Feat langchain v1

CHANGLOG.md

@@ -1,3 +1,8 @@
+## [0.1.20] - 2025-11-10
+### Added
+- langchain callback support langchain V1
+- langchain callback support set tag and name
+
 ## [0.1.19] - 2025-11-10
 ### Fixed
 - fix baggage escape problem

cozeloop/integration/langchain/trace_callback.py

@@ -5,12 +5,13 @@
 import json
 import time
 import traceback
-from typing import List, Dict, Union, Any, Optional
+from typing import List, Dict, Union, Any, Optional, Callable, Protocol
 
 import pydantic
 from pydantic import Field, BaseModel
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentFinish, AgentAction, LLMResult
+from langchain_core.callbacks.base import BaseCallbackHandler
+from langchain_core.outputs import LLMResult, ChatGeneration
+from langchain_core.agents import AgentFinish, AgentAction
 from langchain_core.prompt_values import PromptValue, ChatPromptValue
 from langchain_core.messages import BaseMessage, AIMessageChunk, AIMessage
 from langchain_core.prompts import AIMessagePromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
@@ -28,24 +29,40 @@
 
 class LoopTracer:
     @classmethod
-    def get_callback_handler(cls, client: Client = None):
+    def get_callback_handler(
+            cls,
+            client: Client = None,
+            modify_name_fn: Optional[Callable[[str], str]] = None,
+            add_tags_fn: Optional[Callable[[str], Dict[str, Any]]] = None,
+            tags: Dict[str, Any] = None,
+    ):
         """
         Do not hold it for a long time, get a new callback_handler for each request.
+        modify_name_fn: modify name function, input is node name(if you use langgraph, like add_node(node_name, node_func), it is node name), output is span name.
+        add_tags_fn:    add tags function, input is node name(if you use langgraph, like add_node(node_name, node_func), it is node name), output is tags dict.
         """
         global _trace_callback_client
         if client:
             _trace_callback_client = client
         else:
             _trace_callback_client = get_default_client()
 
-        return LoopTraceCallbackHandler()
+        return LoopTraceCallbackHandler(modify_name_fn, add_tags_fn, tags)
 
 
 class LoopTraceCallbackHandler(BaseCallbackHandler):
-    def __init__(self):
+    def __init__(
+            self,
+            name_fn: Optional[Callable[[str], str]] = None,
+            tags_fn: Optional[Callable[[str], Dict[str, Any]]] = None,
+            tags: Dict[str, Any] = None,
+    ):
         super().__init__()
         self._space_id = _trace_callback_client.workspace_id
         self.run_map: Dict[str, Run] = {}
+        self.name_fn = name_fn
+        self.tags_fn = tags_fn
+        self._tags = tags if tags else {}
 
     def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> Any:
         span_tags = {}
@@ -97,32 +114,26 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
         try:
             # set output span_tag
             flow_span.set_tags({'output': ModelTraceOutput(response.generations).to_json()})
+            # set model tags
+            tags = self._get_model_tags(response, **kwargs)
+            if tags:
+                self._set_span_tags(flow_span, tags, need_convert_tag_value=False)
         except Exception as e:
             flow_span.set_error(e)
-        # calculate token usage，and set span_tag
-        if response.llm_output is not None and 'token_usage' in response.llm_output and response.llm_output['token_usage']:
-            self._set_span_tags(flow_span, response.llm_output['token_usage'], need_convert_tag_value=False)
-        else:
-            try:
-                run_info = self.run_map[str(kwargs['run_id'])]
-                if run_info is not None and run_info.model_meta is not None:
-                    model_name = run_info.model_meta.model_name
-                    input_messages = run_info.model_meta.message
-                    flow_span.set_input_tokens(calc_token_usage(input_messages, model_name))
-                    flow_span.set_output_tokens(calc_token_usage(response, model_name))
-            except Exception as e:
-                flow_span.set_error(e)
         # finish flow_span
-        flow_span.finish()
+        self._end_flow_span(flow_span)
 
     def on_chain_start(self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> Any:
         flow_span = None
         try:
             if kwargs.get('run_type', '') == 'prompt' or kwargs.get('name', '') == 'ChatPromptTemplate':
-                flow_span = self._new_flow_span(kwargs['name'], kwargs['name'], **kwargs)
+                flow_span = self._new_flow_span(kwargs['name'], 'prompt', **kwargs)
                 self._on_prompt_start(flow_span, serialized, inputs, **kwargs)
             else:
-                flow_span = self._new_flow_span(kwargs['name'], kwargs['name'], **kwargs)
+                span_type = 'chain'
+                if kwargs['name'] == 'LangGraph':  # LangGraph is agent span_type，for trajectory evaluation aggregate to an agent
+                    span_type = 'agent'
+                flow_span = self._new_flow_span(kwargs['name'], span_type, **kwargs)
                 flow_span.set_tags({'input': _convert_2_json(inputs)})
         except Exception as e:
             if flow_span is not None:
@@ -141,7 +152,7 @@ def on_chain_end(self, outputs: Union[Dict[str, Any], Any], **kwargs: Any) -> An
                 flow_span.set_tags({'output': _convert_2_json(outputs)})
         except Exception as e:
             flow_span.set_error(e)
-        flow_span.finish()
+        self._end_flow_span(flow_span)
 
     def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> Any:
         flow_span = self._get_flow_span(**kwargs)
@@ -150,7 +161,7 @@ def on_chain_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: A
             flow_span = self._new_flow_span(span_name, 'chain_error', **kwargs)
         flow_span.set_error(error)
         flow_span.set_tags({'error_trace': traceback.format_exc()})
-        flow_span.finish()
+        self._end_flow_span(flow_span)
 
     def on_tool_start(
             self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
@@ -166,7 +177,7 @@ def on_tool_end(self, output: str, **kwargs: Any) -> Any:
             flow_span.set_tags({'output': _convert_2_json(output)})
         except Exception as e:
             flow_span.set_error(e)
-        flow_span.finish()
+        self._end_flow_span(flow_span)
 
     def on_tool_error(
             self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
@@ -177,7 +188,7 @@ def on_tool_error(
             flow_span = self._new_flow_span(span_name, 'tool_error', **kwargs)
         flow_span.set_error(error)
         flow_span.set_tags({'error_trace': traceback.format_exc()})
-        flow_span.finish()
+        self._end_flow_span(flow_span)
 
     def on_text(self, text: str, **kwargs: Any) -> Any:
         """Run on arbitrary text."""
@@ -188,6 +199,67 @@ def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
         return
 
+    def _end_flow_span(self, span: Span):
+        span.set_tags(self._tags)
+        span.finish()
+
+    def _get_model_tags(self, response: LLMResult, **kwargs: Any) -> Dict[str, Any]:
+        return self._get_model_token_tags(response, **kwargs)
+
+    def _get_model_token_tags(self, response: LLMResult, **kwargs: Any) -> Dict[str, Any]:
+        result = {}
+        is_get_from_langchain = False
+        if response.llm_output is not None and 'token_usage' in response.llm_output and response.llm_output[
+            'token_usage']:
+            is_get_from_langchain = True
+            result['input_tokens'] = response.llm_output.get('token_usage', {}).get('prompt_tokens', 0)
+            result['output_tokens'] = response.llm_output.get('token_usage', {}).get('completion_tokens', 0)
+            result['tokens'] = result['input_tokens'] + result['output_tokens']
+            reasoning_tokens = response.llm_output.get('token_usage', {}).get('completion_tokens_details', {}).get(
+                'reasoning_tokens', 0)
+            if reasoning_tokens:
+                result['reasoning_tokens'] = reasoning_tokens
+            input_cached_tokens = response.llm_output.get('token_usage', {}).get('prompt_tokens_details', {}).get(
+                'cached_tokens', 0)
+            if input_cached_tokens:
+                result['input_cached_tokens'] = input_cached_tokens
+        elif response.generations is not None and len(response.generations) > 0 and response.generations[0] is not None:
+            for i, generation in enumerate(response.generations[0]):
+                if isinstance(generation, ChatGeneration) and isinstance(generation.message,(AIMessageChunk, AIMessage)) and generation.message.usage_metadata:
+                    is_get_from_langchain = True
+                    result['input_tokens'] = generation.message.usage_metadata.get('input_tokens', 0)
+                    result['output_tokens'] = generation.message.usage_metadata.get('output_tokens', 0)
+                    result['tokens'] = result['input_tokens'] + result['output_tokens']
+                    if generation.message.usage_metadata.get('output_token_details', {}):
+                        reasoning_tokens = generation.message.usage_metadata.get('output_token_details', {}).get('reasoning', 0)
+                        if reasoning_tokens:
+                            result['reasoning_tokens'] = reasoning_tokens
+                    if generation.message.usage_metadata.get('input_token_details', {}):
+                        input_read_cached_tokens = generation.message.usage_metadata.get('input_token_details', {}).get('cache_read', 0)
+                        if input_read_cached_tokens:
+                            result['input_cached_tokens'] = input_read_cached_tokens
+                        input_creation_cached_tokens = generation.message.usage_metadata.get('input_token_details', {}).get('cache_creation', 0)
+                        if input_creation_cached_tokens:
+                            result['input_creation_cached_tokens'] = input_creation_cached_tokens
+        if is_get_from_langchain:
+            return result
+        else:
+            try:
+                run_info = self.run_map[str(kwargs['run_id'])]
+                if run_info is not None and run_info.model_meta is not None:
+                    model_name = run_info.model_meta.model_name
+                    input_messages = run_info.model_meta.message
+                    token_usage = {
+                        'input_tokens': calc_token_usage(input_messages, model_name),
+                        'output_tokens': calc_token_usage(response, model_name),
+                        'tokens': 0
+                    }
+                    token_usage['tokens'] = token_usage['input_tokens'] + token_usage['output_tokens']
+                    return token_usage
+            except Exception as e:
+                span_tags = {'error_info': repr(e), 'error_trace': traceback.format_exc()}
+                return span_tags
+
     def _on_prompt_start(self, flow_span, serialized: Dict[str, Any], inputs: (Dict[str, Any], str), **kwargs: Any) -> None:
         # get inputs
         params: List[Argument] = []
@@ -233,18 +305,38 @@ def _on_prompt_start(self, flow_span, serialized: Dict[str, Any], inputs: (Dict[
             flow_span.set_tags({'prompt_version': kwargs['metadata']['lc_hub_commit_hash']})
             flow_span.set_tags({'prompt_provider': 'langsmith'})
 
-    def _new_flow_span(self, span_name: str, span_type: str, **kwargs: Any) -> Span:
+    def _new_flow_span(self, node_name: str, span_type: str, **kwargs: Any) -> Span:
         span_type = _span_type_mapping(span_type)
+        span_name = node_name
         # set parent span
         parent_span: Span = None
         if 'parent_run_id' in kwargs and kwargs['parent_run_id'] is not None and str(kwargs['parent_run_id']) in self.run_map:
             parent_span = self.run_map[str(kwargs['parent_run_id'])].span
+        # modify name
+        error_tag = {}
+        try:
+            if self.name_fn:
+                name = self.name_fn(node_name)
+                if name:
+                    span_name = name
+        except Exception as e:
+            error_tag = {'error_info': f'name_fn error {repr(e)}', 'error_trace': traceback.format_exc()}
         # new span
         flow_span = _trace_callback_client.start_span(span_name, span_type, child_of=parent_span)
         run_id = str(kwargs['run_id'])
         self.run_map[run_id] = Run(run_id, flow_span, span_type)
         # set default tags
         flow_span.set_runtime(RuntimeInfo())
+        # set extra tags
+        try:
+            if self.tags_fn:
+                tags = self.tags_fn(node_name)
+                if isinstance(tags, dict):
+                    flow_span.set_tags(tags)
+        except Exception as e:
+            error_tag = {'error_info': f'tags_fn error {repr(e)}', 'error_trace': traceback.format_exc()}
+        if error_tag:
+            flow_span.set_tags(error_tag)
         return flow_span
 
     def _get_flow_span(self, **kwargs: Any) -> Span: