Add Support for Flexible Input Format for LLM and Chat Model Runs (#4805)

Previously, the client expected a strict 'prompt' or 'messages' format
and wouldn't permit running a chat model or llm on prompts or messages
(respectively).

Since many datasets may want to specify custom key: string , relax this
requirement.
Also, add support for running a chat model on raw prompts and LLM on
chat messages through their respective fallbacks.

langchain/client/langchain.py

@@ -39,7 +39,14 @@ from langchain.client.models import (
     ListRunsQueryParams,
 )
 from langchain.llms.base import BaseLLM
-from langchain.schema import ChatResult, LLMResult, messages_from_dict
+from langchain.schema import (
+    BaseMessage,
+    ChatResult,
+    HumanMessage,
+    LLMResult,
+    get_buffer_string,
+    messages_from_dict,
+)
 from langchain.utils import raise_for_status_with_text, xor_args
 
 if TYPE_CHECKING:
@@ -50,6 +57,10 @@ logger = logging.getLogger(__name__)
 MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel]
 
 
+class InputFormatError(Exception):
+    """Raised when input format is invalid."""
+
+
 def _get_link_stem(url: str) -> str:
     scheme = urlsplit(url).scheme
     netloc_prefix = urlsplit(url).netloc.split(":")[0]
@@ -389,6 +400,76 @@ class LangChainPlusClient(BaseSettings):
         raise_for_status_with_text(response)
         return [Example(**dataset) for dataset in response.json()]
 
+    @staticmethod
+    def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
+        """Get prompts from inputs."""
+        if not inputs:
+            raise InputFormatError("Inputs should not be empty.")
+
+        prompts = []
+
+        if "prompt" in inputs:
+            if not isinstance(inputs["prompt"], str):
+                raise InputFormatError(
+                    "Expected string for 'prompt', got"
+                    f" {type(inputs['prompt']).__name__}"
+                )
+            prompts = [inputs["prompt"]]
+        elif "prompts" in inputs:
+            if not isinstance(inputs["prompts"], list) or not all(
+                isinstance(i, str) for i in inputs["prompts"]
+            ):
+                raise InputFormatError(
+                    "Expected list of strings for 'prompts',"
+                    f" got {type(inputs['prompts']).__name__}"
+                )
+            prompts = inputs["prompts"]
+        elif len(inputs) == 1:
+            prompt_ = next(iter(inputs.values()))
+            if isinstance(prompt_, str):
+                prompts = [prompt_]
+            elif isinstance(prompt_, list) and all(isinstance(i, str) for i in prompt_):
+                prompts = prompt_
+            else:
+                raise InputFormatError(
+                    f"LLM Run expects string prompt input. Got {inputs}"
+                )
+        else:
+            raise InputFormatError(
+                f"LLM Run expects 'prompt' or 'prompts' in inputs. Got {inputs}"
+            )
+
+        return prompts
+
+    @staticmethod
+    def _get_messages(inputs: Dict[str, Any]) -> List[List[BaseMessage]]:
+        """Get Chat Messages from inputs."""
+        if not inputs:
+            raise InputFormatError("Inputs should not be empty.")
+
+        if "messages" in inputs:
+            single_input = inputs["messages"]
+        elif len(inputs) == 1:
+            single_input = next(iter(inputs.values()))
+        else:
+            raise InputFormatError(
+                f"Chat Run expects 'messages' in inputs. Got {inputs}"
+            )
+        if isinstance(single_input, list) and all(
+            isinstance(i, dict) for i in single_input
+        ):
+            raw_messages = [single_input]
+        elif isinstance(single_input, list) and all(
+            isinstance(i, list) for i in single_input
+        ):
+            raw_messages = single_input
+        else:
+            raise InputFormatError(
+                f"Chat Run expects List[dict] or List[List[dict]] 'messages'"
+                f" input. Got {inputs}"
+            )
+        return [messages_from_dict(batch) for batch in raw_messages]
+
     @staticmethod
     async def _arun_llm(
         llm: BaseLanguageModel,
@@ -396,16 +477,31 @@ class LangChainPlusClient(BaseSettings):
         langchain_tracer: LangChainTracer,
     ) -> Union[LLMResult, ChatResult]:
         if isinstance(llm, BaseLLM):
-            if "prompt" not in inputs:
-                raise ValueError(f"LLM Run requires 'prompt' input. Got {inputs}")
-            llm_prompt: str = inputs["prompt"]
-            llm_output = await llm.agenerate([llm_prompt], callbacks=[langchain_tracer])
+            try:
+                llm_prompts = LangChainPlusClient._get_prompts(inputs)
+                llm_output = await llm.agenerate(
+                    llm_prompts, callbacks=[langchain_tracer]
+                )
+            except InputFormatError:
+                llm_messages = LangChainPlusClient._get_messages(inputs)
+                buffer_strings = [
+                    get_buffer_string(messages) for messages in llm_messages
+                ]
+                llm_output = await llm.agenerate(
+                    buffer_strings, callbacks=[langchain_tracer]
+                )
         elif isinstance(llm, BaseChatModel):
-            if "messages" not in inputs:
-                raise ValueError(f"Chat Run requires 'messages' input. Got {inputs}")
-            raw_messages: List[dict] = inputs["messages"]
-            messages = messages_from_dict(raw_messages)
-            llm_output = await llm.agenerate([messages], callbacks=[langchain_tracer])
+            try:
+                messages = LangChainPlusClient._get_messages(inputs)
+                llm_output = await llm.agenerate(messages, callbacks=[langchain_tracer])
+            except InputFormatError:
+                prompts = LangChainPlusClient._get_prompts(inputs)
+                converted_messages: List[List[BaseMessage]] = [
+                    [HumanMessage(content=prompt)] for prompt in prompts
+                ]
+                llm_output = await llm.agenerate(
+                    converted_messages, callbacks=[langchain_tracer]
+                )
         else:
             raise ValueError(f"Unsupported LLM type {type(llm)}")
         return llm_output
@@ -562,18 +658,27 @@ class LangChainPlusClient(BaseSettings):
     ) -> Union[LLMResult, ChatResult]:
         """Run the language model on the example."""
         if isinstance(llm, BaseLLM):
-            if "prompt" not in inputs:
-                raise ValueError(f"LLM Run must contain 'prompt' key. Got {inputs}")
-            llm_prompt: str = inputs["prompt"]
-            llm_output = llm.generate([llm_prompt], callbacks=[langchain_tracer])
+            try:
+                llm_prompts = LangChainPlusClient._get_prompts(inputs)
+                llm_output = llm.generate(llm_prompts, callbacks=[langchain_tracer])
+            except InputFormatError:
+                llm_messages = LangChainPlusClient._get_messages(inputs)
+                buffer_strings = [
+                    get_buffer_string(messages) for messages in llm_messages
+                ]
+                llm_output = llm.generate(buffer_strings, callbacks=[langchain_tracer])
         elif isinstance(llm, BaseChatModel):
-            if "messages" not in inputs:
-                raise ValueError(
-                    f"Chat Model Run must contain 'messages' key. Got {inputs}"
+            try:
+                messages = LangChainPlusClient._get_messages(inputs)
+                llm_output = llm.generate(messages, callbacks=[langchain_tracer])
+            except InputFormatError:
+                prompts = LangChainPlusClient._get_prompts(inputs)
+                converted_messages: List[List[BaseMessage]] = [
+                    [HumanMessage(content=prompt)] for prompt in prompts
+                ]
+                llm_output = llm.generate(
+                    converted_messages, callbacks=[langchain_tracer]
                 )
-            raw_messages: List[dict] = inputs["messages"]
-            messages = messages_from_dict(raw_messages)
-            llm_output = llm.generate([messages], callbacks=[langchain_tracer])
         else:
             raise ValueError(f"Unsupported LLM type {type(llm)}")
         return llm_output

tests/unit_tests/client/test_langchain.py

@@ -12,11 +12,14 @@ from langchain.callbacks.tracers.langchain import LangChainTracer
 from langchain.callbacks.tracers.schemas import TracerSession
 from langchain.chains.base import Chain
 from langchain.client.langchain import (
+    InputFormatError,
     LangChainPlusClient,
     _get_link_stem,
     _is_localhost,
 )
 from langchain.client.models import Dataset, Example
+from tests.unit_tests.llms.fake_chat_model import FakeChatModel
+from tests.unit_tests.llms.fake_llm import FakeLLM
 
 _CREATED_AT = datetime(2015, 1, 1, 0, 0, 0)
 _TENANT_ID = "7a3d2b56-cd5b-44e5-846f-7eb6e8144ce4"
@@ -230,3 +233,85 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
             for uuid_ in uuids
         }
         assert results == expected
+
+
+_EXAMPLE_MESSAGE = {
+    "data": {"content": "Foo", "example": False, "additional_kwargs": {}},
+    "type": "human",
+}
+_VALID_MESSAGES = [
+    {"messages": [_EXAMPLE_MESSAGE], "other_key": "value"},
+    {"messages": [], "other_key": "value"},
+    {
+        "messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], [_EXAMPLE_MESSAGE]],
+        "other_key": "value",
+    },
+    {"any_key": [_EXAMPLE_MESSAGE]},
+    {"any_key": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], [_EXAMPLE_MESSAGE]]},
+]
+_VALID_PROMPTS = [
+    {"prompts": ["foo", "bar", "baz"], "other_key": "value"},
+    {"prompt": "foo", "other_key": ["bar", "baz"]},
+    {"some_key": "foo"},
+    {"some_key": ["foo", "bar"]},
+]
+
+
+@pytest.mark.parametrize(
+    "inputs",
+    _VALID_MESSAGES,
+)
+def test__get_messages_valid(inputs: Dict[str, Any]) -> None:
+    {"messages": []}
+    LangChainPlusClient._get_messages(inputs)
+
+
+@pytest.mark.parametrize(
+    "inputs",
+    _VALID_PROMPTS,
+)
+def test__get_prompts_valid(inputs: Dict[str, Any]) -> None:
+    LangChainPlusClient._get_prompts(inputs)
+
+
+@pytest.mark.parametrize(
+    "inputs",
+    [
+        {"prompts": "foo"},
+        {"prompt": ["foo"]},
+        {"some_key": 3},
+        {"some_key": "foo", "other_key": "bar"},
+    ],
+)
+def test__get_prompts_invalid(inputs: Dict[str, Any]) -> None:
+    with pytest.raises(InputFormatError):
+        LangChainPlusClient._get_prompts(inputs)
+
+
+@pytest.mark.parametrize(
+    "inputs",
+    [
+        {"one_key": [_EXAMPLE_MESSAGE], "other_key": "value"},
+        {
+            "messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], _EXAMPLE_MESSAGE],
+            "other_key": "value",
+        },
+        {"prompts": "foo"},
+        {},
+    ],
+)
+def test__get_messages_invalid(inputs: Dict[str, Any]) -> None:
+    with pytest.raises(InputFormatError):
+        LangChainPlusClient._get_messages(inputs)
+
+
+@pytest.mark.parametrize("inputs", _VALID_PROMPTS + _VALID_MESSAGES)
+def test_run_llm_all_formats(inputs: Dict[str, Any]) -> None:
+    llm = FakeLLM()
+    LangChainPlusClient.run_llm(llm, inputs, mock.MagicMock())
+
+
+@pytest.mark.parametrize("inputs", _VALID_MESSAGES + _VALID_PROMPTS)
+def test_run_chat_model_all_formats(inputs: Dict[str, Any]) -> None:
+    llm = FakeChatModel()
+    LangChainPlusClient.run_llm(llm, inputs, mock.MagicMock())