refactor: refactor LLMOptions

src/dbally/collection.py

@@ -8,7 +8,7 @@
 from dbally.audit.event_tracker import EventTracker
 from dbally.data_models.audit import RequestEnd, RequestStart
 from dbally.data_models.execution_result import ExecutionResult
-from dbally.llm_client.base import LLMClient
+from dbally.llm_client.base import LLMClient, LLMOptions
 from dbally.nl_responder.nl_responder import NLResponder
 from dbally.similarity.index import AbstractSimilarityIndex
 from dbally.utils.errors import NoViewFoundError
@@ -157,7 +157,13 @@ def list(self) -> Dict[str, str]:
             name: (textwrap.dedent(view.__doc__).strip() if view.__doc__ else "") for name, view in self._views.items()
         }
 
-    async def ask(self, question: str, dry_run: bool = False, return_natural_response: bool = False) -> ExecutionResult:
+    async def ask(
+        self,
+        question: str,
+        dry_run: bool = False,
+        return_natural_response: bool = False,
+        llm_options: Optional[LLMOptions] = None,
+    ) -> ExecutionResult:
         """
         Ask question in a text form and retrieve the answer based on the available views.
 
@@ -174,6 +180,7 @@ async def ask(self, question: str, dry_run: bool = False, return_natural_respons
             dry_run: if True, only generate the query without executing it
             return_natural_response: if True (and dry_run is False as natural response requires query results),
                                      the natural response will be included in the answer
+            llm_options: options to use for the LLM client.
 
         Returns:
             ExecutionResult object representing the result of the query execution.
@@ -197,7 +204,12 @@ async def ask(self, question: str, dry_run: bool = False, return_natural_respons
         if len(views) == 1:
             selected_view = next(iter(views))
         else:
-            selected_view = await self._view_selector.select_view(question, views, event_tracker)
+            selected_view = await self._view_selector.select_view(
+                question=question,
+                views=views,
+                event_tracker=event_tracker,
+                llm_options=llm_options,
+            )
 
         view = self.get(selected_view)
 
@@ -208,12 +220,18 @@ async def ask(self, question: str, dry_run: bool = False, return_natural_respons
             event_tracker=event_tracker,
             n_retries=self.n_retries,
             dry_run=dry_run,
+            llm_options=llm_options,
         )
         end_time_view = time.monotonic()
 
         textual_response = None
         if not dry_run and return_natural_response:
-            textual_response = await self._nl_responder.generate_response(view_result, question, event_tracker)
+            textual_response = await self._nl_responder.generate_response(
+                result=view_result,
+                question=question,
+                event_tracker=event_tracker,
+                llm_options=llm_options,
+            )
 
         result = ExecutionResult(
             results=view_result.results,

src/dbally/iql_generator/iql_generator.py

@@ -3,7 +3,7 @@
 
 from dbally.audit.event_tracker import EventTracker
 from dbally.iql_generator.iql_prompt_template import IQLPromptTemplate, default_iql_template
-from dbally.llm_client.base import LLMClient
+from dbally.llm_client.base import LLMClient, LLMOptions
 from dbally.prompts.prompt_builder import PromptBuilder
 from dbally.views.exposed_functions import ExposedFunction
 
@@ -49,6 +49,7 @@ async def generate_iql(
         question: str,
         event_tracker: EventTracker,
         conversation: Optional[IQLPromptTemplate] = None,
+        llm_options: Optional[LLMOptions] = None,
     ) -> Tuple[str, IQLPromptTemplate]:
         """
         Uses LLM to generate IQL in text form
@@ -58,6 +59,7 @@ async def generate_iql(
             filters: list of filters exposed by the view
             event_tracker: event store used to audit the generation process
             conversation: conversation to be continued
+            llm_options: options to use for the LLM client
 
         Returns:
             IQL - iql generated based on the user question
@@ -70,6 +72,7 @@ async def generate_iql(
             template=template,
             fmt={"filters": filters_for_prompt, "question": question},
             event_tracker=event_tracker,
+            options=llm_options,
         )
 
         iql_filters = self._prompt_template.llm_response_parser(llm_response)

src/dbally/llm_client/base.py

@@ -2,26 +2,38 @@
 # pylint: disable=W9015,R0914
 
 import abc
-from typing import Dict, List, Optional, Union
+from abc import ABC
+from dataclasses import asdict, dataclass
+from typing import Dict, Generic, Optional, Type, TypeVar, Union
 
 from dbally.audit.event_tracker import EventTracker
 from dbally.data_models.audit import LLMEvent
-from dbally.data_models.llm_options import LLMOptions
 from dbally.prompts import ChatFormat, PromptBuilder, PromptTemplate
 
+LLMClientOptions = TypeVar("LLMClientOptions")
 
-class LLMClient(abc.ABC):
+
+@dataclass
+class LLMOptions(ABC):
+    """
+    Abstract dataclass that represents all available LLM call options.
+    """
+
+    dict = asdict
+
+
+class LLMClient(Generic[LLMClientOptions], ABC):
     """
     Abstract client for interaction with LLM.
 
-    It accepts parameters including the template, format, event tracker,
-    and optional generation parameters like frequency_penalty, max_tokens, and temperature
-    (the full list of options is provided by the [`LLMOptions` class][dbally.data_models.llm_options.LLMOptions]).
     It constructs a prompt using the `PromptBuilder` instance and generates text using the `self.call` method.
     """
 
-    def __init__(self, model_name: str):
+    _options_cls: Type[LLMClientOptions]
+
+    def __init__(self, model_name: str, default_options: Optional[LLMClientOptions] = None) -> None:
         self.model_name = model_name
+        self.default_options = default_options or self._options_cls()
         self._prompt_builder = PromptBuilder(self.model_name)
 
     async def text_generation(  # pylint: disable=R0913
@@ -30,41 +42,35 @@ async def text_generation(  # pylint: disable=R0913
         fmt: dict,
         *,
         event_tracker: Optional[EventTracker] = None,
-        frequency_penalty: Optional[float] = 0.0,
-        max_tokens: Optional[int] = 128,
-        n: Optional[int] = 1,
-        presence_penalty: Optional[float] = 0.0,
-        seed: Optional[int] = None,
-        stop: Optional[Union[str, List[str]]] = None,
-        temperature: Optional[float] = 1.0,
-        top_p: Optional[float] = 1.0,
+        options: Optional[LLMClientOptions] = None,
     ) -> str:
         """
         For a given a PromptType and format dict creates a prompt and
         returns the response from LLM.
 
+        Args:
+            template: Prompt template in system/user/assistant openAI format.
+            fmt: Dictionary with formatting.
+            event_tracker: Event store used to audit the generation process.
+            options: options to use for the LLM client.
+
         Returns:
             Text response from LLM.
         """
-
-        options = LLMOptions(
-            frequency_penalty=frequency_penalty,
-            max_tokens=max_tokens,
-            n=n,
-            presence_penalty=presence_penalty,
-            seed=seed,
-            stop=stop,
-            temperature=temperature,
-            top_p=top_p,
-        )
+        options = options if options else self.default_options
 
         prompt = self._prompt_builder.build(template, fmt)
 
         event = LLMEvent(prompt=prompt, type=type(template).__name__)
 
         event_tracker = event_tracker or EventTracker()
         async with event_tracker.track_event(event) as span:
-            event.response = await self.call(prompt, template.response_format, options, event)
+            event.response = await self.call(
+                prompt=prompt,
+                response_format=template.response_format,
+                options=options,
+                event=event,
+            )
             span(event)
 
         return event.response

src/dbally/llm_client/openai_client.py

@@ -1,36 +1,65 @@
-from typing import Dict, Optional, Union
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Union
+
+from openai import NOT_GIVEN, NotGiven
 
 from dbally.data_models.audit import LLMEvent
-from dbally.data_models.llm_options import LLMOptions
 from dbally.llm_client.base import LLMClient
 from dbally.prompts import ChatFormat
 
+from .base import LLMOptions
+
 
-class OpenAIClient(LLMClient):
+@dataclass
+class OpenAIOptions(LLMOptions):
+    """
+    Dataclass that represents all available LLM call options for the OpenAI API. Each of them is
+    described in the [OpenAI API documentation](https://platform.openai.com/docs/api-reference/chat/create.)
+    """
+
+    frequency_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN
+    max_tokens: Union[Optional[int], NotGiven] = NOT_GIVEN
+    n: Union[Optional[int], NotGiven] = NOT_GIVEN
+    presence_penalty: Union[Optional[float], NotGiven] = NOT_GIVEN
+    seed: Union[Optional[int], NotGiven] = NOT_GIVEN
+    stop: Union[Optional[Union[str, List[str]]], NotGiven] = NOT_GIVEN
+    temperature: Union[Optional[float], NotGiven] = NOT_GIVEN
+    top_p: Union[Optional[float], NotGiven] = NOT_GIVEN
+
+
+class OpenAIClient(LLMClient[OpenAIOptions]):
     """
     `OpenAIClient` is a class designed to interact with OpenAI's language model (LLM) endpoints,
     particularly for the GPT models.
 
     Args:
-        model_name: Name of the [OpenAI's model](https://platform.openai.com/docs/models) to be used,
+        model_name: Name of the [OpenAI's model](https://platform.openai.com/docs/models) to be used,\
             default is "gpt-3.5-turbo".
         api_key: OpenAI's API key. If None OPENAI_API_KEY environment variable will be used
+        default_options: Default options to be used in the LLM calls.
     """
 
-    def __init__(self, model_name: str = "gpt-3.5-turbo", api_key: Optional[str] = None) -> None:
+    _options_cls = OpenAIOptions
+
+    def __init__(
+        self,
+        model_name: str = "gpt-3.5-turbo",
+        api_key: Optional[str] = None,
+        default_options: Optional[OpenAIOptions] = None,
+    ) -> None:
         try:
             from openai import AsyncOpenAI  # pylint: disable=import-outside-toplevel
         except ImportError as exc:
             raise ImportError("You need to install openai package to use GPT models") from exc
 
-        super().__init__(model_name)
+        super().__init__(model_name=model_name, default_options=default_options)
         self._client = AsyncOpenAI(api_key=api_key)
 
     async def call(
         self,
         prompt: Union[str, ChatFormat],
         response_format: Optional[Dict[str, str]],
-        options: LLMOptions,
+        options: OpenAIOptions,
         event: LLMEvent,
     ) -> str:
         """
@@ -52,7 +81,10 @@ async def call(
             response_format = None
 
         response = await self._client.chat.completions.create(
-            messages=prompt, model=self.model_name, response_format=response_format, **options.dict()  # type: ignore
+            messages=prompt,
+            model=self.model_name,
+            response_format=response_format,
+            **options.dict(),  # type: ignore
         )
 
         event.completion_tokens = response.usage.completion_tokens

src/dbally/nl_responder/nl_responder.py

@@ -5,7 +5,7 @@
 
 from dbally.audit.event_tracker import EventTracker
 from dbally.data_models.execution_result import ViewExecutionResult
-from dbally.llm_client.base import LLMClient
+from dbally.llm_client.base import LLMClient, LLMOptions
 from dbally.nl_responder.nl_responder_prompt_template import NLResponderPromptTemplate, default_nl_responder_template
 from dbally.nl_responder.query_explainer_prompt_template import (
     QueryExplainerPromptTemplate,
@@ -46,14 +46,21 @@ def __init__(
         )
         self._max_tokens_count = max_tokens_count
 
-    async def generate_response(self, result: ViewExecutionResult, question: str, event_tracker: EventTracker) -> str:
+    async def generate_response(
+        self,
+        result: ViewExecutionResult,
+        question: str,
+        event_tracker: EventTracker,
+        llm_options: Optional[LLMOptions] = None,
+    ) -> str:
         """
         Uses LLM to generate a response in natural language form.
 
         Args:
             result: object representing the result of the query execution
             question: user question
             event_tracker: event store used to audit the generation process
+            llm_options: options to use for the LLM client.
 
         Returns:
             Natural language response to the user question.
@@ -82,6 +89,7 @@ async def generate_response(self, result: ViewExecutionResult, question: str, ev
                 template=self._query_explainer_prompt_template,
                 fmt={"question": question, "query": query, "number_of_results": len(result.results)},
                 event_tracker=event_tracker,
+                options=llm_options,
             )
 
             return llm_response
@@ -90,6 +98,7 @@ async def generate_response(self, result: ViewExecutionResult, question: str, ev
             template=self._nl_responder_prompt_template,
             fmt={"rows": _promptify_rows(result.results), "question": question},
             event_tracker=event_tracker,
+            options=llm_options,
         )
         return llm_response
 

src/dbally/view_selection/base.py

@@ -1,21 +1,29 @@
 import abc
-from typing import Dict
+from typing import Dict, Optional
 
 from dbally.audit.event_tracker import EventTracker
+from dbally.llm_client.base import LLMOptions
 
 
 class ViewSelector(abc.ABC):
     """Base class for view selectors."""
 
     @abc.abstractmethod
-    async def select_view(self, question: str, views: Dict[str, str], event_tracker: EventTracker) -> str:
+    async def select_view(
+        self,
+        question: str,
+        views: Dict[str, str],
+        event_tracker: EventTracker,
+        llm_options: Optional[LLMOptions] = None,
+    ) -> str:
         """
         Based on user question and list of available views select the most relevant one.
 
         Args:
             question: user question asked in the natural language e.g "Do we have any data scientists?"
             views: dictionary of available view names with corresponding descriptions.
             event_tracker: event tracker used to audit the selection process.
+            llm_options: options to use for the LLM client.
 
         Returns:
             The most relevant view name.