From 787c8333cec047c37fb6cdfa25284a71a6a15c66 Mon Sep 17 00:00:00 2001
From: Arash Mosharraf <armoshar@microsoft.com>
Date: Tue, 16 Jan 2024 15:17:06 -0600
Subject: [PATCH] AzureOPenAILLM added still need a lot of adjustments
---
docs/test.ipynb | 379 +++++++++++++++++++++++++++++++
semantic_router/layer.py | 4 +-
semantic_router/llms/__init__.py | 3 +-
semantic_router/llms/base.py | 1 +
semantic_router/llms/zure.py | 59 +++++
semantic_router/route.py | 50 ++--
6 files changed, 470 insertions(+), 26 deletions(-)
create mode 100644 docs/test.ipynb
create mode 100644 semantic_router/llms/zure.py
diff --git a/docs/test.ipynb b/docs/test.ipynb
new file mode 100644
index 00000000..d6638f0b
--- /dev/null
+++ b/docs/test.ipynb
@@ -0,0 +1,379 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\armoshar\\OneDrive - Microsoft\\Projects\\OpenAI\\semantic-router\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "\n",
+ "current_script_path = os.getcwd()\n",
+ "parent_dir = os.path.dirname(current_script_path)\n",
+ "#subfolder_path = os.path.join(parent_dir)\n",
+ "\n",
+ "print(parent_dir)\n",
+ "# adding Folder_2 to the system path\n",
+ "sys.path.insert(0, parent_dir)\n",
+ " \n",
+ "# importing the add and odd_even\n",
+ "# function\n",
+ " \n",
+ "from semantic_router import Route\n",
+ "from semantic_router.encoders import CohereEncoder, OpenAIEncoder, AzureOpenAIEncoder\n",
+ "\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\",\n",
+ " \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dotenv import load_dotenv\n",
+ "import os\n",
+ "\n",
+ "load_dotenv()\n",
+ "\n",
+ "\n",
+ "api_key = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
+ "azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+ "deployment = os.getenv(\"AZURE_OPENAI_DEPLOYMENT_NAME\")\n",
+ "\n",
+ "encoder = AzureOpenAIEncoder(api_key=api_key, azure_endpoint=azure_endpoint, api_version=\"2023-07-01-preview\",\n",
+ " model=\"text-embedding-ada-002\" )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_direction(start)->str:\n",
+ " \"\"\"just produce a direction from the starting point to the library\n",
+ " :param start: the starting address\n",
+ " :type start: str\n",
+ "\n",
+ " \n",
+ " :return: the direction\n",
+ " \n",
+ " \"\"\"\n",
+ " print(\"From main street to the library: go left and right and you get there in 5 minutes\")\n",
+ "\n",
+ "\n",
+ "def do_fuzzy_case()->str:\n",
+ " \"\"\"Handle the fuzzy case questions \n",
+ " \n",
+ " return: the text\n",
+ " \"\"\"\n",
+ " print(\"I need mroe information to help you with that\")\n",
+ "\n",
+ "def do_irrelevant()->str:\n",
+ " \"\"\"Handle the irrelevant questions \n",
+ " \n",
+ " return: the text\n",
+ " \"\"\"\n",
+ " print(\"I don't know how to help you with that\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.utils.function_call import get_schema\n",
+ "\n",
+ "direction_schema = get_schema(get_direction)\n",
+ "fuzzy_schema = get_schema(do_fuzzy_case)\n",
+ "irrelevant_schema = get_schema(do_irrelevant)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'name': 'do_irrelevant',\n",
+ " 'description': 'Handle the irrelevant questions \\n\\nreturn: the text',\n",
+ " 'signature': '() -> str',\n",
+ " 'output': \"<class 'str'>\"}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "irrelevant_schema"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "direction = Route(\n",
+ " name=\"get_direction\",\n",
+ " utterances=[\n",
+ " \"How do I get to the closes park?\",\n",
+ " \"I want to go to the nearest restaurant\",\n",
+ " \"I want to go to the nearest restaurant from my location\",\n",
+ " \"I want to go to the nearest restaurant from my location to my home\",\n",
+ " \"I want to go to a library in city of Dallas\",\n",
+ " \"How do I get to the mall?\",\n",
+ " \"find me the best route to the closes Chines restaurant\",\n",
+ " \"How far is the drive from my mom's house to the nearest grocery store\",\n",
+ " ],\n",
+ " function_schema=direction_schema,\n",
+ ")\n",
+ "\n",
+ "fuzzycase = Route(\n",
+ " name=\"do_fuzzy_case\",\n",
+ " utterances=[\n",
+ " \"I want to go to the nearest restaurant from my location to my home but I want to stop by the grocery store\",\n",
+ " \"How do I get to a different continent from my home\",\n",
+ " \"I want to go to the libary but I want to stop by the grocery store and then go to the nearest restaurant and come back\",\n",
+ " \"City of Austin to Los Angeles\",\n",
+ " \"Ski Slopes\",\n",
+ " ],\n",
+ " function_schema=fuzzy_schema,\n",
+ ")\n",
+ "\n",
+ "irrelevant = Route(\n",
+ " name=\"do_irrelevant\",\n",
+ " utterances=[\n",
+ " \"How do I make grilled lobster\",\n",
+ " \"What is the best book for finding the driving directions\",\n",
+ " \"What is the best song\",\n",
+ " \"Can you tell me the most popular color in the world\",\n",
+ " \"Tell me a joke\", \n",
+ " ],\n",
+ " function_schema=irrelevant_schema,\n",
+ ")\n",
+ "\n",
+ "routes = [direction, fuzzycase, irrelevant ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-01-16 15:05:08 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "from semantic_router.llms import AzureOpenAILLM\n",
+ "\n",
+ "\n",
+ "llm = AzureOpenAILLM(openai_api_key=api_key, azure_endpoint=azure_endpoint) \n",
+ "rl = RouteLayer(encoder=encoder, routes=routes, llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-01-16 15:05:09 INFO semantic_router.utils.logger LLM `name='gpt-35-turbo' client=<openai.lib.azure.AzureOpenAI object at 0x000001B4C401AE30> temperature=0.01 max_tokens=200` is chosen\u001b[0m\n",
+ "\u001b[32m2024-01-16 15:05:09 INFO semantic_router.utils.logger this is the llm passed to route object name='gpt-35-turbo' client=<openai.lib.azure.AzureOpenAI object at 0x000001B4C401AE30> temperature=0.01 max_tokens=200\u001b[0m\n",
+ "\u001b[32m2024-01-16 15:05:09 INFO semantic_router.utils.logger Extracting function input...\u001b[0m\n",
+ "\u001b[31m2024-01-16 15:05:09 ERROR semantic_router.utils.logger Input validation error: list index out of range\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"start\": \"my home\"\n",
+ " }\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Invalid inputs",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mrl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhow do I get to the nearest gas station from my home?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32mc:\\Users\\armoshar\\OneDrive - Microsoft\\Projects\\OpenAI\\semantic-router\\semantic_router\\layer.py:203\u001b[0m, in \u001b[0;36mRouteLayer.__call__\u001b[1;34m(self, text)\u001b[0m\n\u001b[0;32m 201\u001b[0m route\u001b[38;5;241m.\u001b[39mllm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mllm\n\u001b[0;32m 202\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLLM `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mroute\u001b[38;5;241m.\u001b[39mllm\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` is chosen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mroute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 205\u001b[0m \u001b[38;5;66;03m# if no route passes threshold, return empty route choice\u001b[39;00m\n\u001b[0;32m 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m RouteChoice()\n",
+ "File \u001b[1;32mc:\\Users\\armoshar\\OneDrive - Microsoft\\Projects\\OpenAI\\semantic-router\\semantic_router\\route.py:57\u001b[0m, in \u001b[0;36mRoute.__call__\u001b[1;34m(self, query)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLLM is required for dynamic routes. Please ensure the `llm` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattribute is set.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 55\u001b[0m )\n\u001b[0;32m 56\u001b[0m \u001b[38;5;66;03m# if a function schema is provided we generate the inputs\u001b[39;00m\n\u001b[1;32m---> 57\u001b[0m extracted_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_function_inputs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_schema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_schema\u001b[49m\n\u001b[0;32m 59\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 60\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mextracted inputs \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mextracted_inputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 61\u001b[0m func_call \u001b[38;5;241m=\u001b[39m extracted_inputs\n",
+ "File \u001b[1;32mc:\\Users\\armoshar\\OneDrive - Microsoft\\Projects\\OpenAI\\semantic-router\\semantic_router\\llms\\base.py:87\u001b[0m, in \u001b[0;36mBaseLLM.extract_function_inputs\u001b[1;34m(self, query, function_schema)\u001b[0m\n\u001b[0;32m 85\u001b[0m function_inputs \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(output)\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_valid_inputs(function_inputs, function_schema):\n\u001b[1;32m---> 87\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid inputs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function_inputs\n",
+ "\u001b[1;31mValueError\u001b[0m: Invalid inputs"
+ ]
+ }
+ ],
+ "source": [
+ "out = rl(\"how do I get to the nearest gas station from my home?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Message(role='user', content='\\nYou are a helpful assistant designed to output JSON.\\nGiven the following function schema\\n<< {\\'name\\': \\'do_irrelevant\\', \\'description\\': \\'Handle the irrelevant questions \\\\n\\\\nreturn: the text\\', \\'signature\\': \\'() -> str\\', \\'output\\': \"<class \\'str\\'>\"} >>\\nand query\\n<< \\n \"How do I make grilled lobster\",\\n \"What is the best book for finding the driving directions\",\\n \"What is the best song\",\\n \"Can you tell me the most popular color in the world\",\\n \"Tell me a joke\", \\n >>\\nextract the parameters values from the query, in a valid JSON format.\\nExample:\\nInput:\\nquery: \"How is the weather in Hawaii right now in International units?\"\\nschema:\\n{\\n \"name\": \"get_weather\",\\n \"description\": \"Useful to get the weather in a specific location\",\\n \"signature\": \"(location: str, degree: str) -> str\",\\n \"output\": \"<class \\'str\\'>\",\\n}\\n\\nResult: {\\n \"location\": \"London\",\\n \"degree\": \"Celsius\",\\n}\\n\\nInput:\\nquery: \\n \"How do I make grilled lobster\",\\n \"What is the best book for finding the driving directions\",\\n \"What is the best song\",\\n \"Can you tell me the most popular color in the world\",\\n \"Tell me a joke\", \\n \\nschema: {\\'name\\': \\'do_irrelevant\\', \\'description\\': \\'Handle the irrelevant questions \\\\n\\\\nreturn: the text\\', \\'signature\\': \\'() -> str\\', \\'output\\': \"<class \\'str\\'>\"}\\nResult:\\n')]\n"
+ ]
+ },
+ {
+ "ename": "AttributeError",
+ "evalue": "'list' object has no attribute 'replace'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[15], line 60\u001b[0m\n\u001b[0;32m 57\u001b[0m output \u001b[38;5;241m=\u001b[39m llm_input\n\u001b[0;32m 59\u001b[0m \u001b[38;5;28mprint\u001b[39m(output)\n\u001b[1;32m---> 60\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43moutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreplace\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mstrip()\u001b[38;5;241m.\u001b[39mrstrip(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 62\u001b[0m function_inputs \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(output)\n\u001b[0;32m 64\u001b[0m function_inputs\n",
+ "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'replace'"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "from pydantic import BaseModel\n",
+ "class Message(BaseModel):\n",
+ " role: str\n",
+ " content: str\n",
+ "\n",
+ " def to_openai(self):\n",
+ " if self.role.lower() not in [\"user\", \"assistant\", \"system\"]:\n",
+ " raise ValueError(\"Role must be either 'user', 'assistant' or 'system'\")\n",
+ " return {\"role\": self.role, \"content\": self.content}\n",
+ "\n",
+ " def to_cohere(self):\n",
+ " return {\"role\": self.role, \"message\": self.content}\n",
+ "\n",
+ " def to_llamacpp(self):\n",
+ " return {\"role\": self.role, \"content\": self.content}\n",
+ "import json\n",
+ "import logging\n",
+ "query=\"\"\"\n",
+ " \"How do I make grilled lobster\",\n",
+ " \"What is the best book for finding the driving directions\",\n",
+ " \"What is the best song\",\n",
+ " \"Can you tell me the most popular color in the world\",\n",
+ " \"Tell me a joke\", \n",
+ " \"\"\"\n",
+ "function_schema = irrelevant_schema\n",
+ "logging.info(\"Extracting function input...\")\n",
+ "\n",
+ "prompt = f\"\"\"\n",
+ "You are a helpful assistant designed to output JSON.\n",
+ "Given the following function schema\n",
+ "<< {function_schema} >>\n",
+ "and query\n",
+ "<< {query} >>\n",
+ "extract the parameters values from the query, in a valid JSON format.\n",
+ "Example:\n",
+ "Input:\n",
+ "query: \"How is the weather in Hawaii right now in International units?\"\n",
+ "schema:\n",
+ "{{\n",
+ " \"name\": \"get_weather\",\n",
+ " \"description\": \"Useful to get the weather in a specific location\",\n",
+ " \"signature\": \"(location: str, degree: str) -> str\",\n",
+ " \"output\": \"<class 'str'>\",\n",
+ "}}\n",
+ "\n",
+ "Result: {{\n",
+ " \"location\": \"London\",\n",
+ " \"degree\": \"Celsius\",\n",
+ "}}\n",
+ "\n",
+ "Input:\n",
+ "query: {query}\n",
+ "schema: {function_schema}\n",
+ "Result:\n",
+ "\"\"\"\n",
+ "llm_input = [Message(role=\"user\", content=prompt)]\n",
+ "output = llm_input\n",
+ "\n",
+ "print(output)\n",
+ "output = output.replace(\"'\", '\"').strip().rstrip(\",\")\n",
+ "\n",
+ "function_inputs = json.loads(output)\n",
+ "\n",
+ "function_inputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "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": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index 15b3fc0d..980726a4 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -7,7 +7,7 @@ import yaml
from semantic_router.encoders import BaseEncoder, OpenAIEncoder
from semantic_router.linear import similarity_matrix, top_scores
-from semantic_router.llms import BaseLLM, OpenAILLM
+from semantic_router.llms import BaseLLM, OpenAILLM, AzureOpenAILLM
from semantic_router.route import Route
from semantic_router.schema import Encoder, EncoderType, RouteChoice
from semantic_router.utils.logger import logger
@@ -194,10 +194,12 @@ class RouteLayer:
"default. Ensure API key is set in OPENAI_API_KEY environment "
"variable."
)
+
self.llm = OpenAILLM()
route.llm = self.llm
else:
route.llm = self.llm
+ logger.info(f"LLM `{route.llm}` is chosen")
return route(text)
else:
# if no route passes threshold, return empty route choice
diff --git a/semantic_router/llms/__init__.py b/semantic_router/llms/__init__.py
index 02b3fd5b..217d441b 100644
--- a/semantic_router/llms/__init__.py
+++ b/semantic_router/llms/__init__.py
@@ -2,6 +2,7 @@ from semantic_router.llms.base import BaseLLM
from semantic_router.llms.cohere import CohereLLM
from semantic_router.llms.llamacpp import LlamaCppLLM
from semantic_router.llms.openai import OpenAILLM
+from semantic_router.llms.zure import AzureOpenAILLM
from semantic_router.llms.openrouter import OpenRouterLLM
-__all__ = ["BaseLLM", "OpenAILLM", "OpenRouterLLM", "CohereLLM", "LlamaCppLLM"]
+__all__ = ["BaseLLM", "OpenAILLM", "OpenRouterLLM", "CohereLLM", "LlamaCppLLM", "AzureOpenAILLM"]
diff --git a/semantic_router/llms/base.py b/semantic_router/llms/base.py
index 4fd2c389..e0b3007b 100644
--- a/semantic_router/llms/base.py
+++ b/semantic_router/llms/base.py
@@ -76,6 +76,7 @@ class BaseLLM(BaseModel):
"""
llm_input = [Message(role="user", content=prompt)]
output = self(llm_input)
+ print(output)
if not output:
raise Exception("No output generated for extract function input")
diff --git a/semantic_router/llms/zure.py b/semantic_router/llms/zure.py
new file mode 100644
index 00000000..2810a167
--- /dev/null
+++ b/semantic_router/llms/zure.py
@@ -0,0 +1,59 @@
+import os
+from typing import List, Optional
+
+import openai
+
+from semantic_router.llms import BaseLLM
+from semantic_router.schema import Message
+from semantic_router.utils.logger import logger
+
+
+class AzureOpenAILLM(BaseLLM):
+ client: Optional[openai.AzureOpenAI]
+ temperature: Optional[float]
+ max_tokens: Optional[int]
+
+ def __init__(
+ self,
+ name: Optional[str] = None,
+ openai_api_key: Optional[str] = None,
+ azure_endpoint:Optional[str] = None,
+ temperature: float = 0.01,
+ max_tokens: int = 200,
+ api_version="2023-07-01-preview"
+ ):
+ if name is None:
+ name = os.getenv("OPENAI_CHAT_MODEL_NAME", "gpt-35-turbo")
+ super().__init__(name=name)
+ api_key = openai_api_key or os.getenv("OPENAI_API_KEY")
+ if api_key is None:
+ raise ValueError("OpenAI API key cannot be 'None'.")
+ if azure_endpoint is None:
+ raise ValueError("Azure endpoint API key cannot be 'None'.")
+ try:
+ self.client = openai.AzureOpenAI(api_key=api_key, azure_endpoint=azure_endpoint
+ , api_version=api_version)
+ except Exception as e:
+ raise ValueError(f"OpenAI API client failed to initialize. Error: {e}")
+ self.temperature = temperature
+ self.max_tokens = max_tokens
+
+ def __call__(self, messages: List[Message]) -> str:
+ if self.client is None:
+ raise ValueError("OpenAI client is not initialized.")
+ try:
+ completion = self.client.chat.completions.create(
+ model=self.name,
+ messages=[m.to_openai() for m in messages],
+ temperature=self.temperature,
+ max_tokens=self.max_tokens,
+ )
+
+ output = completion.choices[0].message.content
+
+ if not output:
+ raise Exception("No output generated")
+ return output
+ except Exception as e:
+ logger.error(f"LLM error: {e}")
+ raise Exception(f"LLM error: {e}")
diff --git a/semantic_router/route.py b/semantic_router/route.py
index b3f36b8b..bcc3036d 100644
--- a/semantic_router/route.py
+++ b/semantic_router/route.py
@@ -44,8 +44,9 @@ class Route(BaseModel):
description: Optional[str] = None
function_schema: Optional[Dict[str, Any]] = None
llm: Optional[BaseLLM] = None
-
+
def __call__(self, query: str) -> RouteChoice:
+ logger.info(f"this is the llm passed to route object {self.llm}")
if self.function_schema:
if not self.llm:
raise ValueError(
@@ -56,6 +57,7 @@ class Route(BaseModel):
extracted_inputs = self.llm.extract_function_inputs(
query=query, function_schema=self.function_schema
)
+ logger.info(f"extracted inputs {extracted_inputs}")
func_call = extracted_inputs
else:
# otherwise we just pass None for the call
@@ -96,29 +98,29 @@ class Route(BaseModel):
logger.info("Generating dynamic route...")
prompt = f"""
-You are tasked to generate a JSON configuration based on the provided
-function schema. Please follow the template below, no other tokens allowed:
-
-<config>
-{{
- "name": "<function_name>",
- "utterances": [
- "<example_utterance_1>",
- "<example_utterance_2>",
- "<example_utterance_3>",
- "<example_utterance_4>",
- "<example_utterance_5>"]
-}}
-</config>
-
-Only include the "name" and "utterances" keys in your answer.
-The "name" should match the function name and the "utterances"
-should comprise a list of 5 example phrases that could be used to invoke
-the function. Use real values instead of placeholders.
-
-Input schema:
-{function_schema}
-"""
+ You are tasked to generate a JSON configuration based on the provided
+ function schema. Please follow the template below, no other tokens allowed:
+
+ <config>
+ {{
+ "name": "<function_name>",
+ "utterances": [
+ "<example_utterance_1>",
+ "<example_utterance_2>",
+ "<example_utterance_3>",
+ "<example_utterance_4>",
+ "<example_utterance_5>"]
+ }}
+ </config>
+
+ Only include the "name" and "utterances" keys in your answer.
+ The "name" should match the function name and the "utterances"
+ should comprise a list of 5 example phrases that could be used to invoke
+ the function. Use real values instead of placeholders.
+
+ Input schema:
+ {function_schema}
+ """
llm_input = [Message(role="user", content=prompt)]
output = llm(llm_input)
--
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