support genai and also add token reporting

Author: rohit-kadhe

src/examples/langchain_example/__init__.py

@@ -1,3 +1,4 @@
+from examples.langchain_example.langchain_google_genai import basic_google_genai
 from .basic import basic_app, rag, load_and_split
 from langtrace_python_sdk import with_langtrace_root_span
 
@@ -8,10 +9,11 @@
 class LangChainRunner:
     @with_langtrace_root_span("LangChain")
     def run(self):
-        basic_app()
-        rag()
-        load_and_split()
-        basic_graph_tools()
+        # basic_app()
+        # rag()
+        # load_and_split()
+        # basic_graph_tools()
+        basic_google_genai()
 
 
 class GroqRunner:

src/examples/langchain_example/langchain_google_genai.py

@@ -0,0 +1,21 @@
+from langchain_core.messages import HumanMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langtrace_python_sdk.utils.with_root_span import with_langtrace_root_span
+
+
+@with_langtrace_root_span("basic_google_genai")
+def basic_google_genai():
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
+    # example
+    message = HumanMessage(
+        content=[
+            {
+                "type": "text",
+                "text": "What's in this image?",
+            },
+        ]
+    )
+    message_image = HumanMessage(content="https://picsum.photos/seed/picsum/200/300")
+
+    res = llm.invoke([message, message_image])
+    # print(res)

src/langtrace_python_sdk/instrumentation/langchain/instrumentation.py

@@ -81,21 +81,16 @@ def _instrument(self, **kwargs):
         tracer_provider = kwargs.get("tracer_provider")
         tracer = get_tracer(__name__, "", tracer_provider)
         version = importlib.metadata.version("langchain")
-
         wrap_function_wrapper(
             "langchain.agents.agent",
             "RunnableAgent.plan",
-            generic_patch(
-                "RunnableAgent.plan", "plan", tracer, version, True, True
-            ),
+            generic_patch("RunnableAgent.plan", "plan", tracer, version, True, True),
         )
 
         wrap_function_wrapper(
             "langchain.agents.agent",
             "RunnableAgent.aplan",
-            generic_patch(
-                "RunnableAgent.aplan", "plan", tracer, version, True, True
-            ),
+            generic_patch("RunnableAgent.aplan", "plan", tracer, version, True, True),
         )
 
         # modules_to_patch = []

src/langtrace_python_sdk/instrumentation/langchain_core/instrumentation.py

@@ -134,6 +134,20 @@ def _instrument(self, **kwargs):
         ]
 
         modules_to_patch = [
+            (
+                "langchain_core.language_models.chat_models",
+                "chatmodel",
+                generic_patch,
+                True,
+                True,
+            ),
+            (
+                "langchain_core.language_models.base",
+                "language_model",
+                generic_patch,
+                True,
+                True,
+            ),
             ("langchain_core.retrievers", "retriever", generic_patch, True, True),
             ("langchain_core.prompts.chat", "prompt", generic_patch, True, True),
             (

src/langtrace_python_sdk/instrumentation/langchain_core/patch.py

@@ -57,7 +57,24 @@ def traced_method(wrapped, instance, args, kwargs):
             "langtrace.service.version": version,
             "langtrace.version": v(LANGTRACE_SDK_NAME),
             "langchain.task.name": task,
-            **(extra_attributes if extra_attributes is not None else {}),
+            "gen_ai.request.model": (
+                instance.model if hasattr(instance, "model") else None
+            ),
+            SpanAttributes.LLM_REQUEST_MAX_TOKENS: (
+                instance.max_output_tokens
+                if hasattr(instance, "max_output_tokens")
+                else None
+            ),
+            SpanAttributes.LLM_TOP_K: (
+                instance.top_k if hasattr(instance, "top_k") else None
+            ),
+            SpanAttributes.LLM_REQUEST_TOP_P: (
+                instance.top_p if hasattr(instance, "top_p") else None
+            ),
+            SpanAttributes.LLM_REQUEST_TEMPERATURE: (
+                instance.temperature if hasattr(instance, "temperature") else None
+            ),
+            **(extra_attributes if extra_attributes is not None else {}),  # type: ignore
         }
 
         if trace_input and len(args) > 0:
@@ -79,21 +96,17 @@ def traced_method(wrapped, instance, args, kwargs):
             try:
                 # Attempt to call the original method
                 result = wrapped(*args, **kwargs)
-
                 if trace_output:
                     span.set_attribute("langchain.outputs", to_json_string(result))
-                    if hasattr(result, 'usage'):
-                        prompt_tokens = result.usage.prompt_tokens
-                        completion_tokens = result.usage.completion_tokens
-                        span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS, prompt_tokens)
-                        span.set_attribute(SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, completion_tokens)
-                    elif hasattr(result, 'generations') and len(result.generations) > 0 and len(result.generations[0]) > 0 and hasattr(result.generations[0][0], 'text') and isinstance(result.generations[0][0].text, str):
-                        span.set_attribute(SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, instance.get_num_tokens(result.generations[0][0].text))
-                    elif len(args) > 0 and len(args[0]) > 0 and not hasattr(args[0][0], 'text') and hasattr(instance, 'get_num_tokens'):
-                        span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS, instance.get_num_tokens(args[0][0]))
-                    elif len(args) > 0 and len(args[0]) > 0 and hasattr(args[0][0], 'text') and isinstance(args[0][0].text, str) and hasattr(instance, 'get_num_tokens'):
-                        span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS, instance.get_num_tokens(args[0][0].text))
-
+                    if hasattr(result, "usage_metadata"):
+                        span.set_attribute(
+                            SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+                            result.usage_metadata["input_tokens"],
+                        )
+                        span.set_attribute(
+                            SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+                            result.usage_metadata["output_tokens"],
+                        )
                 span.set_status(StatusCode.OK)
                 return result
             except Exception as err:
@@ -208,9 +221,17 @@ def clean_empty(d):
     if not isinstance(d, (dict, list, tuple)):
         return d
     if isinstance(d, tuple):
-        return tuple(val for val in (clean_empty(val) for val in d) if val != () and val is not None)
+        return tuple(
+            val
+            for val in (clean_empty(val) for val in d)
+            if val != () and val is not None
+        )
     if isinstance(d, list):
-        return [val for val in (clean_empty(val) for val in d) if val != [] and val is not None]
+        return [
+            val
+            for val in (clean_empty(val) for val in d)
+            if val != [] and val is not None
+        ]
     result = {}
     for k, val in d.items():
         if isinstance(val, dict):
@@ -226,7 +247,7 @@ def clean_empty(d):
                 result[k] = val.strip()
         elif isinstance(val, object):
             # some langchain objects have a text attribute
-            val = getattr(val, 'text', None)
+            val = getattr(val, "text", None)
             if val is not None and val.strip() != "":
                 result[k] = val.strip()
     return result

src/langtrace_python_sdk/langtrace.py

@@ -128,8 +128,8 @@ def init(
         "embedchain": EmbedchainInstrumentation(),
         "qdrant-client": QdrantInstrumentation(),
         "langchain": LangchainInstrumentation(),
-        "langchain-core": LangchainCoreInstrumentation(),
-        "langchain-community": LangchainCommunityInstrumentation(),
+        "langchain_core": LangchainCoreInstrumentation(),
+        "langchain_community": LangchainCommunityInstrumentation(),
         "langgraph": LanggraphInstrumentation(),
         "anthropic": AnthropicInstrumentation(),
         "cohere": CohereInstrumentation(),
@@ -190,7 +190,7 @@ def init(
 
 
 def init_instrumentations(
-    disable_instrumentations: DisableInstrumentations, all_instrumentations: dict
+    disable_instrumentations: DisableInstrumentations | None, all_instrumentations: dict
 ):
     if disable_instrumentations is None:
         for idx, (name, v) in enumerate(all_instrumentations.items()):

src/run_example.py

@@ -2,7 +2,7 @@
 
 ENABLED_EXAMPLES = {
     "anthropic": False,
-    "azureopenai": True,
+    "azureopenai": False,
     "chroma": False,
     "cohere": False,
     "fastapi": False,