traceAI-haystack 0.1.9

OpenTelemetry instrumentation for Haystack

Haystack OpenTelemetry Integration

Overview

This integration provides support for using OpenTelemetry with the Haystack framework. It enables tracing and monitoring of applications built with Haystack.

Installation

1. Install traceAI Haystack

pip install traceAI-haystack

Set Environment Variables

Set up your environment variables to authenticate with FutureAGI

import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY

Quickstart

Register Tracer Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="haystack_app"
)

Configure Haystack Instrumentation

Instrument the Haystack client to enable telemetry collection. This step ensures that all interactions with the Haystack SDK are tracked and monitored.

from traceai_haystack import HaystackInstrumentor

HaystackInstrumentor().instrument(tracer_provider=trace_provider)

Create Haystack Components

from haystack import Document, Pipeline
from haystack.components.builders import PromptBuilder
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.components.generators import OpenAIGenerator
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from datasets import load_dataset

document_store = InMemoryDocumentStore()

dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]

doc_embedder = SentenceTransformersDocumentEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2"
)
doc_embedder.warm_up()

docs_with_embeddings = doc_embedder.run(docs)
document_store.write_documents(docs_with_embeddings["documents"])

text_embedder = SentenceTransformersTextEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2"
)

retriever = InMemoryEmbeddingRetriever(document_store)

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{question}}
Answer:
"""

prompt_builder = PromptBuilder(template=template)

generator = OpenAIGenerator(model="gpt-3.5-turbo")

basic_rag_pipeline = Pipeline()
basic_rag_pipeline.add_component("text_embedder", text_embedder)
basic_rag_pipeline.add_component("retriever", retriever)
basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
basic_rag_pipeline.add_component("llm", generator)

basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
basic_rag_pipeline.connect("prompt_builder", "llm")

question = "What does Rhodes Statue look like?"

response = basic_rag_pipeline.run(
    {"text_embedder": {"text": question}, "prompt_builder": {"question": question}}
)

print(response["llm"]["replies"][0])