Enhance langgraph integration to preserve metadata (explodinggradient…

…s#1878)

# Enhance langgraph integration to preserve AI metadata

## Description
This PR updates the `langgraph.py` integration to ensure that metadata
are preserved. This enhancement is crucial for multi-agent scenarios
where identifying the source AI is important for evaluation.

## Changes
- Updated `langgraph.py` to ensure AI names and metadata are preserved.

## Motivation and Context
In the current implementation, metadata such as the name assigned to an
AI is not saved. In the era of multi-agent systems, it is essential to
have information about which AI made a particular statement for accurate
evaluation. This update addresses this issue by preserving the necessary
metadata.

### Example Code and Output
```python
import json
from typing import List, Union

from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage

import ragas.messages as r
from ragas.integrations.langgraph import convert_message_with_metadata

def test_convert_message_with_metadata():
    from langchain_core.messages import HumanMessage, AIMessage

    human_message = HumanMessage(content="Hello", name="me", additional_kwargs={"key1": "value1"})
    ai_message = AIMessage(content="Hi", name="ai_1", additional_kwargs={"tool_calls": [{"function": {"name": "tool1", "arguments": '{"arg1": "val1"}'}}]})

    converted_messages = convert_message_with_metadata([human_message, ai_message])

    for msg in converted_messages:
        print(f"Content: {msg.content}, Metadata: {msg.metadata}")

if __name__ == "__main__":
    test_convert_message_with_metadata()
```

```
Output
Content: Hello, Metadata: {'additional_kwargs': {'key1': 'value1'}, 'response_metadata': {}, 'type': 'human', 'name': 'me', 'id': None, 'example': False}
Content: Hi, Metadata: {'additional_kwargs': {'tool_calls': [{'function': {'name': 'tool1', 'arguments': '{"arg1": "val1"}'}}]}, 'response_metadata': {}, 'type': 'ai', 'name': 'ai_1', 'id': None, 'example': False, 'tool_calls': [{'name': 'tool1', 'args': {'arg1': 'val1'}, 'id': None, 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}
```

src/ragas/integrations/langgraph.py

@@ -7,20 +7,22 @@
 
 
 def convert_to_ragas_messages(
-    messages: List[Union[HumanMessage, SystemMessage, AIMessage, ToolMessage]]
+    messages: List[Union[HumanMessage, SystemMessage, AIMessage, ToolMessage]], metadata: bool = False
 ) -> List[Union[r.HumanMessage, r.AIMessage, r.ToolMessage]]:
     """
-    Convert LangChain messages into Ragas messages for agent evaluation.
+    Convert LangChain messages into Ragas messages with metadata for agent evaluation.
 
     Parameters
     ----------
     messages : List[Union[HumanMessage, SystemMessage, AIMessage, ToolMessage]]
         List of LangChain message objects to be converted.
+    metadata : bool, optional (default=False)
+        Whether to include metadata in the converted messages.
 
     Returns
     -------
     List[Union[r.HumanMessage, r.AIMessage, r.ToolMessage]]
-        List of corresponding Ragas message objects.
+        List of corresponding Ragas message objects with metadata.
 
     Raises
     ------
@@ -42,14 +44,30 @@ def _validate_string_content(message, message_type: str) -> str:
             )
         return message.content
 
-    MESSAGE_TYPE_MAP = {
-        HumanMessage: lambda m: r.HumanMessage(
-            content=_validate_string_content(m, "HumanMessage")
-        ),
-        ToolMessage: lambda m: r.ToolMessage(
-            content=_validate_string_content(m, "ToolMessage")
-        ),
-    }
+    def _extract_metadata(message) -> dict:
+
+        return {k: v for k, v in message.__dict__.items() if k != "content"}
+
+    if metadata:
+        MESSAGE_TYPE_MAP = {
+            HumanMessage: lambda m: r.HumanMessage(
+                content=_validate_string_content(m, "HumanMessage"),
+                metadata=_extract_metadata(m)
+            ),
+            ToolMessage: lambda m: r.ToolMessage(
+                content=_validate_string_content(m, "ToolMessage"),
+                metadata=_extract_metadata(m)
+            ),
+        }
+    else:
+        MESSAGE_TYPE_MAP = {
+            HumanMessage: lambda m: r.HumanMessage(
+                content=_validate_string_content(m, "HumanMessage")
+            ),
+            ToolMessage: lambda m: r.ToolMessage(
+                content=_validate_string_content(m, "ToolMessage")
+            ),
+        }
 
     def _extract_tool_calls(message: AIMessage) -> List[r.ToolCall]:
         tool_calls = message.additional_kwargs.get("tool_calls", [])
@@ -61,18 +79,25 @@ def _extract_tool_calls(message: AIMessage) -> List[r.ToolCall]:
             for tool_call in tool_calls
         ]
 
-    def _convert_ai_message(message: AIMessage) -> r.AIMessage:
+    def _convert_ai_message(message: AIMessage, metadata: bool) -> r.AIMessage:
         tool_calls = _extract_tool_calls(message) if message.additional_kwargs else None
-        return r.AIMessage(
-            content=_validate_string_content(message, "AIMessage"),
-            tool_calls=tool_calls,
-        )
+        if metadata:
+            return r.AIMessage(
+                content=_validate_string_content(message, "AIMessage"),
+                tool_calls=tool_calls,
+                metadata=_extract_metadata(message)
+            )
+        else:
+            return r.AIMessage(
+                content=_validate_string_content(message, "AIMessage"),
+                tool_calls=tool_calls
+            )
 
-    def _convert_message(message):
+    def _convert_message(message, metadata: bool = False):
         if isinstance(message, SystemMessage):
             return None  # Skip SystemMessages
         if isinstance(message, AIMessage):
-            return _convert_ai_message(message)
+            return _convert_ai_message(message, metadata)
         converter = MESSAGE_TYPE_MAP.get(type(message))
         if converter is None:
             raise ValueError(f"Unsupported message type: {type(message).__name__}")