Adding Arize to the integrations page (#253)

* adding arize to the integrations page * resolving some comments * Fixes to the arize integration docs (#256) * add more links, fix spelling * fix spacing * add import * split readmes in two * docs: cleanup arize integration (#257) * docs: cleanup arize integration * clean up * add an image * final image --------- Co-authored-by: Xander Song <[email protected]> * remove this gif in favor of already existing one --------- Co-authored-by: Mikyo King <[email protected]> Co-authored-by: Xander Song <[email protected]>
deepset-ai · Aug 19, 2024 · 6720ca1 · 6720ca1
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diff --git a/integrations/arize-phoenix.md b/integrations/arize-phoenix.md
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+---
+layout: integration
+name: Arize Phoenix
+description: Trace your Haystack pipelines with Arize Phoenix
+authors:
+  - name: Arize AI
+    socials:
+      github: Arize-ai
+      twitter: ArizePhoenix
+      linkedin: arizeai
+pypi: https://pypi.org/project/openinference-instrumentation-haystack/
+repo: https://github.com/Arize-ai/phoenix
+type: Monitoring Tool
+report_issue: https://github.com/Arize-ai/openinference/issues
+logo: /logos/arize-phoenix.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Resources](#resources)
+
+## Overview
+
+**Arize Phoenix** is Arize's open-source platform that offers developers the quickest way to troubleshoot, evaluate, and experiment with LLM applications.
+
+For a detailed integration guide, see the [documentation for Phoenix + Haystack](https://docs.arize.com/phoenix/tracing/integrations-tracing/haystack)
+
+## Installation
+
+```bash
+pip install openinference-instrumentation-haystack haystack-ai opentelemetry-sdk opentelemetry-exporter-otlp arize-phoenix
+```
+
+## Usage
+
+To trace any Haystack pipeline with Phoenix, simply initialize OpenTelemetry and the `HaystackInstrumentor`. Haystack pipelines that run within the same environment send traces to Phoenix.
+
+First, start a Phoenix instance to send traces to.
+
+```sh
+python -m phoenix.server.main serve
+```
+
+Now let's connect our Haystack pipeline to Phoenix using OpenTelemetry.
+
+```python
+from openinference.instrumentation.haystack import HaystackInstrumentor
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,
+)
+from opentelemetry.sdk import trace as trace_sdk
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+
+endpoint = "http://localhost:6006/v1/traces" # The URL to your Phoenix instance
+tracer_provider = trace_sdk.TracerProvider()
+tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
+
+HaystackInstrumentor().instrument(tracer_provider=tracer_provider)
+```
+
+Now, you can run a Haystack pipeline within the same environment, resulting in the following trace:
+
+> To run the example below, export your OpenAI Key to the `OPENAI_API_KEY` environment variable.
+
+![Arize Phoenix Demo](https://raw.githubusercontent.com/deepset-ai/haystack-integrations/main/images/arize-demo.gif)
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+document_store = InMemoryDocumentStore()
+document_store.write_documents([
+    Document(content="My name is Jean and I live in Paris."),
+    Document(content="My name is Mark and I live in Berlin."),
+    Document(content="My name is Giorgio and I live in Rome.")
+])
+
+prompt_template = """
+Given these documents, answer the question.
+Documents:
+{% for doc in documents %}
+    {{ doc.content }}
+{% endfor %}
+Question: {{question}}
+Answer:
+"""
+
+retriever = InMemoryBM25Retriever(document_store=document_store)
+prompt_builder = PromptBuilder(template=prompt_template)
+llm = OpenAIGenerator()
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component("retriever", retriever)
+rag_pipeline.add_component("prompt_builder", prompt_builder)
+rag_pipeline.add_component("llm", llm)
+rag_pipeline.connect("retriever", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "llm")
+
+question = "Who lives in Paris?"
+results = rag_pipeline.run(
+    {
+        "retriever": {"query": question},
+        "prompt_builder": {"question": question},
+    }
+)
+```
+
+## Resources
+
+- Check out the Phoenix [github.com/Arize-ai/phoenix](GitHub repository)
+- For an in-depth guide on how to host your own Phoenix instance, see the [Phoenix documentation](https://docs.arize.com/phoenix/deployment)
+- Try out free hosted Phoenix instances at [phoenix.arize.com](https://phoenix.arize.com/)
+- Check out the [Phoenix documentation](https://docs.arize.com/phoenix)
diff --git a/integrations/arize.md b/integrations/arize.md
@@ -0,0 +1,104 @@
+---
+layout: integration
+name: Arize AI
+description: Trace and Monitor your Haystack pipelines with Arize AI
+authors:
+  - name: Arize AI
+    socials:
+      github: Arize-ai
+      twitter: arizeai
+      linkedin: arizeai
+pypi: https://pypi.org/project/openinference-instrumentation-haystack/
+repo: https://github.com/Arize-ai/openinference
+type: Monitoring Tool
+report_issue: https://github.com/Arize-ai/openinference/issues
+logo: /logos/arize.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+
+## Overview
+
+Arize is AI Observability and Evaluation platform designed to help you troubleshoot, evaluate, and experiment on LLM and ML applications. Developers use Arize to get applications working quickly, evaluate performance, detect and prevent production issues, and curate datasets.
+
+- [Documentation for Arize AI + Haystack](https://docs.arize.com/arize/large-language-models/tracing/auto-instrumentation/haystack)
+
+## Installation
+
+```bash
+pip install openinference-instrumentation-haystack haystack-ai arize-otel opentelemetry-sdk opentelemetry-exporter-otlp
+```
+
+## Usage
+
+To trace any Haystack pipeline with Arize, simply initialize OpenTelemetry and the `HaystackInstrumentor`. Haystack pipelines that run within the same environment send traces to Arize.
+
+```python
+from openinference.instrumentation.haystack import HaystackInstrumentor
+# Import open-telemetry dependencies
+from arize_otel import register_otel, Endpoints
+
+# Setup OTEL via our convenience function
+register_otel(
+    endpoints = Endpoints.ARIZE,
+    space_id = "<your-space-id>", # from the space settings page
+    api_key = "<your-api-key>", # from the space settings page
+    model_id = "<your-haystack-app-name>", # name this to whatever you would like
+)
+```
+
+Now, you can run a Haystack pipeline within the same environment, resulting in the following trace:
+
+> To run the example below, export your OpenAI Key to the `OPENAI_API_KEY` environment variable.
+
+![Arize Demo](https://raw.githubusercontent.com/deepset-ai/haystack-integrations/main/images/arize-demo.gif)
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+document_store = InMemoryDocumentStore()
+document_store.write_documents([
+    Document(content="My name is Jean and I live in Paris."),
+    Document(content="My name is Mark and I live in Berlin."),
+    Document(content="My name is Giorgio and I live in Rome.")
+])
+
+prompt_template = """
+Given these documents, answer the question.
+Documents:
+{% for doc in documents %}
+    {{ doc.content }}
+{% endfor %}
+Question: {{question}}
+Answer:
+"""
+
+retriever = InMemoryBM25Retriever(document_store=document_store)
+prompt_builder = PromptBuilder(template=prompt_template)
+llm = OpenAIGenerator()
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component("retriever", retriever)
+rag_pipeline.add_component("prompt_builder", prompt_builder)
+rag_pipeline.add_component("llm", llm)
+rag_pipeline.connect("retriever", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "llm")
+
+question = "Who lives in Paris?"
+results = rag_pipeline.run(
+    {
+        "retriever": {"query": question},
+        "prompt_builder": {"question": question},
+    }
+)
+```
diff --git a/logos/arize-phoenix.png b/logos/arize-phoenix.png
diff --git a/logos/arize.png b/logos/arize.png