Harrison/jina (#2043)

Co-authored-by: numb3r3 <wangfelix87@gmail.com>
Co-authored-by: felix-wang <35718120+numb3r3@users.noreply.github.com>

docs/ecosystem/jina.md

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+# Jina
+
+This page covers how to use the Jina ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install jina`
+- Get a Jina AI Cloud auth token from [here](https://cloud.jina.ai/settings/tokens) and set it as an environment variable (`JINA_AUTH_TOKEN`)
+
+## Wrappers
+
+### Embeddings
+
+There exists a Jina Embeddings wrapper, which you can access with 
+```python
+from langchain.embeddings import JinaEmbeddings
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)

docs/modules/models/text_embedding/examples/jina.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c0cf975",
+   "metadata": {},
+   "source": [
+    "# Jina\n",
+    "\n",
+    "Let's load the Jina Embedding class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d94c62b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import JinaEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "523a09e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = JinaEmbeddings(jina_auth_token=jina_auth_token, model_name=\"ViT-B-32::openai\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b212bd5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57db66bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b790fd09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f3607a0",
+   "metadata": {},
+   "source": [
+    "In the above example, `ViT-B-32::openai`, OpenAI's pretrained `ViT-B-32` model is used. For a full list of models, see [here](https://cloud.jina.ai/user/inference/model/63dca9df5a0da83009d519cd)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd5f148e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}