewExecutionResult:
         """
         Executes the generated SQL query and returns the results.
@@ -77,6 +97,7 @@ def execute(self, dry_run: bool = False) -> ViewExecutionResult:
         """
 
         results = []
+
         sql = str(self._select.compile(bind=self._sqlalchemy_engine, compile_kwargs={"literal_binds": True}))
 
         if not dry_run: