OPEA integration (#328)

* OPEA integration

* PyPI package

* More info on OPEA backends

Author: danielfleischer

integrations/opea.md

@@ -0,0 +1,91 @@
+---
+layout: integration
+name: OPEA
+description: Use the OPEA framework for hardware abstraction and orchestration
+authors:
+  - name: OPEA-Project
+    socials:
+      github: opea-project
+pypi: https://pypi.org/project/haystack-opea/
+repo: https://github.com/opea-project/Haystack-OPEA
+type: Distributed Computing
+report_issue: https://github.com/opea-project/Haystack-OPEA/issues
+logo: /logos/opea.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Embeddings](#embeddings)
+  - [LLM Generation](#llm-generation)
+
+## Overview
+
+The `haystack-opea` integration connects Haystack to [OPEA](https://opea.dev/)—a collection of containerized microservices for LLMs, embedding, retrieval and reranking. By delegating heavy compute to OPEA services, you can build flexible Retrieval-Augmented Generation (RAG) pipelines that scale across cloud, on-prem and edge deployments.  
+
+Key features:  
+- Hardware-agnostic LLM & embedding services.
+- Easy orchestration of LLM, embedder, retriever, ranker, among others.
+- Support for local development via Docker Compose or production clusters.
+
+## Installation
+
+Install from source:
+
+```bash
+git clone https://github.com/opea-project/Haystack-OPEA.git
+cd Haystack-OPEA
+pip install poetry
+poetry install --with test
+```
+
+## Usage
+
+Below are quickstart examples for embeddings and LLM generation. Make sure your OPEA backend is running: e.g. via the provided [Docker Compose](https://github.com/opea-project/Haystack-OPEA/blob/main/samples/compose.yaml) file. OPEA services can be configured to use a variety of model serving backends like TGI, vLLM, ollama, OVMS... and offer validated runtime settings for good performance on various hardware's including Intel Gaudi, see the [LLM](https://github.com/opea-project/GenAIComps/tree/main/comps/llms/src/text-generation) section in the OPEA components library.
+
+### Embeddings
+
+```python
+from haystack import Document
+from haystack_opea import OPEATextEmbedder, OPEADocumentEmbedder
+
+# Text embedding example
+text_embedder = OPEATextEmbedder(api_url="http://localhost:6006")
+text_embedder.warm_up()
+result = text_embedder.run("I love pizza!")
+print("Text embedding:", result["vectors"][0])
+
+# Document embedding example
+doc = Document(content="I love pizza!")
+doc_embedder = OPEADocumentEmbedder(api_url="http://localhost:6006")
+doc_embedder.warm_up()
+out = doc_embedder.run([doc])
+print("Document embedding:", out["documents"][0].embedding)
+```
+
+### LLM Generation
+
+```python
+from haystack_opea import OPEAGenerator
+
+# Initialize the OPEA LLM service
+generator = OPEAGenerator(
+    api_url="http://localhost:9009",
+    model_arguments={
+        "temperature": 0.2,
+        "top_p": 0.7,
+        "max_tokens": 512,
+    },
+)
+generator.warm_up()
+
+# Run a simple prompt
+response = generator.run(prompt="What is the capital of France?")
+print("LLM reply:", response["replies"][0])
+```
+
+For more examples, see the `samples/` folder and the [official OPEA documentation](https://opea.dev/), as well as the [Components Library](https://github.com/opea-project/GenAIComps).