Add IBM watsonx.ai instrumentation (#941)

* Add IBM watsonx.ai instrumentation

* Add IBMMachineLearningInstrumentor and extend watsonx.ai instrumentation with new methods

* Remove deprecated IBM Machine Learning instrumentation and update IBMWatsonXInstrumentor to reflect changes in library dependencies.

* Refactor IBM WatsonX AI instrumentation.

* Fix

* Added Exmaples

---------

Co-authored-by: Braelyn Boynton <bboynton97@gmail.com>

Author: Dwij1704

agentops/instrumentation/__init__.py

@@ -77,11 +77,16 @@ def get_instance(self) -> BaseInstrumentor:
         class_name="GoogleGenerativeAIInstrumentor",
         provider_import_name="google.genai",
     ),
+    InstrumentorLoader(
+        module_name="agentops.instrumentation.ibm_watsonx_ai",
+        class_name="IBMWatsonXInstrumentor",
+        provider_import_name="ibm_watsonx_ai",
+    ),
     InstrumentorLoader(
         module_name="agentops.instrumentation.ag2",
         class_name="AG2Instrumentor",
         provider_import_name="autogen",
-    ),
+    )
 ]
 
 

agentops/instrumentation/ibm_watsonx_ai/__init__.py

@@ -0,0 +1,32 @@
+"""IBM WatsonX AI instrumentation for AgentOps.
+
+This package provides instrumentation for IBM's WatsonX AI foundation models,
+capturing telemetry for model interactions including completions, chat, and streaming responses.
+"""
+
+import logging
+from typing import Collection
+
+logger = logging.getLogger(__name__)
+
+def get_version() -> str:
+    """Get the version of the IBM watsonx.ai SDK, or 'unknown' if not found."""
+    try:
+        from importlib.metadata import version
+        return version("ibm-watsonx-ai")
+    except ImportError:
+        logger.debug("Could not find IBM WatsonX AI SDK version")
+        return "1.3.11"  # Default to known supported version if not found
+
+# Library identification for instrumentation
+LIBRARY_NAME = "ibm_watsonx_ai"
+LIBRARY_VERSION = get_version()
+
+# Import after defining constants to avoid circular imports
+from agentops.instrumentation.ibm_watsonx_ai.instrumentor import IBMWatsonXInstrumentor  # noqa: E402
+
+__all__ = [
+    "LIBRARY_NAME",
+    "LIBRARY_VERSION",
+    "IBMWatsonXInstrumentor",
+] 

agentops/instrumentation/ibm_watsonx_ai/attributes/__init__.py

@@ -0,0 +1,27 @@
+"""Attribute extraction utilities for IBM watsonx.ai instrumentation."""
+
+from agentops.instrumentation.ibm_watsonx_ai.attributes.attributes import (
+    get_generate_attributes,
+    get_chat_attributes,
+    get_tokenize_attributes,
+    get_model_details_attributes
+)
+from agentops.instrumentation.ibm_watsonx_ai.attributes.common import (
+    extract_params_attributes,
+    convert_params_to_dict,
+    extract_prompt_from_args,
+    extract_messages_from_args,
+    extract_params_from_args
+)
+
+__all__ = [
+    "get_generate_attributes",
+    "get_chat_attributes",
+    "get_tokenize_attributes",
+    "get_model_details_attributes",
+    "extract_params_attributes",
+    "convert_params_to_dict",
+    "extract_prompt_from_args",
+    "extract_messages_from_args",
+    "extract_params_from_args"
+] 

agentops/instrumentation/ibm_watsonx_ai/attributes/attributes.py

@@ -0,0 +1,244 @@
+"""Attributes for IBM watsonx.ai model instrumentation.
+
+This module provides attribute extraction functions for IBM watsonx.ai model operations.
+"""
+from typing import Any, Dict, Optional, Tuple
+from agentops.instrumentation.common.attributes import AttributeMap
+from agentops.semconv import SpanAttributes, MessageAttributes
+from agentops.instrumentation.ibm_watsonx_ai.attributes.common import (
+    extract_params_attributes,
+    convert_params_to_dict,
+    extract_prompt_from_args,
+    extract_messages_from_args,
+    extract_params_from_args
+)
+from ibm_watsonx_ai.foundation_models.schema import TextGenParameters, TextChatParameters
+
+def get_generate_attributes(args: Optional[Tuple] = None, kwargs: Optional[Dict] = None, return_value: Optional[Any] = None) -> AttributeMap:
+    """Extract token usage attributes from generate method calls."""
+    attributes = {}
+    
+    # Extract prompt using helper function
+    prompt = extract_prompt_from_args(args, kwargs)
+    if prompt:
+        attributes[MessageAttributes.PROMPT_ROLE.format(i=0)] = "user"
+        attributes[MessageAttributes.PROMPT_CONTENT.format(i=0)] = prompt
+        attributes[MessageAttributes.PROMPT_TYPE.format(i=0)] = "text"
+    
+    # Extract parameters using helper functions
+    params = extract_params_from_args(args, kwargs)
+    if params:
+        params_dict = convert_params_to_dict(params)
+        if params_dict:
+            attributes.update(extract_params_attributes(params_dict))
+                
+    # Extract response information
+    if return_value:
+        if isinstance(return_value, dict):
+            # Extract model information
+            if 'model_id' in return_value:
+                attributes[SpanAttributes.LLM_REQUEST_MODEL] = return_value['model_id']
+                
+            # Handle results
+            if 'results' in return_value:
+                for idx, result in enumerate(return_value['results']):
+                    # Extract completion
+                    if 'generated_text' in result:
+                        attributes[MessageAttributes.COMPLETION_CONTENT.format(i=idx)] = result['generated_text']
+                        attributes[MessageAttributes.COMPLETION_ROLE.format(i=idx)] = "assistant"
+                        attributes[MessageAttributes.COMPLETION_TYPE.format(i=idx)] = "text"
+                    
+                    # Extract token usage
+                    if 'input_token_count' in result:
+                        attributes[SpanAttributes.LLM_USAGE_PROMPT_TOKENS] = result['input_token_count']
+                    if 'generated_token_count' in result:
+                        attributes[SpanAttributes.LLM_USAGE_COMPLETION_TOKENS] = result['generated_token_count']
+                    if 'input_token_count' in result and 'generated_token_count' in result:
+                        attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] = result['input_token_count'] + result['generated_token_count']
+                        
+                    if 'stop_reason' in result:
+                        attributes[SpanAttributes.LLM_RESPONSE_STOP_REASON] = result['stop_reason']
+                
+    return attributes
+
+def get_tokenize_attributes(args: Optional[Tuple] = None, kwargs: Optional[Dict] = None, return_value: Optional[Any] = None) -> AttributeMap:
+    """Extract attributes from tokenize method calls."""
+    attributes = {}
+    
+    # Extract input from args or kwargs using helper function
+    prompt = extract_prompt_from_args(args, kwargs)
+    if prompt:
+        attributes[MessageAttributes.PROMPT_ROLE.format(i=0)] = "user"
+        attributes[MessageAttributes.PROMPT_CONTENT.format(i=0)] = prompt
+        attributes[MessageAttributes.PROMPT_TYPE.format(i=0)] = "text"
+        
+    # Extract response information
+    if return_value and isinstance(return_value, dict):
+        if "model_id" in return_value:
+            attributes[SpanAttributes.LLM_REQUEST_MODEL] = return_value["model_id"]
+        if "result" in return_value:
+            attributes["ibm.watsonx.tokenize.result"] = str(return_value["result"])
+            if "token_count" in return_value["result"]:
+                attributes[SpanAttributes.LLM_USAGE_PROMPT_TOKENS] = return_value["result"]["token_count"]
+            
+    return attributes
+
+def get_model_details_attributes(args: Optional[Tuple] = None, kwargs: Optional[Dict] = None, return_value: Optional[Any] = None) -> AttributeMap:
+    """Extract attributes from get_details method calls."""
+    if not isinstance(return_value, dict):
+        return {}
+        
+    # Basic model information
+    attributes = {
+        f"ibm.watsonx.model.{key}": value
+        for key, value in return_value.items()
+        if key in ["model_id", "label", "provider", "source", "short_description", "long_description", 
+                  "number_params", "input_tier", "output_tier"]
+    }
+    
+    # Model functions
+    if "functions" in return_value:
+        attributes["ibm.watsonx.model.functions"] = str([func["id"] for func in return_value["functions"]])
+        
+    # Model tasks
+    if "tasks" in return_value:
+        task_info = [
+            {k: v for k, v in task.items() if k in ["id", "ratings", "tags"]}
+            for task in return_value["tasks"]
+        ]
+        attributes["ibm.watsonx.model.tasks"] = str(task_info)
+        
+    # Model limits
+    if "model_limits" in return_value:
+        limits = return_value["model_limits"]
+        attributes.update({
+            f"ibm.watsonx.model.{key}": value
+            for key, value in limits.items()
+            if key in ["max_sequence_length", "max_output_tokens", "training_data_max_records"]
+        })
+        
+    # Service tier limits
+    if "limits" in return_value:
+        for tier, tier_limits in return_value["limits"].items():
+            attributes.update({
+                f"ibm.watsonx.model.limits.{tier}.{key}": value
+                for key, value in tier_limits.items()
+                if key in ["call_time", "max_output_tokens"]
+            })
+            
+    # Model lifecycle
+    if "lifecycle" in return_value:
+        attributes.update({
+            f"ibm.watsonx.model.lifecycle.{stage['id']}": stage["start_date"]
+            for stage in return_value["lifecycle"]
+            if "id" in stage and "start_date" in stage
+        })
+        
+    # Training parameters
+    if "training_parameters" in return_value:
+        attributes.update({
+            f"ibm.watsonx.model.training.{key}": str(value) if isinstance(value, dict) else value
+            for key, value in return_value["training_parameters"].items()
+        })
+        
+    return attributes
+
+def get_chat_attributes(args: Optional[Tuple] = None, kwargs: Optional[Dict] = None, return_value: Optional[Any] = None) -> AttributeMap:
+    """Extract attributes from chat method calls."""
+    attributes = {}
+    
+    # Extract messages using helper function
+    messages = extract_messages_from_args(args, kwargs)
+    if messages:
+        # Process each message in the conversation
+        for i, message in enumerate(messages):
+            if not isinstance(message, dict):
+                continue
+                
+            # Extract role and content
+            role = message.get('role', '')
+            content = message.get('content', [])
+            
+            # Handle content which can be a list of different types (text, image_url)
+            if isinstance(content, list):
+                # Combine all text content
+                text_content = []
+                image_urls = []
+                
+                for content_item in content:
+                    if isinstance(content_item, dict):
+                        if content_item.get('type') == 'text':
+                            text_content.append(content_item.get('text', ''))
+                        elif content_item.get('type') == 'image_url':
+                            image_url = content_item.get('image_url', {})
+                            if isinstance(image_url, dict) and 'url' in image_url:
+                                url = image_url['url']
+                                # Only store URLs that start with http, otherwise use placeholder
+                                if url and isinstance(url, str) and url.startswith(('http://', 'https://')):
+                                    image_urls.append(url)
+                                else:
+                                    image_urls.append("[IMAGE_PLACEHOLDER]")
+                
+                # Set text content if any
+                if text_content:
+                    attributes[MessageAttributes.PROMPT_CONTENT.format(i=i)] = ' '.join(text_content)
+                    attributes[MessageAttributes.PROMPT_TYPE.format(i=i)] = "text"
+                    attributes[MessageAttributes.PROMPT_ROLE.format(i=i)] = role
+                
+                # Set image URLs if any
+                if image_urls:
+                    attributes[f"ibm.watsonx.chat.message.{i}.images"] = str(image_urls)
+            else:
+                # Handle string content
+                attributes[MessageAttributes.PROMPT_CONTENT.format(i=i)] = str(content)
+                attributes[MessageAttributes.PROMPT_TYPE.format(i=i)] = "text"
+                attributes[MessageAttributes.PROMPT_ROLE.format(i=i)] = role
+    
+    # Extract parameters using helper functions
+    params = extract_params_from_args(args, kwargs)
+    if params:
+        params_dict = convert_params_to_dict(params)
+        if params_dict:
+            attributes.update(extract_params_attributes(params_dict))
+
+    # Extract response information
+    if return_value and isinstance(return_value, dict):
+        # Extract model information
+        if 'model_id' in return_value:
+            attributes[SpanAttributes.LLM_REQUEST_MODEL] = return_value['model_id']
+        elif 'model' in return_value:
+            attributes[SpanAttributes.LLM_REQUEST_MODEL] = return_value['model']
+            
+        # Extract completion from choices
+        if 'choices' in return_value:
+            for idx, choice in enumerate(return_value['choices']):
+                if isinstance(choice, dict) and 'message' in choice:
+                    message = choice['message']
+                    if isinstance(message, dict):
+                        if 'content' in message:
+                            attributes[MessageAttributes.COMPLETION_CONTENT.format(i=idx)] = message['content']
+                            attributes[MessageAttributes.COMPLETION_ROLE.format(i=idx)] = message.get('role', 'assistant')
+                            attributes[MessageAttributes.COMPLETION_TYPE.format(i=idx)] = "text"
+                        if 'finish_reason' in choice:
+                            attributes[SpanAttributes.LLM_RESPONSE_STOP_REASON] = choice['finish_reason']
+        
+        # Extract token usage
+        if 'usage' in return_value:
+            usage = return_value['usage']
+            if isinstance(usage, dict):
+                if 'prompt_tokens' in usage:
+                    attributes[SpanAttributes.LLM_USAGE_PROMPT_TOKENS] = usage['prompt_tokens']
+                if 'completion_tokens' in usage:
+                    attributes[SpanAttributes.LLM_USAGE_COMPLETION_TOKENS] = usage['completion_tokens']
+                if 'total_tokens' in usage:
+                    attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] = usage['total_tokens']
+        
+        # Extract additional metadata
+        if 'id' in return_value:
+            attributes['ibm.watsonx.chat.id'] = return_value['id']
+        if 'model_version' in return_value:
+            attributes['ibm.watsonx.model.version'] = return_value['model_version']
+        if 'created_at' in return_value:
+            attributes['ibm.watsonx.chat.created_at'] = return_value['created_at']
+                
+    return attributes