traceAI-haystack 0.1.8
OpenTelemetry instrumentation for Haystack
Requires Python: <3.13,>3.9

# 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**

```bash
pip install traceAI-haystack
```

### Set Environment Variables
Set up your environment variables to authenticate with FutureAGI

```python
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:

```python
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.

```python
from traceai_haystack import HaystackInstrumentor

HaystackInstrumentor().instrument(tracer_provider=trace_provider)
```

### Create Haystack Components

```python
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])
```