Allow custom type to be streamed and use native response

dspy/adapters/base.py

@@ -4,7 +4,7 @@
 import json_repair
 import litellm
 
-from dspy.adapters.types import History
+from dspy.adapters.types import History, Type
 from dspy.adapters.types.base_type import split_message_content_for_custom_types
 from dspy.adapters.types.tool import Tool, ToolCalls
 from dspy.experimental import Citations
@@ -16,11 +16,13 @@
 if TYPE_CHECKING:
     from dspy.clients.lm import LM
 
+_DEFAULT_NATIVE_RESPONSE_TYPES = [Citations]
 
 class Adapter:
-    def __init__(self, callbacks: list[BaseCallback] | None = None, use_native_function_calling: bool = False):
+    def __init__(self, callbacks: list[BaseCallback] | None = None, use_native_function_calling: bool = False, native_response_types: list[type[Type]] | None = None):
         self.callbacks = callbacks or []
         self.use_native_function_calling = use_native_function_calling
+        self.native_response_types = native_response_types or _DEFAULT_NATIVE_RESPONSE_TYPES
 
     def __init_subclass__(cls, **kwargs) -> None:
         super().__init_subclass__(**kwargs)
@@ -64,9 +66,10 @@ def _call_preprocess(
 
                 return signature_for_native_function_calling
 
-        citation_output_field_name = self._get_citation_output_field_name(signature)
-        if citation_output_field_name:
-            signature = signature.delete(citation_output_field_name)
+        # Handle custom types that use native response
+        for name, field in signature.output_fields.items():
+            if isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types:
+                signature = signature.delete(name)
 
         return signature
 
@@ -75,23 +78,21 @@ def _call_postprocess(
         processed_signature: type[Signature],
         original_signature: type[Signature],
         outputs: list[dict[str, Any]],
+        lm: "LM",
     ) -> list[dict[str, Any]]:
         values = []
 
         tool_call_output_field_name = self._get_tool_call_output_field_name(original_signature)
-        citation_output_field_name = self._get_citation_output_field_name(original_signature)
 
         for output in outputs:
             output_logprobs = None
             tool_calls = None
-            citations = None
             text = output
 
             if isinstance(output, dict):
                 text = output["text"]
                 output_logprobs = output.get("logprobs")
                 tool_calls = output.get("tool_calls")
-                citations = output.get("citations")
 
             if text:
                 value = self.parse(processed_signature, text)
@@ -114,9 +115,10 @@ def _call_postprocess(
                 ]
                 value[tool_call_output_field_name] = ToolCalls.from_dict_list(tool_calls)
 
-            if citations and citation_output_field_name:
-                citations_obj = Citations.from_dict_list(citations)
-                value[citation_output_field_name] = citations_obj
+            # Parse custom types that does not rely on the adapter parsing
+            for name, field in original_signature.output_fields.items():
+                if isinstance(field.annotation, type) and issubclass(field.annotation, Type) and field.annotation in self.native_response_types:
+                    value[name] = field.annotation.parse_lm_response(output)
 
             if output_logprobs:
                 value["logprobs"] = output_logprobs
@@ -137,7 +139,7 @@ def __call__(
         inputs = self.format(processed_signature, demos, inputs)
 
         outputs = lm(messages=inputs, **lm_kwargs)
-        return self._call_postprocess(processed_signature, signature, outputs)
+        return self._call_postprocess(processed_signature, signature, outputs, lm)
 
     async def acall(
         self,
@@ -151,7 +153,7 @@ async def acall(
         inputs = self.format(processed_signature, demos, inputs)
 
         outputs = await lm.acall(messages=inputs, **lm_kwargs)
-        return self._call_postprocess(processed_signature, signature, outputs)
+        return self._call_postprocess(processed_signature, signature, outputs, lm)
 
     def format(
         self,
@@ -402,12 +404,6 @@ def _get_tool_call_output_field_name(self, signature: type[Signature]) -> bool:
                 return name
         return None
 
-    def _get_citation_output_field_name(self, signature: type[Signature]) -> str | None:
-        """Find the Citations output field in the signature."""
-        for name, field in signature.output_fields.items():
-            if field.annotation == Citations:
-                return name
-        return None
 
     def format_conversation_history(
         self,

dspy/adapters/types/base_type.py

@@ -1,9 +1,10 @@
 import json
 import re
-from typing import Any, get_args, get_origin
+from typing import Any, Optional, get_args, get_origin
 
 import json_repair
 import pydantic
+from litellm import ModelResponseStream
 
 CUSTOM_TYPE_START_IDENTIFIER = "<<CUSTOM-TYPE-START-IDENTIFIER>>"
 CUSTOM_TYPE_END_IDENTIFIER = "<<CUSTOM-TYPE-END-IDENTIFIER>>"
@@ -67,6 +68,36 @@ def serialize_model(self):
             return f"{CUSTOM_TYPE_START_IDENTIFIER}{formatted}{CUSTOM_TYPE_END_IDENTIFIER}"
         return formatted
 
+    @classmethod
+    def is_streamable(cls) -> bool:
+        """Whether the custom type is streamable."""
+        return False
+
+    @classmethod
+    def parse_stream_chunk(cls, chunk: ModelResponseStream) -> Optional["Type"]:
+        """
+        Parse a stream chunk into the custom type.
+
+        Args:
+            chunk: A stream chunk.
+
+        Returns:
+            A custom type object or None if the chunk is not for this custom type.
+        """
+        return None
+
+
+    @classmethod
+    def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Type"]:
+        """Parse a LM response into the custom type.
+
+        Args:
+            response: A LM response.
+
+        Returns:
+            A custom type object.
+        """
+        return None
 
 def split_message_content_for_custom_types(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
     """Split user message content into a list of content blocks.

dspy/adapters/types/citation.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, Optional
 
 import pydantic
 
@@ -166,3 +166,51 @@ def __len__(self):
     def __getitem__(self, index):
         """Allow indexing into citations."""
         return self.citations[index]
+
+    @classmethod
+    def is_streamable(cls) -> bool:
+        """Whether the Citations type is streamable."""
+        return True
+
+    @classmethod
+    def parse_stream_chunk(cls, chunk) -> Optional["Citations"]:
+        """
+        Parse a stream chunk into Citations.
+
+        Args:
+            chunk: A stream chunk from the LM.
+
+        Returns:
+            A Citations object if the chunk contains citation data, None otherwise.
+        """
+        try:
+            # Check if the chunk has citation data in provider_specific_fields
+            if hasattr(chunk, "choices") and chunk.choices:
+                delta = chunk.choices[0].delta
+                if hasattr(delta, "provider_specific_fields") and delta.provider_specific_fields:
+                    citation_data = delta.provider_specific_fields.get("citation")
+                    if citation_data:
+                        return cls.from_dict_list([citation_data])
+        except Exception:
+            pass
+        return None
+
+
+    @classmethod
+    def parse_lm_response(cls, response: str | dict[str, Any]) -> Optional["Citations"]:
+        """Parse a LM response into Citations.
+
+        Args:
+            response: A LM response that may contain citation data.
+
+        Returns:
+            A Citations object if citation data is found, None otherwise.
+        """
+        if isinstance(response, dict):
+            # Check if the response contains citations in the expected format
+            if "citations" in response:
+                citations_data = response["citations"]
+                if isinstance(citations_data, list):
+                    return cls.from_dict_list(citations_data)
+
+        return None

dspy/streaming/streaming_listener.py

@@ -7,7 +7,7 @@
 
 from dspy.adapters.chat_adapter import ChatAdapter
 from dspy.adapters.json_adapter import JSONAdapter
-from dspy.adapters.types.citation import Citations
+from dspy.adapters.types import Type
 from dspy.adapters.xml_adapter import XMLAdapter
 from dspy.dsp.utils.settings import settings
 from dspy.streaming.messages import StreamResponse
@@ -102,18 +102,15 @@ def receive(self, chunk: ModelResponseStream):
         except Exception:
             return
 
-        # Handle anthropic citations. see https://docs.litellm.ai/docs/providers/anthropic#beta-citations-api
-        try:
-            if self._signature_field_is_citation_type():
-                if chunk_citation := chunk.choices[0].delta.provider_specific_fields.get("citation", None):
-                    return StreamResponse(
-                        self.predict_name,
-                        self.signature_field_name,
-                        Citations.from_dict_list([chunk_citation]),
-                        is_last_chunk=False,
-                    )
-        except Exception:
-            pass
+        # Handle custom streamable types
+        if self._output_type and issubclass(self._output_type, Type) and self._output_type.is_streamable():
+            if parsed_chunk := self._output_type.parse_stream_chunk(chunk):
+                return StreamResponse(
+                    self.predict_name,
+                    self.signature_field_name,
+                    parsed_chunk,
+                    is_last_chunk=self.stream_end,
+                )
 
         if chunk_message and start_identifier in chunk_message:
             # If the cache is hit, the chunk_message could be the full response. When it happens we can
@@ -217,10 +214,13 @@ def flush(self) -> str:
                 f"{', '.join([a.__name__ for a in ADAPTER_SUPPORT_STREAMING])}"
             )
 
-    def _signature_field_is_citation_type(self) -> bool:
-        """Check if the signature field is a citations field."""
-        from dspy.predict import Predict
-        return isinstance(self.predict, Predict) and getattr(self.predict.signature.output_fields.get(self.signature_field_name, None), "annotation", None) == Citations
+    @property
+    def _output_type(self) -> type | None:
+        try:
+            return self.predict.signature.output_fields[self.signature_field_name].annotation
+        except Exception:
+            return None
+
 
 
 def find_predictor_for_stream_listeners(program: "Module", stream_listeners: list[StreamListener]):
@@ -249,10 +249,10 @@ def find_predictor_for_stream_listeners(program: "Module", stream_listeners: lis
                     "predictor to use for streaming. Please specify the predictor to listen to."
                 )
 
-            if field_info.annotation not in [str, Citations]:
+            if not _is_streamable(field_info.annotation):
                 raise ValueError(
-                    f"Stream listener can only be applied to string or Citations output field, but your field {field_name} is of "
-                    f"type {field_info.annotation}."
+                    f"Stream listener can only be applied to string or subclass of `dspy.Type` that has `is_streamable() == True`, "
+                    f"but your field {field_name} is of type {field_info.annotation}."
                 )
 
             field_name_to_named_predictor[field_name] = (name, predictor)
@@ -271,3 +271,12 @@ def find_predictor_for_stream_listeners(program: "Module", stream_listeners: lis
         listener.predict_name, listener.predict = field_name_to_named_predictor[listener.signature_field_name]
         predict_id_to_listener[id(listener.predict)].append(listener)
     return predict_id_to_listener
+
+def _is_streamable(field_type: type | None) -> bool:
+    if field_type is None:
+        return False
+    if field_type is str:
+        return True
+    if issubclass(field_type, Type):
+        return field_type.is_streamable()
+    return False

tests/adapters/test_citation.py

@@ -135,7 +135,7 @@ class CitationSignature(Signature):
         answer: str = dspy.OutputField()
         citations: Citations = dspy.OutputField()
 
-    adapter = ChatAdapter()
+    adapter = ChatAdapter(native_response_types=[Citations])
 
     outputs = [{
         "text": "[[ ## answer ## ]]\nThe answer is blue.\n\n[[ ## citations ## ]]\n[]",
@@ -154,7 +154,8 @@ class CitationSignature(Signature):
     result = adapter._call_postprocess(
         CitationSignature.delete("citations"),
         CitationSignature,
-        outputs
+        outputs,
+        dspy.LM(model="claude-3-5-sonnet-20241022")
     )
 
     assert len(result) == 1

tests/streaming/test_streaming.py

@@ -9,6 +9,7 @@
 from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
 import dspy
+from dspy.adapters.types import Type
 from dspy.experimental import Citations, Document
 from dspy.streaming import StatusMessage, StatusMessageProvider, streaming_response
 
@@ -877,6 +878,58 @@ async def send_to_stream():
     assert isinstance(all_chunks[1], dspy.Prediction)
 
 
+@pytest.mark.anyio
+async def test_streaming_allows_custom_streamable_type():
+    class CustomType(Type):
+        message: str
+
+        @classmethod
+        def is_streamable(cls) -> bool:
+            return True
+
+        @classmethod
+        def parse_stream_chunk(cls, chunk):
+            return CustomType(message=chunk.choices[0].delta.content)
+
+        @classmethod
+        def parse_lm_response(cls, response: dict) -> "CustomType":
+            return CustomType(message=response.split("\n\n")[0])
+
+    class CustomSignature(dspy.Signature):
+        question: str = dspy.InputField()
+        answer: CustomType = dspy.OutputField()
+
+    program = dspy.streamify(
+        dspy.Predict(CustomSignature),
+        stream_listeners=[
+            dspy.streaming.StreamListener(signature_field_name="answer"),
+        ],
+    )
+
+    async def stream(*args, **kwargs):
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="Hello"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="World"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="\n\n"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content="[[ ##"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content=" completed"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content=" ##"))])
+        yield ModelResponseStream(model="gpt-4o-mini", choices=[StreamingChoices(delta=Delta(content=" ]]"))])
+
+
+    with mock.patch("litellm.acompletion", side_effect=stream):
+        with dspy.context(lm=dspy.LM("openai/gpt-4o-mini", cache=False), adapter=dspy.ChatAdapter(native_response_types=[CustomType])):
+            output = program(question="why did a chicken cross the kitchen?")
+            all_chunks = []
+            async for value in output:
+                if isinstance(value, dspy.streaming.StreamResponse):
+                    all_chunks.append(value)
+                elif isinstance(value, dspy.Prediction):
+                    assert isinstance(value.answer, CustomType)
+                    assert value.answer.message == "HelloWorld"
+
+    assert all(isinstance(chunk.chunk, CustomType) for chunk in all_chunks)
+
+
 @pytest.mark.anyio
 async def test_streaming_with_citations():
     class AnswerWithSources(dspy.Signature):
@@ -936,7 +989,7 @@ async def citation_stream(*args, **kwargs):
         # Create test documents
         docs = [Document(data="Water boils at 100°C at standard pressure.", title="Physics Facts")]
 
-        with dspy.context(lm=dspy.LM("anthropic/claude-3-5-sonnet-20241022", cache=False)):
+        with dspy.context(lm=dspy.LM("anthropic/claude-3-5-sonnet-20241022", cache=False), adapter=dspy.ChatAdapter(native_response_types=[Citations])):
             output = program(documents=docs, question="What temperature does water boil?")
             citation_chunks = []
             final_prediction = None