feat(langchain): add v1 support

langfuse/_utils/serializer.py

@@ -19,7 +19,7 @@
 
 # Attempt to import Serializable
 try:
-    from langchain.load.serializable import Serializable
+    from langchain_core.load.serializable import Serializable
 except ImportError:
     # If Serializable is not available, set it to a placeholder type
     class Serializable:  # type: ignore

langfuse/langchain/CallbackHandler.py

@@ -1,5 +1,17 @@
-import typing
 from contextvars import Token
+from typing import (
+    Any,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Set,
+    Type,
+    Union,
+    cast,
+)
+from uuid import UUID
 
 import pydantic
 from opentelemetry import context, trace
@@ -16,41 +28,52 @@
     LangfuseSpan,
     LangfuseTool,
 )
+from langfuse._utils import _get_timestamp
+from langfuse.langchain.utils import _extract_model_name
 from langfuse.logger import langfuse_logger
 
 try:
-    import langchain  # noqa
-
-except ImportError as e:
-    langfuse_logger.error(
-        f"Could not import langchain. The langchain integration will not work. {e}"
-    )
+    import langchain
 
-from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Type, Union, cast
-from uuid import UUID
+    if langchain.__version__.startswith("1"):
+        # Langchain v1
+        from langchain_core.agents import AgentAction, AgentFinish
+        from langchain_core.callbacks import (
+            BaseCallbackHandler as LangchainBaseCallbackHandler,
+        )
+        from langchain_core.documents import Document
+        from langchain_core.messages import (
+            AIMessage,
+            BaseMessage,
+            ChatMessage,
+            FunctionMessage,
+            HumanMessage,
+            SystemMessage,
+            ToolMessage,
+        )
+        from langchain_core.outputs import ChatGeneration, LLMResult
 
-from langfuse._utils import _get_timestamp
-from langfuse.langchain.utils import _extract_model_name
+    else:
+        # Langchain v0
+        from langchain.callbacks.base import (  # type: ignore
+            BaseCallbackHandler as LangchainBaseCallbackHandler,
+        )
+        from langchain.schema.agent import AgentAction, AgentFinish  # type: ignore
+        from langchain.schema.document import Document  # type: ignore
+        from langchain_core.messages import (
+            AIMessage,
+            BaseMessage,
+            ChatMessage,
+            FunctionMessage,
+            HumanMessage,
+            SystemMessage,
+            ToolMessage,
+        )
+        from langchain_core.outputs import (
+            ChatGeneration,
+            LLMResult,
+        )
 
-try:
-    from langchain.callbacks.base import (
-        BaseCallbackHandler as LangchainBaseCallbackHandler,
-    )
-    from langchain.schema.agent import AgentAction, AgentFinish
-    from langchain.schema.document import Document
-    from langchain_core.messages import (
-        AIMessage,
-        BaseMessage,
-        ChatMessage,
-        FunctionMessage,
-        HumanMessage,
-        SystemMessage,
-        ToolMessage,
-    )
-    from langchain_core.outputs import (
-        ChatGeneration,
-        LLMResult,
-    )
 except ImportError:
     raise ModuleNotFoundError(
         "Please install langchain to use the Langfuse langchain integration: 'pip install langchain'"
@@ -1011,7 +1034,7 @@ def _flatten_comprehension(matrix: Any) -> Any:
     return [item for row in matrix for item in row]
 
 
-def _parse_usage_model(usage: typing.Union[pydantic.BaseModel, dict]) -> Any:
+def _parse_usage_model(usage: Union[pydantic.BaseModel, dict]) -> Any:
     # maintains a list of key translations. For each key, the usage model is checked
     # and a new object will be created with the new key if the key exists in the usage model
     # All non matched keys will remain on the object.

pyproject.toml

@@ -8,13 +8,12 @@ license = "MIT"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<4.0"
+python = ">=3.10,<4.0"
 httpx = ">=0.15.4,<1.0"
 pydantic = ">=1.10.7, <3.0"
 backoff = ">=1.10.0"
 openai = { version = ">=0.27.8", optional = true }
 wrapt = "^1.14"
-langchain = { version = ">=0.0.309", optional = true }
 packaging = ">=23.2,<26.0"
 requests = "^2"
 opentelemetry-api = "^1.33.1"
@@ -31,7 +30,8 @@ pytest-httpserver = "^1.0.8"
 ruff = ">=0.1.8,<0.13.0"
 mypy = "^1.0.0"
 langchain-openai = ">=0.0.5,<0.4"
-langgraph = ">=0.2.62,<0.7.0"
+langchain = ">=1"
+langgraph = ">=1"
 autoevals = "^0.0.130"
 
 [tool.poetry.group.docs.dependencies]

tests/test_datasets.py

@@ -3,7 +3,7 @@
 from concurrent.futures import ThreadPoolExecutor
 from typing import Sequence
 
-from langchain import PromptTemplate
+from langchain_core.prompts import PromptTemplate
 from langchain_openai import OpenAI
 
 from langfuse import Langfuse, observe

tests/test_decorators.py

@@ -7,7 +7,7 @@
 from typing import Optional
 
 import pytest
-from langchain.prompts import ChatPromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 from opentelemetry import trace
 

tests/test_json.py

@@ -7,7 +7,7 @@
 from unittest.mock import patch
 
 import pytest
-from langchain.schema.messages import HumanMessage
+from langchain.messages import HumanMessage
 from pydantic import BaseModel
 
 import langfuse