> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Haystack

## 1. Installation

Install the traceAI and Haystack packages.

```bash theme={null}
pip install traceAI-haystack haystack-ai trafilatura
```

***

## 2. Set Environment Variables

Set up your environment variables to authenticate with FutureAGI.

```python theme={null}
import os

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"
```

***

## 3. Initialize Trace Provider

Set up the trace provider to create a new project in FutureAGI, establish telemetry data pipelines .

```python theme={null}
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

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

***

## 4. Instrument your Project

Initialize the Haystack instrumentor to enable automatic tracing.

```python theme={null}
from traceai_haystack import HaystackInstrumentor

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

***

## 5. Create Haystack Components

Set up your Haystack components as you normally would. Our Instrumentor will automatically trace and send the telemetry data to our platform.

```python theme={null}

from haystack import Pipeline
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_template = [
    ChatMessage.from_user(
      """
      According to the contents of this website:
      {% for document in documents %}
        {{document.content}}
      {% endfor %}
      Answer the given question: {{query}}
      Answer:
      """
    )
]

prompt_builder = ChatPromptBuilder(template=prompt_template)
llm = OpenAIChatGenerator()

pipeline = Pipeline()
pipeline.add_component("fetcher", fetcher)
pipeline.add_component("converter", converter)
pipeline.add_component("prompt", prompt_builder)
pipeline.add_component("llm", llm)

pipeline.connect("fetcher.streams", "converter.sources")
pipeline.connect("converter.documents", "prompt.documents")
pipeline.connect("prompt.prompt", "llm")

result = pipeline.run({"fetcher": {"urls": ["https://haystack.deepset.ai/overview/quick-start"]},
              "prompt": {"query": "Which components do I need for a RAG pipeline?"}})

print(result["llm"]["replies"][0].text)
```