Integrate with AWS Bedrock
==========================

.. note::
   **Problem-solving guide for AWS Bedrock integration**
   
   This guide helps you solve specific problems when integrating HoneyHive with AWS Bedrock, with support for multiple instrumentor options.

This guide covers AWS Bedrock integration with HoneyHive's BYOI architecture, supporting both OpenInference and Traceloop instrumentors.

Compatibility
-------------

**Problem**: I need to know if my Python version and AWS Bedrock SDK version are compatible with HoneyHive.

**Solution**: Check the compatibility information below before installation.

Python Version Support
^^^^^^^^^^^^^^^^^^^^^^

.. list-table::
   :header-rows: 1
   :widths: 30 70

   * - Support Level
     - Python Versions
   * - Fully Supported
     - 3.11, 3.12, 3.13
   * - Not Supported
     - 3.10 and below

Provider SDK Requirements
^^^^^^^^^^^^^^^^^^^^^^^^^

- **Minimum**: boto3 >= 1.26.0
- **Recommended**: boto3 >= 1.28.0
- **Tested Versions**: 1.28.0, 1.29.0, 1.30.0

Instrumentor Compatibility
^^^^^^^^^^^^^^^^^^^^^^^^^^

.. list-table::
   :header-rows: 1
   :widths: 30 20 50

   * - Instrumentor
     - Status
     - Notes
   * - OpenInference
     - Fully Supported
     - Support for Claude, Titan, and Llama models on Bedrock
   * - Traceloop
     - Partial Support
     - Basic support, some Bedrock-specific features require OpenInference

Known Limitations
^^^^^^^^^^^^^^^^^

- **Model Support**: Claude, Titan, Llama 2 fully supported; other models experimental
- **Streaming**: Supported with both instrumentors, automatic span management
- **Cross-Region**: Requires proper AWS credentials and region configuration
- **Embedding Models**: Traced but may require manual metadata enrichment

.. note::
   For the complete compatibility matrix across all providers, see :doc:`/how-to/integrations/multi-provider`.

Choose Your Instrumentor
------------------------

**Problem**: I need to choose between OpenInference and Traceloop for AWS Bedrock integration.

**Solution**: Choose the instrumentor that best fits your needs:

- **OpenInference**: Open-source, lightweight, great for getting started
- **Traceloop**: Enhanced LLM metrics, cost tracking, production optimizations

.. raw:: html

   <div class="instrumentor-selector">
   <div class="instrumentor-tabs">
     <button class="instrumentor-button active" onclick="showInstrumentor(event, 'openinference-section')">OpenInference</button>
     <button class="instrumentor-button" onclick="showInstrumentor(event, 'openllmetry-section')">Traceloop</button>
   </div>

   <div id="openinference-section" class="instrumentor-content active">

.. raw:: html

   <div class="code-example">
   <div class="code-tabs">
     <button class="tab-button active" onclick="showTab(event, 'bedrock-openinference-install')">Installation</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openinference-basic')">Basic Setup</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openinference-advanced')">Advanced Usage</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openinference-troubleshoot')">Troubleshooting</button>
   </div>

   <div id="bedrock-openinference-install" class="tab-content active">

**Best for**: Open-source projects, simple tracing needs, getting started quickly

.. code-block:: bash

   # Recommended: Install with AWS Bedrock integration
   pip install honeyhive[openinference-bedrock]
   
   # Alternative: Manual installation
   pip install honeyhive openinference-instrumentation-bedrock boto3>=1.26.0

.. raw:: html

   </div>
   <div id="bedrock-openinference-basic" class="tab-content">

.. code-block:: python

   from honeyhive import HoneyHiveTracer
   from openinference.instrumentation.bedrock import BedrockInstrumentor
   import boto3
   import os

   # Environment variables (recommended for production)
   # .env file:
   # HH_API_KEY=your-honeyhive-key
   # AWS_ACCESS_KEY_ID=your-bedrock-key

   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )  # Uses HH_API_KEY from environment
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

   # Basic usage with error handling
   try:
       import boto3
       
       # Create Bedrock client
       bedrock = boto3.client(
           "bedrock-runtime",
           region_name="us-east-1"
       )
       
       # Invoke model
       response = bedrock.invoke_model(
           modelId="anthropic.claude-3-sonnet-20240229-v1:0",
           body=json.dumps({
               "anthropic_version": "bedrock-2023-05-31",
               "max_tokens": 1000,
               "messages": [{"role": "user", "content": "Hello from Bedrock!"}]
           })
       )
       # Automatically traced! ✨
   except botocore.exceptions.ClientError as e:
       print(f"AWS Bedrock API error: {e}")
   except Exception as e:
       print(f"Unexpected error: {e}")

.. raw:: html

   </div>
   <div id="bedrock-openinference-advanced" class="tab-content">

.. code-block:: python

   from honeyhive import HoneyHiveTracer, trace, enrich_span
   from honeyhive.models import EventType
   from openinference.instrumentation.bedrock import BedrockInstrumentor
   import boto3

   # Initialize with custom configuration
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       api_key="your-honeyhive-key",  # Or set HH_API_KEY environment variable
       project="your-project",        # Or set HH_PROJECT environment variable
       source="production"            # Or set HH_SOURCE environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

   @trace(tracer=tracer, event_type=EventType.chain)
   def multi_model_bedrock_workflow(prompts: List[str]) -> dict:
       """Advanced example with business context and multiple AWS Bedrock calls."""
       import boto3
       import json
       
       # Configure AWS Bedrock
       bedrock = boto3.client(
           "bedrock-runtime",
           region_name=os.getenv("AWS_REGION", "us-east-1")
       )
       
       # Add business context to the trace
       enrich_span({
           "business.input_type": type(prompts).__name__,
           "business.use_case": "multi_model_analysis",
           "bedrock.strategy": "bedrock_model_comparison",
           "instrumentor.type": "openinference"
       })
       
       try:
           # Test multiple Bedrock models
       models = [
           "anthropic.claude-3-sonnet-20240229-v1:0",
           "anthropic.claude-3-haiku-20240307-v1:0",
           "amazon.titan-text-express-v1"
       ]
       
       results = []
       for prompt in prompts:
           model_results = {}
           
           for model_id in models:
               try:
                   # Prepare request based on model type
                   if "anthropic" in model_id:
                       body = {
                           "anthropic_version": "bedrock-2023-05-31",
                           "max_tokens": 1000,
                           "messages": [{"role": "user", "content": prompt}]
                       }
                   elif "titan" in model_id:
                       body = {
                           "inputText": prompt,
                           "textGenerationConfig": {
                               "maxTokenCount": 1000,
                               "temperature": 0.7
                           }
                       }
                   
                   # Invoke model
                   response = bedrock.invoke_model(
                       modelId=model_id,
                       body=json.dumps(body)
                   )
                   
                   response_body = json.loads(response["body"].read())
                   model_results[model_id] = response_body
                   
               except Exception as e:
                   model_results[model_id] = {"error": str(e)}
           
           results.append({
               "prompt": prompt,
               "model_responses": model_results
           })
           
           # Add result metadata
           enrich_span({
               "business.successful": True,
               "bedrock.models_used": ["claude-3-sonnet", "claude-3-haiku", "titan-text"],
               "business.result_confidence": "high"
           })
           
           return {{RETURN_VALUE}}
           
       except botocore.exceptions.ClientError as e:
           enrich_span({
               "error.type": "api_error", 
               "error.message": str(e),
               "instrumentor.source": "openinference"
           })
           raise

.. raw:: html

   </div>
   <div id="bedrock-openinference-troubleshoot" class="tab-content">

**Common OpenInference Issues**:

1. **Missing Traces**
   
   .. code-block:: python
   
      # Use correct initialization pattern
      # Step 1: Initialize HoneyHive tracer first (without instrumentors)
      tracer = HoneyHiveTracer.init(
          project="your-project"  # Or set HH_PROJECT environment variable
      )
      
      # Step 2: Initialize instrumentor separately with tracer_provider
      instrumentor = BedrockInstrumentor()
      instrumentor.instrument(tracer_provider=tracer.provider)

2. **Performance for High Volume**
   
   .. code-block:: python
   
      # OpenInference uses efficient span processors automatically
      # No additional configuration needed

3. **Multiple Instrumentors**
   
   .. code-block:: python
   
      # You can combine OpenInference with other instrumentors
      from openinference.instrumentation.bedrock import BedrockInstrumentor
       from openinference.instrumentation.openai import OpenAIInstrumentor
       
       # Step 1: Initialize HoneyHive tracer first (without instrumentors)
       tracer = HoneyHiveTracer.init(
           project="your-project"  # Or set HH_PROJECT environment variable
       )
       
       # Step 2: Initialize instrumentors separately with tracer_provider
       # REPLACE_WITH_INSTRUMENTOR_SETUP
               BedrockInstrumentor(),
              OpenAIInstrumentor()
          ]
      )

4. **Environment Configuration**
   
   .. code-block:: bash
   
      # HoneyHive configuration
      export HH_API_KEY="your-honeyhive-api-key"
      export HH_SOURCE="production"
      
      # AWS Bedrock configuration
      export AWS_ACCESS_KEY_ID="your-aws-access-key"
      export AWS_SECRET_ACCESS_KEY="your-aws-secret-key"
      export AWS_DEFAULT_REGION="us-east-1"

.. raw:: html

   </div>
   </div>

.. raw:: html

   </div>

   <div id="openllmetry-section" class="instrumentor-content">

.. raw:: html

   <div class="code-example">
   <div class="code-tabs">
     <button class="tab-button active" onclick="showTab(event, 'bedrock-openllmetry-install')">Installation</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openllmetry-basic')">Basic Setup</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openllmetry-advanced')">Advanced Usage</button>
     <button class="tab-button" onclick="showTab(event, 'bedrock-openllmetry-troubleshoot')">Troubleshooting</button>
   </div>

   <div id="bedrock-openllmetry-install" class="tab-content active">

**Best for**: Production deployments, cost tracking, enhanced LLM observability

.. code-block:: bash

   # Recommended: Install with Traceloop AWS Bedrock integration
   pip install honeyhive[traceloop-bedrock]
   
   # Alternative: Manual installation
   pip install honeyhive opentelemetry-instrumentation-bedrock boto3>=1.26.0

.. raw:: html

   </div>
   <div id="bedrock-openllmetry-basic" class="tab-content">

.. code-block:: python

   from honeyhive import HoneyHiveTracer
   from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
   import boto3
   import os

   # Environment variables (recommended for production)
   # .env file:
   # HH_API_KEY=your-honeyhive-key
   # AWS_ACCESS_KEY_ID=your-bedrock-key

   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )  # Uses HH_API_KEY from environment
   
   # Step 2: Initialize Traceloop instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

   # Basic usage with automatic tracing
   try:
       import boto3
       
       # Create Bedrock client
       bedrock = boto3.client(
           "bedrock-runtime",
           region_name="us-east-1"
       )
       
       # Invoke model
       response = bedrock.invoke_model(
           modelId="anthropic.claude-3-sonnet-20240229-v1:0",
           body=json.dumps({
               "anthropic_version": "bedrock-2023-05-31",
               "max_tokens": 1000,
               "messages": [{"role": "user", "content": "Hello from Bedrock!"}]
           })
       )
       # Automatically traced by Traceloop with enhanced metrics! ✨
   except botocore.exceptions.ClientError as e:
       print(f"AWS Bedrock API error: {e}")
   except Exception as e:
       print(f"Unexpected error: {e}")

.. raw:: html

   </div>
   <div id="bedrock-openllmetry-advanced" class="tab-content">

.. code-block:: python

   from honeyhive import HoneyHiveTracer, trace, enrich_span
   from honeyhive.models import EventType
   from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
   import boto3

   # Initialize HoneyHive with Traceloop instrumentor
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       api_key="your-honeyhive-key",  # Or set HH_API_KEY environment variable
       project="your-project",        # Or set HH_PROJECT environment variable
       source="production"            # Or set HH_SOURCE environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

   @trace(tracer=tracer, event_type=EventType.chain)
   def multi_model_bedrock_workflow(prompts: List[str]) -> dict:
       """Advanced example with business context and enhanced LLM metrics."""
       import boto3
       import json
       
       # Configure AWS Bedrock
       bedrock = boto3.client(
           "bedrock-runtime",
           region_name=os.getenv("AWS_REGION", "us-east-1")
       )
       
       # Add business context to the trace
       enrich_span({
           "business.input_type": type(prompts).__name__,
           "business.use_case": "multi_model_analysis",
           "bedrock.strategy": "cost_optimized_bedrock_model_comparison",
           "instrumentor.type": "openllmetry",
           "observability.enhanced": True
       })
       
       try:
           # Test multiple Bedrock models
       models = [
           "anthropic.claude-3-sonnet-20240229-v1:0",
           "anthropic.claude-3-haiku-20240307-v1:0",
           "amazon.titan-text-express-v1"
       ]
       
       results = []
       for prompt in prompts:
           model_results = {}
           
           for model_id in models:
               try:
                   # Prepare request based on model type
                   if "anthropic" in model_id:
                       body = {
                           "anthropic_version": "bedrock-2023-05-31",
                           "max_tokens": 1000,
                           "messages": [{"role": "user", "content": prompt}]
                       }
                   elif "titan" in model_id:
                       body = {
                           "inputText": prompt,
                           "textGenerationConfig": {
                               "maxTokenCount": 1000,
                               "temperature": 0.7
                           }
                       }
                   
                   # Invoke model
                   response = bedrock.invoke_model(
                       modelId=model_id,
                       body=json.dumps(body)
                   )
                   
                   response_body = json.loads(response["body"].read())
                   model_results[model_id] = response_body
                   
               except Exception as e:
                   model_results[model_id] = {"error": str(e)}
           
           results.append({
               "prompt": prompt,
               "model_responses": model_results
           })
           
           # Add result metadata
           enrich_span({
               "business.successful": True,
               "bedrock.models_used": ["claude-3-sonnet", "claude-3-haiku", "titan-text"],
               "business.result_confidence": "high",
               "openllmetry.cost_tracking": "enabled",
               "openllmetry.token_metrics": "captured"
           })
           
           return {{RETURN_VALUE}}
           
       except botocore.exceptions.ClientError as e:
           enrich_span({
               "error.type": "api_error", 
               "error.message": str(e),
               "instrumentor.error_handling": "openllmetry"
           })
           raise

.. raw:: html

   </div>
   <div id="bedrock-openllmetry-troubleshoot" class="tab-content">

**Common Traceloop Issues**:

1. **Missing Traces**
   
   .. code-block:: python
   
      # Ensure Traceloop instrumentor is passed to tracer
      from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
      
      # Step 1: Initialize HoneyHive tracer first (without instrumentors)
      tracer = HoneyHiveTracer.init(
          project="your-project"  # Or set HH_PROJECT environment variable
      )
      
      # Step 2: Initialize instrumentor separately with tracer_provider
      instrumentor = BedrockInstrumentor()
      instrumentor.instrument(tracer_provider=tracer.provider)

2. **Enhanced Metrics Not Showing**
   
   .. code-block:: python
   
      # Ensure you're using the latest version
      # pip install --upgrade opentelemetry-instrumentation-bedrock
      
      # The instrumentor automatically captures enhanced metrics
      from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
      # Step 1: Initialize HoneyHive tracer first (without instrumentors)
      tracer = HoneyHiveTracer.init(
          project="your-project"  # Or set HH_PROJECT environment variable
      )
      
      # Step 2: Initialize instrumentor separately with tracer_provider
      instrumentor = BedrockInstrumentor()
      instrumentor.instrument(tracer_provider=tracer.provider)

3. **Multiple Traceloop Instrumentors**
   
   .. code-block:: python
   
      # You can combine multiple Traceloop instrumentors
      from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
       from opentelemetry.instrumentation.openai import OpenAIInstrumentor
       
       # Step 1: Initialize HoneyHive tracer first (without instrumentors)
       tracer = HoneyHiveTracer.init(
           project="your-project"  # Or set HH_PROJECT environment variable
       )
       
       # Step 2: Initialize instrumentors separately with tracer_provider
       # REPLACE_WITH_INSTRUMENTOR_SETUP
               BedrockInstrumentor(),       # Traceloop Bedrock
               OpenAIInstrumentor()         # Traceloop OpenAI
           ]
       )

4. **Performance Optimization**
   
   .. code-block:: python
   
      # Traceloop instrumentors handle batching automatically
      # No additional configuration needed for performance

5. **Environment Configuration**
   
   .. code-block:: bash
   
      # HoneyHive configuration
      export HH_API_KEY="your-honeyhive-api-key"
      export HH_SOURCE="production"
      
      # AWS Bedrock configuration
      export AWS_ACCESS_KEY_ID="your-aws-access-key"
      export AWS_SECRET_ACCESS_KEY="your-aws-secret-key"
      export AWS_DEFAULT_REGION="us-east-1"
      
      # Optional: Traceloop cloud features
      export TRACELOOP_API_KEY="your-traceloop-key"
      export TRACELOOP_BASE_URL="https://api.traceloop.com"

.. raw:: html

   </div>
   </div>

.. raw:: html

   </div>
   </div>

Comparison: OpenInference vs Traceloop for AWS Bedrock
------------------------------------------------------

.. list-table:: Feature Comparison
   :header-rows: 1
   :widths: 30 35 35

   * - Feature
     - OpenInference
     - Traceloop
   * - **Setup Complexity**
     - Simple, single instrumentor
     - Single instrumentor setup
   * - **Token Tracking**
     - Basic span attributes
     - Detailed token metrics + costs
   * - **Model Metrics**
     - Model name, basic timing
     - Cost per model, latency analysis
   * - **Performance**
     - Lightweight, fast
     - Optimized with smart batching
   * - **Cost Analysis**
     - Manual calculation needed
     - Automatic cost per request
   * - **Production Ready**
     - ✅ Yes
     - ✅ Yes, with cost insights
   * - **Debugging**
     - Standard OpenTelemetry
     - Enhanced LLM-specific debug
   * - **Best For**
     - Simple integrations, dev
     - Production, cost optimization

Migration Between Instrumentors
-------------------------------

**From OpenInference to Traceloop**:

.. code-block:: python

   # Before (OpenInference)
   from openinference.instrumentation.bedrock import BedrockInstrumentor
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)
   
   # After (Traceloop) - different instrumentor package
   from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

**From Traceloop to OpenInference**:

.. code-block:: python

   # Before (Traceloop)
   from opentelemetry.instrumentation.bedrock import BedrockInstrumentor
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)
   
   # After (OpenInference)
   from openinference.instrumentation.bedrock import BedrockInstrumentor
   # Step 1: Initialize HoneyHive tracer first (without instrumentors)
   tracer = HoneyHiveTracer.init(
       project="your-project"  # Or set HH_PROJECT environment variable
   )
   
   # Step 2: Initialize instrumentor separately with tracer_provider
   instrumentor = BedrockInstrumentor()
   instrumentor.instrument(tracer_provider=tracer.provider)

See Also
--------

- :doc:`multi-provider` - Use Bedrock with other providers
- :doc:`../llm-application-patterns` - Common integration patterns
- :doc:`../../tutorials/02-add-llm-tracing-5min` - LLM integration tutorial
- :doc:`anthropic` - Similar integration for Anthropic Claude

.. raw:: html

   <script>
   function showTab(evt, tabName) {
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   function showInstrumentor(evt, instrumentorName) {
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     }
     instrumentorLinks = document.getElementsByClassName("instrumentor-button");
     for (i = 0; i < instrumentorLinks.length; i++) {
       instrumentorLinks[i].classList.remove("active");
     }
     document.getElementById(instrumentorName).classList.add("active");
     evt.currentTarget.classList.add("active");
   }
   </script>
   
   <style>
   .instrumentor-selector {
     margin: 2rem 0;
     border: 2px solid #2980b9;
     border-radius: 12px;
     overflow: hidden;
     box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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   .instrumentor-tabs {
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     background: none;
     border: none;
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     background: rgba(255, 255, 255, 0.2);
     border-bottom: 3px solid #f39c12;
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   .instrumentor-content {
     display: none;
     padding: 1.5rem;
     background: #f8f9fa;
   }
   .instrumentor-content.active {
     display: block;
   }
   .code-example {
     margin: 1.5rem 0;
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   }
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     display: block;
   }
   .tab-content .highlight {
     margin: 0;
     border-radius: 0;
   }
   </style>