respan-exporter-haystack 1.0.1

Respan integration for Haystack pipelines with tracing and logging support

Keywords AI Haystack Integration

Monitor and optimize your Haystack pipelines with Keywords AI's LLM observability platform.

Features

Gateway Mode

Route LLM calls through Keywords AI gateway:

• Automatic logging (zero config)
• Model fallbacks & retries
• Load balancing
• Cost optimization
• Rate limiting & caching

Tracing Mode

Capture full workflow execution:

• Multi-component pipelines
• Parent-child span relationships
• Timing per component
• Input/output tracking
• RAG + Agent workflows

Combined Mode (Recommended)

Use both together for:

• Gateway reliability + Tracing visibility
• Production-ready monitoring

---

Installation

pip install keywordsai-exporter-haystack

Quick Start

1. Get API Keys

• Keywords AI API Key
• OpenAI API Key (for examples)

2. Set Environment Variables

export KEYWORDSAI_API_KEY="your-keywords-ai-key"
export OPENAI_API_KEY="your-openai-key"
export HAYSTACK_CONTENT_TRACING_ENABLED="true"  # For tracing mode

---

Usage Examples

Gateway Mode (Auto-Logging)

Just replace OpenAIGenerator with KeywordsAIGenerator:

import os
from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from keywordsai_exporter_haystack import KeywordsAIGenerator

# Create pipeline
pipeline = Pipeline()
pipeline.add_component("prompt", PromptBuilder(template="Tell me about {{topic}}."))
pipeline.add_component("llm", KeywordsAIGenerator(
    model="gpt-4o-mini",
    api_key=os.getenv("KEYWORDSAI_API_KEY")
))
pipeline.connect("prompt", "llm")

# Run
result = pipeline.run({"prompt": {"topic": "machine learning"}})
print(result["llm"]["replies"][0])

That's it! All LLM calls are automatically logged to Keywords AI with no additional code.

See: examples/gateway_example.py

---

Prompt Management

Use platform-managed prompts for centralized control:

import os
from haystack import Pipeline
from keywordsai_exporter_haystack import KeywordsAIGenerator

# Create prompt on platform: https://platform.keywordsai.co/platform/prompts
# Get your prompt_id from the platform

# Create pipeline with platform prompt (model config comes from platform)
pipeline = Pipeline()
pipeline.add_component("llm", KeywordsAIGenerator(
    prompt_id="1210b368ce2f4e5599d307bc591d9b7a",  # Your prompt ID
    api_key=os.getenv("KEYWORDSAI_API_KEY")
))

# Run with prompt variables
result = pipeline.run({
    "llm": {
        "prompt_variables": {
            "user_input": "The cat sat on the mat"
        }
    }
})

print("Response received successfully!")
print(f"Model: {result['llm']['meta'][0]['model']}")
print(f"Tokens: {result['llm']['meta'][0]['usage']['total_tokens']}")

Benefits:

• Update prompts without code changes
• Model config managed on platform (no hardcoding)
• Version control & rollback
• A/B testing
• Team collaboration

See: examples/prompt_example.py

---

Tracing Mode (Workflow Monitoring)

Add KeywordsAIConnector to capture the entire pipeline:

import os
from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from keywordsai_exporter_haystack import KeywordsAIConnector

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

# Create pipeline with tracing
pipeline = Pipeline()
pipeline.add_component("tracer", KeywordsAIConnector("My Workflow"))
pipeline.add_component("prompt", PromptBuilder(template="Tell me about {{topic}}."))
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini"))
pipeline.connect("prompt", "llm")

# Run
result = pipeline.run({"prompt": {"topic": "artificial intelligence"}})
print(result["llm"]["replies"][0])
print(f"\nTrace URL: {result['tracer']['trace_url']}")

Dashboard shows:

• Pipeline (root span)
• PromptBuilder (template processing)
• LLM (generation with tokens + cost)

See: examples/tracing_example.py

---

Combined Mode (Recommended for Production)

Use BOTH gateway + prompt + tracing for the full stack:

import os
from haystack import Pipeline
from keywordsai_exporter_haystack import KeywordsAIConnector, KeywordsAIGenerator

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

# Create pipeline with gateway, prompt management, and tracing
pipeline = Pipeline()
pipeline.add_component("tracer", KeywordsAIConnector("Full Stack: Gateway + Prompt + Tracing"))
pipeline.add_component("llm", KeywordsAIGenerator(
    prompt_id="1210b368ce2f4e5599d307bc591d9b7a",  # Platform-managed prompt
    api_key=os.getenv("KEYWORDSAI_API_KEY")
))

# Run with prompt variables
result = pipeline.run({
    "llm": {
        "prompt_variables": {
            "user_input": "She sells seashells by the seashore"
        }
    }
})

print("Response received successfully!")
print(f"Trace URL: {result['tracer']['trace_url']}")

You get:

1. Gateway routing with fallbacks, cost tracking, and reliability
2. Platform prompts managed centrally (no hardcoded prompts/models)
3. Full workflow trace with all components and timing

See: examples/combined_example.py

---

What Gets Logged

Gateway Mode

• Model used
• Prompt & completion
• Tokens & cost
• Latency
• Request metadata

Tracing Mode

Each span includes:

• Component name & type
• Input data
• Output data
• Timing (latency)
• Parent-child relationships

For LLM spans, additionally:

• Model name
• Token counts
• Calculated cost (auto-computed)

---

View Your Data

All logs and traces appear in your Keywords AI dashboard:

Dashboard: https://platform.keywordsai.co/logs

• Logs view: Individual LLM calls
• Traces view: Full pipeline workflows with tree visualization

---

API Reference

KeywordsAIGenerator

Gateway component for LLM calls.

KeywordsAIGenerator(
    model: Optional[str] = None,         # Model name (e.g., "gpt-4o-mini") - optional if using prompt_id
    api_key: Optional[str] = None,       # Keywords AI API key (defaults to KEYWORDSAI_API_KEY env var)
    base_url: Optional[str] = None,      # API base URL (defaults to https://api.keywordsai.co)
    prompt_id: Optional[str] = None,     # Platform prompt ID for prompt management
    generation_kwargs: Optional[Dict] = None
)

Replaces: OpenAIGenerator with gateway routing

Note: When using prompt_id, model config comes from the platform - no need to specify model

---

KeywordsAIConnector

Tracing component for workflow monitoring.

KeywordsAIConnector(
    name: str,                           # Pipeline name for dashboard
    api_key: Optional[str] = None,       # Keywords AI API key (defaults to KEYWORDSAI_API_KEY env var)
    base_url: Optional[str] = None,      # API base URL (defaults to https://api.keywordsai.co)
    metadata: Optional[Dict] = None      # Custom metadata for all spans
)

Returns: {"name": str, "trace_url": str}

Requires: HAYSTACK_CONTENT_TRACING_ENABLED=true environment variable

---

Examples

Run the examples:

# Set environment variables
export KEYWORDSAI_API_KEY="your-key"
export OPENAI_API_KEY="your-openai-key"
export HAYSTACK_CONTENT_TRACING_ENABLED="true"

# Gateway mode (auto-logging)
python examples/gateway_example.py

# Tracing mode (workflow monitoring)
python examples/tracing_example.py

# Prompt management (platform prompts)
python examples/prompt_example.py

# Combined mode (gateway + prompt + tracing)
python examples/combined_example.py

---

Requirements

• Python 3.9+
• haystack-ai >= 2.0.0
• requests >= 2.31.0

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Support

• Documentation: https://docs.keywordsai.co/
• Dashboard: https://platform.keywordsai.co/
• Issues: GitHub Issues

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License

MIT License - see LICENSE file for details.