Harrison/jina

docs/ecosystem/jina.md

@@ -0,0 +1,18 @@
+# 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

@@ -0,0 +1,101 @@
+{
+ "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
+}

langchain/embeddings/__init__.py

@@ -9,6 +9,7 @@
     HuggingFaceInstructEmbeddings,
 )
 from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
+from langchain.embeddings.jina import JinaEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
 from langchain.embeddings.self_hosted import SelfHostedEmbeddings
@@ -24,6 +25,7 @@
     "OpenAIEmbeddings",
     "HuggingFaceEmbeddings",
     "CohereEmbeddings",
+    "JinaEmbeddings",
     "HuggingFaceHubEmbeddings",
     "TensorflowHubEmbeddings",
     "SagemakerEndpointEmbeddings",

langchain/embeddings/jina.py

@@ -0,0 +1,98 @@
+"""Wrapper around Jina embedding models."""
+
+import os
+from typing import Any, Dict, List, Optional
+
+import requests
+from pydantic import BaseModel, root_validator
+
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_dict_or_env
+
+
+class JinaEmbeddings(BaseModel, Embeddings):
+    client: Any  #: :meta private:
+
+    model_name: str = "ViT-B-32::openai"
+    """Model name to use."""
+
+    jina_auth_token: Optional[str] = None
+    jina_api_url: str = "https://api.clip.jina.ai/api/v1/models/"
+    request_headers: Optional[dict] = None
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that auth token exists in environment."""
+        # Set Auth
+        jina_auth_token = get_from_dict_or_env(
+            values, "jina_auth_token", "JINA_AUTH_TOKEN"
+        )
+        values["jina_auth_token"] = jina_auth_token
+        values["request_headers"] = (("authorization", jina_auth_token),)
+
+        # Test that package is installed
+        try:
+            import jina
+        except ImportError:
+            raise ValueError(
+                "Could not import `jina` python package. "
+                "Please it install it with `pip install jina`."
+            )
+
+        # Setup client
+        jina_api_url = os.environ.get("JINA_API_URL", values["jina_api_url"])
+        model_name = values["model_name"]
+        try:
+            resp = requests.get(
+                jina_api_url + f"?model_name={model_name}",
+                headers={"Authorization": jina_auth_token},
+            )
+
+            if resp.status_code == 401:
+                raise ValueError(
+                    "The given Jina auth token is invalid. "
+                    "Please check your Jina auth token."
+                )
+            elif resp.status_code == 404:
+                raise ValueError(
+                    f"The given model name `{model_name}` is not valid. "
+                    f"Please go to https://cloud.jina.ai/user/inference "
+                    f"and create a model with the given model name."
+                )
+            resp.raise_for_status()
+
+            endpoint = resp.json()["endpoints"]["grpc"]
+            values["client"] = jina.Client(host=endpoint)
+        except requests.exceptions.HTTPError as err:
+            raise ValueError(f"Error: {err!r}")
+        return values
+
+    def _post(self, docs: List[Any], **kwargs: Any) -> Any:
+        payload = dict(inputs=docs, metadata=self.request_headers, **kwargs)
+        return self.client.post(on="/encode", **payload)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to Jina's embedding endpoint.
+        Args:
+            texts: The list of texts to embed.
+        Returns:
+            List of embeddings, one for each text.
+        """
+        from docarray import Document, DocumentArray
+
+        embeddings = self._post(
+            docs=DocumentArray([Document(text=t) for t in texts])
+        ).embeddings
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to Jina's embedding endpoint.
+        Args:
+            text: The text to embed.
+        Returns:
+            Embeddings for the text.
+        """
+        from docarray import Document, DocumentArray
+
+        embedding = self._post(docs=DocumentArray([Document(text=text)])).embeddings[0]
+        return list(map(float, embedding))