Retain return type from @dispatcher.span (#17817)

Author: GICodeWarrior

llama-index-core/llama_index/core/chat_engine/condense_plus_context.py

@@ -347,6 +347,7 @@ def stream_chat(
         )
 
         response = synthesizer.synthesize(message, context_nodes)
+        assert isinstance(response, StreamingResponse)
 
         def wrapped_gen(response: StreamingResponse) -> ChatResponseGen:
             full_response = ""
@@ -405,6 +406,7 @@ async def astream_chat(
         )
 
         response = await synthesizer.asynthesize(message, context_nodes)
+        assert isinstance(response, AsyncStreamingResponse)
 
         async def wrapped_gen(response: AsyncStreamingResponse) -> ChatResponseAsyncGen:
             full_response = ""

llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py

@@ -45,7 +45,7 @@ async def _aget_retrieved_ids_and_texts(
 
         return (
             [node.node.node_id for node in retrieved_nodes],
-            [node.node.text for node in retrieved_nodes],
+            [node.text for node in retrieved_nodes],
         )
 
 
@@ -84,7 +84,7 @@ async def _aget_retrieved_ids_and_texts(
             node = scored_node.node
             if isinstance(node, ImageNode):
                 image_nodes.append(node)
-            if node.text:
+            if isinstance(node, TextNode):
                 text_nodes.append(node)
 
         if mode == "text":

llama-index-core/llama_index/core/extractors/metadata_extractors.py

@@ -20,7 +20,7 @@
 (similar with contrastive learning)
 """
 
-from typing import Any, Callable, Dict, List, Optional, Sequence, cast
+from typing import Any, Callable, Dict, Generic, List, Optional, Sequence, cast
 
 from llama_index.core.async_utils import DEFAULT_NUM_WORKERS, run_jobs
 from llama_index.core.bridge.pydantic import (
@@ -33,7 +33,7 @@
 from llama_index.core.prompts import PromptTemplate
 from llama_index.core.schema import BaseNode, TextNode
 from llama_index.core.settings import Settings
-from llama_index.core.types import BasePydanticProgram
+from llama_index.core.types import BasePydanticProgram, Model
 
 DEFAULT_TITLE_NODE_TEMPLATE = """\
 Context: {context_str}. Give a title that summarizes all of \
@@ -462,15 +462,15 @@ async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
 """
 
 
-class PydanticProgramExtractor(BaseExtractor):
+class PydanticProgramExtractor(BaseExtractor, Generic[Model]):
     """Pydantic program extractor.
 
     Uses an LLM to extract out a Pydantic object. Return attributes of that object
     in a dictionary.
 
     """
 
-    program: SerializeAsAny[BasePydanticProgram] = Field(
+    program: SerializeAsAny[BasePydanticProgram[Model]] = Field(
         ..., description="Pydantic program to extract."
     )
     input_key: str = Field(
@@ -500,7 +500,9 @@ async def _acall_program(self, node: BaseNode) -> Dict[str, Any]:
         )
 
         ret_object = await self.program.acall(**{self.input_key: extract_str})
-        return ret_object.dict()
+        assert not isinstance(ret_object, list)
+
+        return ret_object.model_dump()
 
     async def aextract(self, nodes: Sequence[BaseNode]) -> List[Dict]:
         """Extract pydantic program."""

llama-index-core/llama_index/core/indices/common_tree/base.py

@@ -219,8 +219,7 @@ async def abuild_index_from_nodes(
                 self._llm.apredict(self.summary_prompt, context_str=text_chunk)
                 for text_chunk in text_chunks_progress
             ]
-            outputs: List[Tuple[str, str]] = await asyncio.gather(*tasks)
-            summaries = [output[0] for output in outputs]
+            summaries = await asyncio.gather(*tasks)
 
             event.on_end(payload={"summaries": summaries, "level": level})
 

llama-index-core/llama_index/core/instrumentation/dispatcher.py

@@ -2,7 +2,7 @@
 from functools import partial
 from contextlib import contextmanager
 from contextvars import Context, ContextVar, Token, copy_context
-from typing import Any, Callable, Generator, List, Optional, Dict, Protocol
+from typing import Any, Callable, Generator, List, Optional, Dict, Protocol, TypeVar
 import inspect
 import logging
 import uuid
@@ -26,6 +26,7 @@
 active_instrument_tags: ContextVar[Dict[str, Any]] = ContextVar(
     "instrument_tags", default={}
 )
+_R = TypeVar("_R")
 
 
 @contextmanager
@@ -239,7 +240,7 @@ def span_exit(
             else:
                 c = c.parent
 
-    def span(self, func: Callable) -> Any:
+    def span(self, func: Callable[..., _R]) -> Callable[..., _R]:
         # The `span` decorator should be idempotent.
         try:
             if hasattr(func, DISPATCHER_SPAN_DECORATED_ATTR):

llama-index-core/llama_index/core/llms/llm.py

@@ -73,6 +73,7 @@
 
 if TYPE_CHECKING:
     from llama_index.core.chat_engine.types import AgentChatResponse
+    from llama_index.core.program.utils import FlexibleModel
     from llama_index.core.tools.types import BaseTool
     from llama_index.core.llms.structured_llm import StructuredLLM
 
@@ -322,11 +323,11 @@ def _as_query_component(self, **kwargs: Any) -> QueryComponent:
     @dispatcher.span
     def structured_predict(
         self,
-        output_cls: Type[BaseModel],
+        output_cls: Type[Model],
         prompt: PromptTemplate,
         llm_kwargs: Optional[Dict[str, Any]] = None,
         **prompt_args: Any,
-    ) -> BaseModel:
+    ) -> Model:
         r"""Structured predict.
 
         Args:
@@ -372,17 +373,19 @@ class Test(BaseModel):
         )
 
         result = program(llm_kwargs=llm_kwargs, **prompt_args)
+        assert not isinstance(result, list)
+
         dispatcher.event(LLMStructuredPredictEndEvent(output=result))
         return result
 
     @dispatcher.span
     async def astructured_predict(
         self,
-        output_cls: Type[BaseModel],
+        output_cls: Type[Model],
         prompt: PromptTemplate,
         llm_kwargs: Optional[Dict[str, Any]] = None,
         **prompt_args: Any,
-    ) -> BaseModel:
+    ) -> Model:
         r"""Async Structured predict.
 
         Args:
@@ -429,17 +432,19 @@ class Test(BaseModel):
         )
 
         result = await program.acall(llm_kwargs=llm_kwargs, **prompt_args)
+        assert not isinstance(result, list)
+
         dispatcher.event(LLMStructuredPredictEndEvent(output=result))
         return result
 
     @dispatcher.span
     def stream_structured_predict(
         self,
-        output_cls: Type[BaseModel],
+        output_cls: Type[Model],
         prompt: PromptTemplate,
         llm_kwargs: Optional[Dict[str, Any]] = None,
         **prompt_args: Any,
-    ) -> Generator[Union[Model, List[Model]], None, None]:
+    ) -> Generator[Union[Model, "FlexibleModel"], None, None]:
         r"""Stream Structured predict.
 
         Args:
@@ -489,18 +494,19 @@ class Test(BaseModel):
         result = program.stream_call(llm_kwargs=llm_kwargs, **prompt_args)
         for r in result:
             dispatcher.event(LLMStructuredPredictInProgressEvent(output=r))
+            assert not isinstance(r, list)
             yield r
 
         dispatcher.event(LLMStructuredPredictEndEvent(output=r))
 
     @dispatcher.span
     async def astream_structured_predict(
         self,
-        output_cls: Type[BaseModel],
+        output_cls: Type[Model],
         prompt: PromptTemplate,
         llm_kwargs: Optional[Dict[str, Any]] = None,
         **prompt_args: Any,
-    ) -> AsyncGenerator[Union[Model, List[Model]], None]:
+    ) -> AsyncGenerator[Union[Model, "FlexibleModel"], None]:
         r"""Async Stream Structured predict.
 
         Args:
@@ -534,8 +540,10 @@ class Test(BaseModel):
             ```
         """
 
-        async def gen() -> AsyncGenerator[Union[Model, List[Model]], None]:
-            from llama_index.core.program.utils import get_program_for_llm
+        async def gen() -> AsyncGenerator[Union[Model, "FlexibleModel"], None]:
+            from llama_index.core.program.utils import (
+                get_program_for_llm,
+            )
 
             dispatcher.event(
                 LLMStructuredPredictStartEvent(
@@ -552,6 +560,7 @@ async def gen() -> AsyncGenerator[Union[Model, List[Model]], None]:
             result = await program.astream_call(llm_kwargs=llm_kwargs, **prompt_args)
             async for r in result:
                 dispatcher.event(LLMStructuredPredictInProgressEvent(output=r))
+                assert not isinstance(r, list)
                 yield r
 
             dispatcher.event(LLMStructuredPredictEndEvent(output=r))

llama-index-core/llama_index/core/output_parsers/pydantic.py

@@ -1,7 +1,7 @@
 """Pydantic output parser."""
 
 import json
-from typing import Any, List, Optional, Type
+from typing import Any, Generic, List, Optional, Type
 
 from llama_index.core.output_parsers.base import ChainableOutputParser
 from llama_index.core.output_parsers.utils import extract_json_str
@@ -15,7 +15,7 @@
 """
 
 
-class PydanticOutputParser(ChainableOutputParser):
+class PydanticOutputParser(ChainableOutputParser, Generic[Model]):
     """Pydantic Output Parser.
 
     Args:
@@ -36,7 +36,7 @@ def __init__(
 
     @property
     def output_cls(self) -> Type[Model]:
-        return self._output_cls  # type: ignore
+        return self._output_cls
 
     @property
     def format_string(self) -> str: