feat: Add _agenerate implementation

libs/ibm/langchain_ibm/llms.py

@@ -6,7 +6,10 @@
 from ibm_watsonx_ai import APIClient, Credentials  # type: ignore
 from ibm_watsonx_ai.foundation_models import Model, ModelInference  # type: ignore
 from ibm_watsonx_ai.metanames import GenTextParamsMetaNames  # type: ignore
-from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
 from langchain_core.language_models.llms import BaseLLM
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
 from langchain_core.utils.utils import secret_from_env
@@ -349,9 +352,9 @@ def _stream_response_to_generation_chunk(
         return GenerationChunk(
             text=stream_response["results"][0]["generated_text"],
             generation_info=dict(
-                finish_reason=None
-                if finish_reason == "not_finished"
-                else finish_reason,
+                finish_reason=(
+                    None if finish_reason == "not_finished" else finish_reason
+                ),
                 llm_output={
                     "model_id": self.model_id,
                     "deployment_id": self.deployment_id,
@@ -383,6 +386,20 @@ def _call(
         )
         return result.generations[0][0].text
 
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Async version of the _call method."""
+
+        result = await self._agenerate(
+            prompts=[prompt], stop=stop, run_manager=run_manager, **kwargs
+        )
+        return result.generations[0][0].text
+
     def _generate(
         self,
         prompts: List[str],
@@ -431,6 +448,31 @@ def _generate(
             )
             return self._create_llm_result(response)
 
+    async def _agenerate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        stream: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Async run the LLM on the given prompt and input."""
+        params, kwargs = self._get_chat_params(stop=stop, **kwargs)
+        params = self._validate_chat_params(params)
+        if stream:
+            return await super()._agenerate(
+                prompts=prompts, stop=stop, run_manager=run_manager, **kwargs
+            )
+        else:
+            responses = [
+                await self.watsonx_model.agenerate(
+                    prompt=prompt, params=params, **kwargs
+                )
+                for prompt in prompts
+            ]
+
+            return self._create_llm_result(responses)
+
     def _stream(
         self,
         prompt: str,

libs/ibm/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-ibm"
-version = "0.2.0"
+version = "0.2.1"
 description = "An integration package connecting IBM watsonx.ai and LangChain"
 authors = ["IBM"]
 readme = "README.md"
@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 langchain-core = ">=0.3.0,<0.4"
-ibm-watsonx-ai = "^1.0.8"
+ibm-watsonx-ai = "^1.1.9"
 
 [tool.poetry.group.test]
 optional = true

libs/ibm/tests/integration_tests/test_llms.py

@@ -422,6 +422,16 @@ async def test_watsonx_ainvoke() -> None:
     assert isinstance(response, str)
 
 
+async def test_watsonx_acall() -> None:
+    watsonxllm = WatsonxLLM(
+        model_id=MODEL_ID,
+        url="https://us-south.ml.cloud.ibm.com",  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    response = await watsonxllm._acall("what is the color of the grass?")
+    assert "green" in response.lower()
+
+
 async def test_watsonx_agenerate() -> None:
     watsonxllm = WatsonxLLM(
         model_id=MODEL_ID,
@@ -435,6 +445,16 @@ async def test_watsonx_agenerate() -> None:
     assert response.llm_output["token_usage"]["generated_token_count"] != 0  # type: ignore
 
 
+async def test_watsonx_agenerate_with_stream() -> None:
+    watsonxllm = WatsonxLLM(
+        model_id=MODEL_ID,
+        url="https://us-south.ml.cloud.ibm.com",  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    response = await watsonxllm.agenerate(["What color sunflower is?"], stream=True)
+    assert "yellow" in response.generations[0][0].text.lower()
+
+
 def test_get_num_tokens() -> None:
     watsonxllm = WatsonxLLM(
         model_id=MODEL_ID,