Javelin

docs/api_reference/guide_imports.json

@@ -465,6 +465,7 @@
         "PromptLayer": "https://python.langchain.com/docs/integrations/callbacks/promptlayer",
         "Log10": "https://python.langchain.com/docs/integrations/providers/log10",
         "MLflow AI Gateway": "https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway",
+        "Javelin AI Gateway": "https://python.langchain.com/docs/integrations/providers/javelin_ai_gateway",
         "Flyte": "https://python.langchain.com/docs/integrations/providers/flyte",
         "Arthur": "https://python.langchain.com/docs/integrations/providers/arthur_tracking",
         "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
@@ -1245,6 +1246,7 @@
         "Context": "https://python.langchain.com/docs/integrations/callbacks/context",
         "Label Studio": "https://python.langchain.com/docs/integrations/callbacks/labelstudio",
         "MLflow AI Gateway": "https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway",
+        "Javelin AI Gateway": "https://python.langchain.com/docs/integrations/providers/javelin_ai_gateway",
         "Chatbots": "https://python.langchain.com/docs/use_cases/chatbots",
         "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents",
         "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa",
@@ -1879,6 +1881,15 @@
     "ChatMLflowAIGateway": {
         "MLflow AI Gateway": "https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway"
     },
+    "JavelinAIGateway": {
+        "Javelin AI Gateway": "https://python.langchain.com/docs/integrations/providers/javelin_ai_gateway"
+    },
+    "JavelinAIGatewayEmbeddings": {
+        "Javelin AI Gateway": "https://python.langchain.com/docs/integrations/providers/javelin_ai_gateway"
+    },
+    "ChatJavelinAIGateway": {
+        "Javelin AI Gateway": "https://python.langchain.com/docs/integrations/providers/javelin_ai_gateway"
+    },
     "SingleStoreDB": {
         "SingleStoreDB": "https://python.langchain.com/docs/integrations/vectorstores/singlestoredb"
     },

docs/extras/integrations/llms/javelin.ipynb

@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62bacc68-1976-44eb-9316-d5baf54bf595",
+   "metadata": {},
+   "source": [
+    "# Javelin AI Gateway Tutorial\n",
+    "\n",
+    "This Jupyter Notebook will explore how to interact with the Javelin AI Gateway using the Python SDK. \n",
+    "The Javelin AI Gateway facilitates the utilization of large language models (LLMs) like OpenAI, Cohere, Anthropic, and others by \n",
+    "providing a secure and unified endpoint. The gateway itself provides a centralized mechanism to roll out models systematically, \n",
+    "provide access security, policy & cost guardrails for enterprises, etc., \n",
+    "\n",
+    "For a complete listing of all the features & benefits of Javelin, please visit www.getjavelin.io\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e52185f8-132b-4585-b73d-6fee928ac199",
+   "metadata": {},
+   "source": [
+    "## Step 1: Introduction\n",
+    "[The Javelin AI Gateway](https://www.getjavelin.io) is an enterprise-grade API Gateway for AI applications. It integrates robust access security, ensuring secure interactions with large language models. Learn more in the [official documentation](https://docs.getjavelin.io).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2acdb3-e3b8-422b-b077-7a0d63d18349",
+   "metadata": {},
+   "source": [
+    "## Step 2: Installation\n",
+    "Before we begin, we must install the `javelin_sdk` and set up the Javelin API key as an environment variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e91518a4-43ce-443e-b4c0-dbc652eb749f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: javelin_sdk in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (0.1.8)\n",
+      "Requirement already satisfied: httpx<0.25.0,>=0.24.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from javelin_sdk) (0.24.1)\n",
+      "Requirement already satisfied: pydantic<2.0.0,>=1.10.7 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from javelin_sdk) (1.10.12)\n",
+      "Requirement already satisfied: certifi in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpx<0.25.0,>=0.24.0->javelin_sdk) (2023.5.7)\n",
+      "Requirement already satisfied: httpcore<0.18.0,>=0.15.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpx<0.25.0,>=0.24.0->javelin_sdk) (0.17.3)\n",
+      "Requirement already satisfied: idna in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpx<0.25.0,>=0.24.0->javelin_sdk) (3.4)\n",
+      "Requirement already satisfied: sniffio in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpx<0.25.0,>=0.24.0->javelin_sdk) (1.3.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from pydantic<2.0.0,>=1.10.7->javelin_sdk) (4.7.1)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.0->javelin_sdk) (0.14.0)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<0.25.0,>=0.24.0->javelin_sdk) (3.7.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install 'javelin_sdk'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53b546dc-9ca3-4602-9a7b-d733d99e8e2f",
+   "metadata": {},
+   "source": [
+    "## Step 3: Completions Example\n",
+    "This section will demonstrate how to interact with the Javelin AI Gateway to get completions from a large language model. Here is a Python script that demonstrates this:\n",
+    "(note) assumes that you have setup a route in the gateway called 'eng_dept03'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d36949f0-5354-44ca-9a31-70c769344319",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'JavelinAIGateway' from 'langchain.llms' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/llms/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchains\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LLMChain\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m JavelinAIGateway\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprompts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PromptTemplate\n\u001b[1;32m      5\u001b[0m route_completions \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meng_dept03\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'JavelinAIGateway' from 'langchain.llms' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/llms/__init__.py)"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import JavelinAIGateway\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "route_completions = \"eng_dept03\"\n",
+    "\n",
+    "gateway = JavelinAIGateway(\n",
+    "    gateway_uri=\"http://localhost:8000\", # replace with service URL or host/port of Javelin\n",
+    "    route=route_completions,\n",
+    "    model_name=\"text-davinci-003\",\n",
+    ")\n",
+    "\n",
+    "prompt = PromptTemplate(\"Translate the following English text to French: {text}\")\n",
+    "\n",
+    "llmchain = LLMChain(llm=gateway, prompt=prompt)\n",
+    "result = llmchain.run(\"podcast player\")\n",
+    "\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b63fe93-2e77-4ea9-b8e7-dec2b96b8e95",
+   "metadata": {},
+   "source": [
+    "# Step 4: Embeddings Example\n",
+    "This section demonstrates how to use the Javelin AI Gateway to obtain embeddings for text queries and documents. Here is a Python script that illustrates this:\n",
+    "(note) assumes that you have setup a route in the gateway called 'embeddings'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "878e6c1d-be7f-49de-825c-43c266c8714e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'JavelinAIGatewayEmbeddings' from 'langchain.embeddings' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/embeddings/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01membeddings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m JavelinAIGatewayEmbeddings\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01membeddings\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopenai\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpenAIEmbeddings\n\u001b[1;32m      4\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m JavelinAIGatewayEmbeddings(\n\u001b[1;32m      5\u001b[0m     gateway_uri\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp://localhost:8000\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;66;03m# replace with service URL or host/port of Javelin\u001b[39;00m\n\u001b[1;32m      6\u001b[0m     route\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'JavelinAIGatewayEmbeddings' from 'langchain.embeddings' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/embeddings/__init__.py)"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.embeddings import JavelinAIGatewayEmbeddings\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "\n",
+    "embeddings = JavelinAIGatewayEmbeddings(\n",
+    "    gateway_uri=\"http://localhost:8000\", # replace with service URL or host/port of Javelin\n",
+    "    route=\"embeddings\",\n",
+    ")\n",
+    "\n",
+    "print(embeddings.embed_query(\"hello\"))\n",
+    "print(embeddings.embed_documents([\"hello\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c6691b-d333-4598-b2b7-c0933ed75937",
+   "metadata": {},
+   "source": [
+    "# Step 5: Chat Example\n",
+    "This section illustrates how to interact with the Javelin AI Gateway to facilitate a chat with a large language model. Here is a Python script that demonstrates this:\n",
+    "(note) assumes that you have setup a route in the gateway called 'mychatbot_route'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "653ef88c-36cd-4730-9c12-43c246b551f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'ChatJavelinAIGateway' from 'langchain.chat_models' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/chat_models/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchat_models\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChatJavelinAIGateway\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mschema\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage, SystemMessage\n\u001b[1;32m      4\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      5\u001b[0m     SystemMessage(\n\u001b[1;32m      6\u001b[0m         content\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a helpful assistant that translates English to French.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m     ),\n\u001b[1;32m     11\u001b[0m ]\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'ChatJavelinAIGateway' from 'langchain.chat_models' (/usr/local/Caskroom/miniconda/base/lib/python3.11/site-packages/langchain/chat_models/__init__.py)"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatJavelinAIGateway\n",
+    "from langchain.schema import HumanMessage, SystemMessage\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant that translates English to French.\"\n",
+    "    ),\n",
+    "    HumanMessage(\n",
+    "        content=\"Artificial Intelligence has the power to transform humanity and make the world a better place\"\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "chat = ChatJavelinAIGateway(\n",
+    "    gateway_uri=\"http://localhost:8000\", # replace with service URL or host/port of Javelin\n",
+    "    route=\"mychatbot_route\",\n",
+    "    model_name=\"gpt-3.5-turbo\",\n",
+    "    params={\n",
+    "        \"temperature\": 0.1\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "print(chat(messages))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6eb9cf33-6505-4e05-808b-645856463a8e",
+   "metadata": {},
+   "source": [
+    "Step 6: Conclusion\n",
+    "This tutorial introduced the Javelin AI Gateway and demonstrated how to interact with it using the Python SDK. \n",
+    "Remember to check the Javelin [Python SDK](https://www.github.com/getjavelin.io/javelin-python) for more examples and to explore the official documentation for additional details.\n",
+    "\n",
+    "Happy coding!"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/providers/javelin_ai_gateway.mdx

@@ -0,0 +1,92 @@
+# Javelin AI Gateway
+
+[The Javelin AI Gateway](https://www.getjavelin.io) service is a high-performance, enterprise grade API Gateway for AI applications.  
+It is designed to streamline the usage and access of various large language model (LLM) providers, 
+such as OpenAI, Cohere, Anthropic and custom large language models within an organization by incorporating
+robust access security for all interactions with LLMs. 
+
+Javelin offers a high-level interface that simplifies the interaction with LLMs by providing a unified endpoint 
+to handle specific LLM related requests. 
+
+See the Javelin AI Gateway [documentation](https://docs.getjavelin.io) for more details.  
+[Javelin Python SDK](https://www.github.com/getjavelin/javelin-python) is an easy to use client library meant to be embedded into AI Applications
+
+## Installation and Setup
+
+Install `javelin_sdk` to interact with Javelin AI Gateway:
+
+```sh
+pip install 'javelin_sdk'
+```
+
+Set the Javelin's API key as an environment variable:
+
+```sh
+export JAVELIN_API_KEY=...
+```
+
+## Completions Example
+
+```python
+
+from langchain.chains import LLMChain
+from langchain.llms import JavelinAIGateway
+from langchain.prompts import PromptTemplate
+
+route_completions = "eng_dept03"
+
+gateway = JavelinAIGateway(
+    gateway_uri="http://localhost:8000",
+    route=route_completions,
+    model_name="text-davinci-003",
+)
+
+llmchain = LLMChain(llm=gateway, prompt=prompt)
+result = llmchain.run("podcast player")
+
+print(result)
+
+```
+
+## Embeddings Example
+
+```python
+from langchain.embeddings import JavelinAIGatewayEmbeddings
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+embeddings = JavelinAIGatewayEmbeddings(
+    gateway_uri="http://localhost:8000",
+    route="embeddings",
+)
+
+print(embeddings.embed_query("hello"))
+print(embeddings.embed_documents(["hello"]))
+```
+
+## Chat Example
+```python
+from langchain.chat_models import ChatJavelinAIGateway
+from langchain.schema import HumanMessage, SystemMessage
+
+messages = [
+    SystemMessage(
+        content="You are a helpful assistant that translates English to French."
+    ),
+    HumanMessage(
+        content="Artificial Intelligence has the power to transform humanity and make the world a better place"
+    ),
+]
+
+chat = ChatJavelinAIGateway(
+    gateway_uri="http://localhost:8000",
+    route="mychatbot_route",
+    model_name="gpt-3.5-turbo"
+    params={
+        "temperature": 0.1
+    }
+)
+
+print(chat(messages))
+
+```
+

libs/langchain/langchain/chat_models/__init__.py

@@ -26,6 +26,7 @@
 from langchain.chat_models.fake import FakeListChatModel
 from langchain.chat_models.google_palm import ChatGooglePalm
 from langchain.chat_models.human import HumanInputChatModel
+from langchain.chat_models.javelin_ai_gateway import ChatJavelinAIGateway
 from langchain.chat_models.jinachat import JinaChat
 from langchain.chat_models.konko import ChatKonko
 from langchain.chat_models.litellm import ChatLiteLLM
@@ -53,6 +54,7 @@
     "ChatAnyscale",
     "ChatLiteLLM",
     "ErnieBotChat",
+    "ChatJavelinAIGateway",
     "ChatKonko",
     "QianfanChatEndpoint",
 ]