Optimize parameters processing

constraints

@@ -15,7 +15,7 @@ certifi==2024.2.2
  # requests
 charset-normalizer==3.3.2
  # via requests
-coverage==7.5.2
+coverage==7.5.3
  # via pytest-cov
 distro==1.9.0
  # via openai
@@ -38,27 +38,27 @@ markdown-it-py==3.0.0
  # via rich
 mdurl==0.1.2
  # via markdown-it-py
-nodeenv==1.8.0
+nodeenv==1.9.0
  # via pyright
 numpy==1.26.4
  # via draive (pyproject.toml)
-openai==1.30.3
+openai==1.30.4
  # via draive (pyproject.toml)
 packaging==24.0
  # via pytest
 pbr==6.0.0
  # via stevedore
 pluggy==1.5.0
  # via pytest
-pydantic==2.7.1
+pydantic==2.7.2
  # via
  # draive (pyproject.toml)
  # openai
-pydantic-core==2.18.2
+pydantic-core==2.18.3
  # via pydantic
 pygments==2.18.0
  # via rich
-pyright==1.1.364
+pyright==1.1.365
  # via draive (pyproject.toml)
 pytest==7.4.4
  # via
@@ -77,10 +77,8 @@ requests==2.32.2
  # via tiktoken
 rich==13.7.1
  # via bandit
-ruff==0.4.5
+ruff==0.4.6
  # via draive (pyproject.toml)
-setuptools==70.0.0
- # via nodeenv
 sniffio==1.3.1
  # via
  # anyio

examples/BasicConversation.ipynb

@@ -43,14 +43,20 @@
  "metadata": {},
  "outputs": [
  {
- "ename": "TypeError",
- "evalue": "OpenAIChatConfig.__init__() missing 8 required keyword-only arguments: 'temperature', 'top_p', 'frequency_penalty', 'max_tokens', 'seed', 'response_format', 'vision_details', and 'timeout'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[3], line 17\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdraive\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 2\u001b[0m LMM,\n\u001b[1;32m 3\u001b[0m ConversationMessage,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 9\u001b[0m openai_lmm_invocation,\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# initialize dependencies and configuration\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mnew(\n\u001b[1;32m 14\u001b[0m dependencies\u001b[38;5;241m=\u001b[39m[OpenAIClient], \u001b[38;5;66;03m# use OpenAI client\u001b[39;00m\n\u001b[1;32m 15\u001b[0m state\u001b[38;5;241m=\u001b[39m[\n\u001b[1;32m 16\u001b[0m LMM(invocation\u001b[38;5;241m=\u001b[39mopenai_lmm_invocation), \u001b[38;5;66;03m# define used LMM\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[43mOpenAIChatConfig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgpt-3.5-turbo-0125\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;66;03m# configure OpenAI model\u001b[39;00m\n\u001b[1;32m 18\u001b[0m ],\n\u001b[1;32m 19\u001b[0m ):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# request conversation completion\u001b[39;00m\n\u001b[1;32m 21\u001b[0m response: ConversationMessage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m conversation_completion( \u001b[38;5;66;03m# noqa: PLE1142\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# provide a prompt instruction\u001b[39;00m\n\u001b[1;32m 23\u001b[0m instruction\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a helpful assistant.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 29\u001b[0m ),\n\u001b[1;32m 30\u001b[0m )\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mprint\u001b[39m(response)\n",
- "\u001b[0;31mTypeError\u001b[0m: OpenAIChatConfig.__init__() missing 8 required keyword-only arguments: 'temperature', 'top_p', 'frequency_penalty', 'max_tokens', 'seed', 'response_format', 'vision_details', and 'timeout'"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"identifier\": \"d5b98674fd024c0b977a49bfd9d3caa7\",\n",
+ " \"role\": \"model\",\n",
+ " \"author\": null,\n",
+ " \"created\": \"2024-05-29T15:26:35.346493+00:00\",\n",
+ " \"content\": {\n",
+ " \"elements\": [\n",
+ " \"The current UTC time and date is Wednesday, 29 May 2024, 15:26:34.\"\n",
+ " ]\n",
+ " }\n",
+ "}\n"
  ]
  }
  ],

src/draive/__init__.py

@@ -70,7 +70,18 @@
  openai_lmm_invocation,
  openai_tokenize_text,
 )
-from draive.parameters import Argument, BasicValue, DataModel, Field, ParameterPath, State
+from draive.parameters import (
+ Argument,
+ BasicValue,
+ DataModel,
+ Field,
+ ParameterDefaultFactory,
+ ParameterPath,
+ ParameterValidationContext,
+ ParameterValidator,
+ ParameterVerifier,
+ State,
+)
 from draive.scope import (
  ScopeDependencies,
  ScopeDependency,
@@ -119,8 +130,8 @@
  getenv_float,
  getenv_int,
  getenv_str,
+ is_missing,
  load_env,
- missing,
  not_missing,
  setup_logging,
  split_sequence,
@@ -197,7 +208,7 @@
  "MetricsTrace",
  "MetricsTraceReport",
  "MetricsTraceReporter",
- "missing",
+ "is_missing",
  "Missing",
  "MISSING",
  "mistral_embed_text",
@@ -252,4 +263,8 @@
  "VideoContent",
  "VideoDataContent",
  "VideoURLContent",
+ "ParameterDefaultFactory",
+ "ParameterValidationContext",
+ "ParameterValidator",
+ "ParameterVerifier",
 ]

src/draive/conversation/lmm.py

@@ -164,6 +164,7 @@ async def _lmm_conversation_completion(
  **extra,
  ):
  case LMMCompletion() as completion:
+ ctx.log_debug("Received conversation result")
  response_message: ConversationMessage = ConversationMessage(
  role="model",
  created=datetime.now(UTC),
@@ -176,6 +177,7 @@ async def _lmm_conversation_completion(
  return response_message
 
  case LMMToolRequests() as tool_requests:
+ ctx.log_debug("Received conversation tool calls")
  context.append(tool_requests)
  responses: list[LMMToolResponse] = await toolbox.respond(tool_requests)
 
@@ -221,6 +223,7 @@ async def _lmm_conversation_completion_stream(
  ):
  match part:
  case LMMCompletionChunk() as chunk:
+ ctx.log_debug("Received conversation result chunk")
  response_content = response_content.extending(chunk.content)
 
  yield ConversationMessageChunk(
@@ -230,6 +233,7 @@ async def _lmm_conversation_completion_stream(
  # keep yielding parts
 
  case LMMToolRequests() as tool_requests:
+ ctx.log_debug("Received conversation tool calls")
  assert ( # nosec: B101
  not response_content
  ), "Tools and completion message should not be used at the same time"
@@ -264,6 +268,7 @@ async def _lmm_conversation_completion_stream(
  break # exit the loop with result
 
  if response_content:
+ ctx.log_debug("Remembering conversation result")
  # remember messages when finishing stream
  await conversation_memory.remember(
  request_message,

src/draive/generation/model/lmm.py

@@ -21,7 +21,7 @@
 ]
 
 
-async def lmm_generate_model[Generated: DataModel]( # noqa: PLR0913
+async def lmm_generate_model[Generated: DataModel]( # noqa: PLR0913, C901
  generated: type[Generated],
  /,
  *,
@@ -57,7 +57,7 @@ async def lmm_generate_model[Generated: DataModel]( # noqa: PLR0913
  if variable := schema_variable:
  instruction_message = LMMInstruction.of(
  generation_instruction.updated(
- **{variable: generated.json_schema()},
+ **{variable: generated.json_schema(indent=2)},
  ),
  )
 
@@ -66,7 +66,7 @@ async def lmm_generate_model[Generated: DataModel]( # noqa: PLR0913
  generation_instruction.extended(
  DEFAULT_INSTRUCTION_EXTENSION,
  joiner="\n\n",
- schema=generated.json_schema(),
+ schema=generated.json_schema(indent=2),
  )
  )
 
@@ -93,9 +93,11 @@ async def lmm_generate_model[Generated: DataModel]( # noqa: PLR0913
  **extra,
  ):
  case LMMCompletion() as completion:
+ ctx.log_debug("Received model generation result")
  return generated.from_json(completion.content.as_string())
 
  case LMMToolRequests() as tool_requests:
+ ctx.log_debug("Received model generation tool calls")
  context.append(tool_requests)
  responses: list[LMMToolResponse] = await toolbox.respond(tool_requests)
 

src/draive/generation/text/lmm.py

@@ -63,9 +63,11 @@ async def lmm_generate_text(
  **extra,
  ):
  case LMMCompletion() as completion:
+ ctx.log_debug("Received text generation result")
  return completion.content.as_string()
 
  case LMMToolRequests() as tool_requests:
+ ctx.log_debug("Received text generation tool calls")
  context.append(tool_requests)
  responses: list[LMMToolResponse] = await toolbox.respond(tool_requests)
 

src/draive/lmm/tool.py

@@ -7,9 +7,15 @@
 )
 from uuid import uuid4
 
+from draive.lmm.errors import ToolException
 from draive.lmm.state import ToolCallContext, ToolStatusStream
 from draive.metrics import ArgumentsTrace, ResultTrace
-from draive.parameters import Function, ParametrizedFunction, ToolSpecification
+from draive.parameters import (
+ Function,
+ ParameterSpecification,
+ ParametrizedFunction,
+ ToolSpecification,
+)
 from draive.scope import ctx
 from draive.types import MultimodalContent, MultimodalContentElement
 from draive.utils import freeze, not_missing
@@ -23,7 +29,7 @@
 
 
 class ToolAvailabilityCheck(Protocol):
- def __call__(self) -> None: ...
+ def __call__(self) -> bool: ...
 
 
 @final
@@ -41,23 +47,38 @@ def __init__( # noqa: PLR0913
  direct_result: bool = False,
  ) -> None:
  super().__init__(function=function)
- if not_missing(self._parameters.specification):
- self.specification: ToolSpecification = {
- "type": "function",
- "function": {
- "name": name,
- "parameters": self._parameters.specification,
- "description": description or "",
- },
- }
+ aliased_required: list[str] = []
+ parameters: dict[str, ParameterSpecification] = {}
+ for parameter in self._parameters.values():
+ if not_missing(parameter.specification):
+ parameters[parameter.alias or parameter.name] = parameter.specification
+
+ else:
+ raise TypeError(
+ f"{function.__qualname__} can't be represented as a tool"
+ f" - argument '{parameter.name}' is missing specification."
+ )
 
- else:
- raise TypeError(f"{function.__qualname__} can't be represented as a tool")
+ if not (parameter.has_default and parameter.allows_missing):
+ aliased_required.append(parameter.alias or parameter.name)
+
+ self.specification: ToolSpecification = {
+ "type": "function",
+ "function": {
+ "name": name,
+ "parameters": {
+ "type": "object",
+ "properties": parameters,
+ "required": aliased_required,
+ },
+ "description": description or "",
+ },
+ }
 
  self.name: str = name
  self._direct_result: bool = direct_result
  self._check_availability: ToolAvailabilityCheck = availability_check or (
- lambda: None # available by default
+ lambda: True # available by default
  )
  self.format_result: Callable[[Result], MultimodalContent | MultimodalContentElement] = (
  format_result
@@ -71,8 +92,7 @@ def __init__( # noqa: PLR0913
  @property
  def available(self) -> bool:
  try:
- self._check_availability()
- return True
+ return self._check_availability()
 
  except Exception:
  return False
@@ -101,7 +121,9 @@ async def _toolbox_call(
  call_context.report("STARTED")
 
  try:
- self._check_availability()
+ if not self.available:
+ raise ToolException(f"{self.name} is not available!")
+
  result: Result = await super().__call__(**arguments) # pyright: ignore[reportCallIssue]
  ctx.record(ResultTrace.of(result))
 

src/draive/metrics/log_reporter.py

@@ -3,7 +3,7 @@
 
 from draive.metrics.reporter import MetricsTraceReport, MetricsTraceReporter
 from draive.parameters import ParametrizedData
-from draive.utils import missing
+from draive.utils import is_missing
 
 __all__ = [
  "metrics_log_reporter",
@@ -150,7 +150,7 @@ def _raw_value_report(
  list_items_limit: int | None,
  item_character_limit: int | None,
 ) -> str | None:
- if missing(value):
+ if is_missing(value):
  return None # skip missing
 
  # workaround for pydantic models

src/draive/mistral/lmm.py

@@ -86,6 +86,7 @@ async def mistral_lmm_invocation(
  ),
  ],
  ):
+ ctx.log_debug("Requested Mistral lmm")
  client: MistralClient = ctx.dependency(MistralClient)
  config: MistralChatConfig = ctx.state(MistralChatConfig).updated(**extra)
  match output:

src/draive/openai/lmm.py

@@ -104,6 +104,7 @@ async def openai_lmm_invocation(
  ),
  ],
  ):
+ ctx.log_debug("Requested OpenAI lmm")
  client: OpenAIClient = ctx.dependency(OpenAIClient)
  config: OpenAIChatConfig = ctx.state(OpenAIChatConfig).updated(**extra)
  match output:
@@ -138,7 +139,7 @@ async def openai_lmm_invocation(
  )
 
 
-def _convert_content_element(
+def _convert_content_element( # noqa: C901
  element: MultimodalContentElement,
  config: OpenAIChatConfig,
 ) -> ChatCompletionContentPartParam: