Adding compatibility for OllamaFunctions with ImagePromptTemplate (#24499)

- [ ] **PR title**: "experimental: Adding compatibility for
OllamaFunctions with ImagePromptTemplate"

- [ ] **PR message**: 
- **Description:** Removes the outdated
`_convert_messages_to_ollama_messages` method override in the
`OllamaFunctions` class to ensure that ollama multimodal models can be
invoked with an image.
    - **Issue:** #24174

---------

Co-authored-by: Joel Akeret <joel.akeret@ti&m.com>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>

libs/experimental/langchain_experimental/llms/ollama_functions.py

@@ -12,7 +12,6 @@ from typing import (
     TypedDict,
     TypeVar,
     Union,
-    cast,
 )
 
 from langchain_community.chat_models.ollama import ChatOllama
@@ -24,10 +23,7 @@ from langchain_core.language_models import LanguageModelInput
 from langchain_core.messages import (
     AIMessage,
     BaseMessage,
-    HumanMessage,
-    SystemMessage,
     ToolCall,
-    ToolMessage,
 )
 from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.json import JsonOutputParser
@@ -282,59 +278,6 @@ class OllamaFunctions(ChatOllama):
         else:
             return llm | parser_chain
 
-    def _convert_messages_to_ollama_messages(
-        self, messages: List[BaseMessage]
-    ) -> List[Dict[str, Union[str, List[str]]]]:
-        ollama_messages: List = []
-        for message in messages:
-            role = ""
-            if isinstance(message, HumanMessage):
-                role = "user"
-            elif isinstance(message, AIMessage) or isinstance(message, ToolMessage):
-                role = "assistant"
-            elif isinstance(message, SystemMessage):
-                role = "system"
-            else:
-                raise ValueError("Received unsupported message type for Ollama.")
-
-            content = ""
-            images = []
-            if isinstance(message.content, str):
-                content = message.content
-            else:
-                for content_part in cast(List[Dict], message.content):
-                    if content_part.get("type") == "text":
-                        content += f"\n{content_part['text']}"
-                    elif content_part.get("type") == "image_url":
-                        if isinstance(content_part.get("image_url"), str):
-                            image_url_components = content_part["image_url"].split(",")
-                            # Support data:image/jpeg;base64,<image> format
-                            # and base64 strings
-                            if len(image_url_components) > 1:
-                                images.append(image_url_components[1])
-                            else:
-                                images.append(image_url_components[0])
-                        else:
-                            raise ValueError(
-                                "Only string image_url content parts are supported."
-                            )
-                    else:
-                        raise ValueError(
-                            "Unsupported message content type. "
-                            "Must either have type 'text' or type 'image_url' "
-                            "with a string 'image_url' field."
-                        )
-
-            ollama_messages.append(
-                {
-                    "role": role,
-                    "content": content,
-                    "images": images,
-                }
-            )
-
-        return ollama_messages
-
     def _generate(
         self,
         messages: List[BaseMessage],

libs/experimental/tests/unit_tests/test_ollama_functions.py

@@ -0,0 +1,30 @@
+import json
+from typing import Any
+from unittest.mock import patch
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.pydantic_v1 import BaseModel
+
+from langchain_experimental.llms.ollama_functions import OllamaFunctions
+
+
+class Schema(BaseModel):
+    pass
+
+
+@patch.object(OllamaFunctions, "_create_stream")
+def test_convert_image_prompt(
+    _create_stream_mock: Any,
+) -> None:
+    response = {"message": {"content": '{"tool": "Schema", "tool_input": {}}'}}
+    _create_stream_mock.return_value = [json.dumps(response)]
+
+    prompt = ChatPromptTemplate.from_messages(
+        [("human", [{"image_url": "data:image/jpeg;base64,{image_url}"}])]
+    )
+
+    lmm = prompt | OllamaFunctions().with_structured_output(schema=Schema)
+
+    schema_instance = lmm.invoke(dict(image_url=""))
+
+    assert schema_instance is not None