LangWatch does not have a built-in auto-tracking integration for Haystack from deepset.ai. However, you can leverage community-provided OpenTelemetry instrumentors to integrate your Haystack pipelines with LangWatch.

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Integrating Community Instrumentors with LangWatch

Community-provided OpenTelemetry instrumentors for Haystack allow you to automatically capture detailed trace data from your Haystack pipeline components (Nodes, Pipelines, etc.). LangWatch can seamlessly integrate with these instrumentors.

There are two main ways to integrate these:

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1. Via langwatch.setup()

You can pass an instance of the Haystack instrumentor to the instrumentors list in the langwatch.setup() call. LangWatch will then manage the lifecycle of this instrumentor.

import langwatch
import os
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PromptNode, PromptTemplate
from haystack.pipelines import Pipeline
from openinference.instrumentation.haystack import HaystackInstrumentor # Assuming this is the correct import

langwatch.setup(
    instrumentors=[HaystackInstrumentor()]
)

# Initialize Haystack components
document_store = InMemoryDocumentStore(use_bm25=True)
# You can add documents to the store here if needed

prompt_template = PromptTemplate(
    prompt="Answer the question based on the context: {query}",
    output_parser=None # Or specify an output parser
)
prompt_node = PromptNode(
    model_name_or_path="gpt-4o-mini", # Replace with your desired model
    api_key=os.environ.get("OPENAI_API_KEY"),
    default_prompt_template=prompt_template
)

pipeline = Pipeline()
pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Query"])

@langwatch.trace(name="Haystack Pipeline with OpenInference (Setup)")
def run_haystack_pipeline_oi_setup(query: str):
    # The OpenInference instrumentor, when configured via langwatch.setup(),
    # should automatically capture Haystack operations.
    result = pipeline.run(query=query)
    return result

if __name__ == "__main__":
    if not os.environ.get("OPENAI_API_KEY"):
        print("Please set the OPENAI_API_KEY environment variable.")
    else:
        user_query = "What is the capital of France?"
        print(f"Running Haystack pipeline with OpenInference (setup) for query: {user_query}")
        output = run_haystack_pipeline_oi_setup(user_query)
        print("\n\nHaystack Pipeline Output:")
        print(output)

Ensure you have the respective community instrumentation library installed:

  • For OpenInference: pip install openinference-instrumentation-haystack
  • For OpenLLMetry: pip install opentelemetry-instrumentation-haystack You’ll also need Haystack: pip install farm-haystack[openai] (if using OpenAI models). Consult the specific library’s documentation for the exact package name and instrumentor class if the above assumptions are incorrect.

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2. Direct Instrumentation

If you have an existing OpenTelemetry TracerProvider configured in your application (or if LangWatch is configured to use the global provider), you can use the community instrumentor’s instrument() method directly. LangWatch will automatically pick up the spans generated by these instrumentors as long as its exporter is part of the active TracerProvider.

import langwatch
import os
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PromptNode, PromptTemplate
from haystack.pipelines import Pipeline
from openinference.instrumentation.haystack import HaystackInstrumentor # Assuming this is the correct import

langwatch.setup()

# Instrument Haystack directly using OpenInference
HaystackInstrumentor().instrument()

document_store = InMemoryDocumentStore(use_bm25=True)
prompt_template = PromptTemplate(prompt="Summarize this for a 5-year old: {query}")
prompt_node = PromptNode(
    model_name_or_path="gpt-4o-mini",
    api_key=os.environ.get("OPENAI_API_KEY"),
    default_prompt_template=prompt_template
)
pipeline = Pipeline()
pipeline.add_node(component=prompt_node, name="SummarizerNode", inputs=["Query"])

@langwatch.trace(name="Haystack Pipeline with OpenInference (Direct)")
def run_haystack_pipeline_oi_direct(query: str):
    # Spans from Haystack operations should be captured by the directly configured instrumentor.
    result = pipeline.run(query=query)
    return result

if __name__ == "__main__":
    if not os.environ.get("OPENAI_API_KEY"):
        print("Please set the OPENAI_API_KEY environment variable.")
    else:
        user_query = "The quick brown fox jumps over the lazy dog."
        print(f"Running Haystack pipeline with OpenInference (direct) for query: {user_query}")
        output = run_haystack_pipeline_oi_direct(user_query)
        print("\n\nHaystack Pipeline Output:")
        print(output)

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Key points for community instrumentors:

  • These instrumentors typically patch Haystack at a global level or integrate deeply with its execution flow (e.g., via BaseComponent or Pipeline hooks), meaning most Haystack operations should be captured once instrumented.
  • If using langwatch.setup(instrumentors=[...]), LangWatch handles the setup and lifecycle of the instrumentor.
  • If instrumenting directly (e.g., HaystackInstrumentor().instrument()), ensure that the TracerProvider used by the instrumentor is the same one LangWatch is exporting from. This usually means LangWatch is configured to use an existing global provider or one you explicitly pass to langwatch.setup().
  • Always refer to the specific documentation of the community instrumentor (OpenInference or OpenLLMetry) for the most accurate and up-to-date installation and usage instructions, including the correct class names for instrumentors and any specific setup requirements for different Haystack versions or components.
  • The examples use a PromptNode with an OpenAI model for simplicity. Ensure you have the necessary API keys (e.g., OPENAI_API_KEY) set in your environment if you run these examples.