docs: add jina integration documentation (#82)

integrations/jina.md

@@ -0,0 +1,65 @@
+---
+layout: integration
+name: Jina
+description: Use Jina embedding models with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/jina-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/jina.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Haystack 2.0](#haystack-20)
+  - [Installation](#installation)
+  - [Usage](#usage)
+
+## Haystack 2.0
+
+You can use [Jina embedding Models](https://jina.ai/embeddings) in your Haystack 2.0 pipelines with the Jina [Embedders](https://docs.haystack.deepset.ai/v2.0/docs/embedders).
+
+### Installation
+
+```bash
+pip install jina-haystack
+```
+
+### Usage
+
+You can use Jina Embedding models with two components: [JinaTextEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/jina/src/jina_haystack/text_embedder.py) and [JinaDocumentEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/jina/src/jina_haystack/document_embedder.py).
+
+To create semantic embeddings for documents, use `JinaDocumentEmbedder` in your indexing pipeline. For generating embeddings for queries, use `JinaTextEmbedder`. Once you've selected the suitable component for your specific use case, initialize the component with the model name and Jina API key. You can also
+set the environment variable JINA_API_KEY instead of passing the api key as an argument.
+
+Below is the example indexing pipeline with `InMemoryDocumentStore`, `JinaDocumentEmbedder` and  `DocumentWriter`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.components.writers import DocumentWriter
+from jina_haystack import JinaDocumentEmbedder
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [Document(content="I enjoy programming in Python"),
+             Document(content="My city does not get snow in winter"),
+             Document(content="Japanese diet is well known for being good for your health"),
+             Document(content="Thomas is injured and can't play sports")]
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component("embedder", JinaDocumentEmbedder(api_key="JINA_API_KEY", model_name="jina-embeddings-v2-base-en"))
+indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+indexing_pipeline.connect("embedder", "writer")
+
+indexing_pipeline.run({"embedder": {"documents": documents}})
+```
+