adapt Jina Embeddings to new Jina AI Embedding API (#13658)

- **Description:** Adapt JinaEmbeddings to run with the new Jina AI
Embedding platform
- **Twitter handle:** https://twitter.com/JinaAI_

---------

Co-authored-by: Joan Fontanals Martinez <joan.fontanals.martinez@jina.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/docs/integrations/providers/jina.mdx

@@ -1,75 +1,20 @@
 # Jina
 
-This page covers how to use the Jina ecosystem within LangChain.
+This page covers how to use the Jina Embeddings 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
+- Get a Jina AI API token from [here](https://jina.ai/embeddings/) and set it as an environment variable (`JINA_API_TOKEN`)
 
 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](/docs/integrations/text_embedding/jina)
 
-## Deployment
-
-[Langchain-serve](https://github.com/jina-ai/langchain-serve), powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots. 
-
-### Usage
-
-Install the package from PyPI. 
-
-```bash
-pip install langchain-serve
+# you can pas jina_api_key, if none is passed it will be taken from `JINA_API_TOKEN` environment variable
+embeddings = JinaEmbeddings(jina_api_key='jina_**', model_name='jina-embeddings-v2-base-en')
 ```
 
-Wrap your LangChain app with the `@serving` decorator. 
+You can check the list of available models from [here](https://jina.ai/embeddings/)
 
-```python
-# app.py
-from lcserve import serving
-
-@serving
-def ask(input: str) -> str:
-    from langchain.chains import LLMChain
-    from langchain.llms import OpenAI
-    from langchain.agents import AgentExecutor, ZeroShotAgent
-    
-    tools = [...] # list of tools
-    prompt = ZeroShotAgent.create_prompt(
-        tools, input_variables=["input", "agent_scratchpad"],
-    )
-    llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
-    agent = ZeroShotAgent(
-        llm_chain=llm_chain, allowed_tools=[tool.name for tool in tools]
-    )
-    agent_executor = AgentExecutor.from_agent_and_tools(
-        agent=agent, 
-        tools=tools, 
-        verbose=True,
-    )
-    return agent_executor.run(input)
-```
-
-Deploy on Jina AI Cloud with `lc-serve deploy jcloud app`. Once deployed, we can send a POST request to the API endpoint to get a response.
-
-```bash
-curl -X 'POST' 'https://<your-app>.wolf.jina.ai/ask' \
- -d '{
-  "input": "Your Question here?",
-  "envs": {
-     "OPENAI_API_KEY": "sk-***"
-  }
-}'
-```
-
-You can also self-host the app on your infrastructure with Docker-compose or Kubernetes. See [here](https://github.com/jina-ai/langchain-serve#-self-host-llm-apps-with-docker-compose-or-kubernetes) for more details.
-
-
-Langchain-serve also allows to deploy the apps with WebSocket APIs and Slack Bots both on [Jina AI Cloud](https://cloud.jina.ai/) or self-hosted infrastructure. 
+For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina.ipynb)

docs/docs/integrations/text_embedding/jina.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "d94c62b4",
    "metadata": {},
    "outputs": [],
@@ -28,7 +28,7 @@
    "outputs": [],
    "source": [
     "embeddings = JinaEmbeddings(\n",
-    "    jina_auth_token=jina_auth_token, model_name=\"ViT-B-32::openai\"\n",
+    "    jina_api_key=\"jina_*\", model_name=\"jina-embeddings-v2-base-en\"\n",
     ")"
    ]
   },
@@ -52,6 +52,16 @@
     "query_result = embeddings.embed_query(text)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aea3ca33-1e6e-499c-8284-b7e26f38c514",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(query_result)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -62,21 +72,15 @@
     "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",
+   "id": "c2e6b743-768c-4d7e-a331-27d5f0e8e30e",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "print(doc_result)"
+   ]
   }
  ],
  "metadata": {
@@ -95,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,