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Fixed Issue #173 - Evaluate Base Model Endpoints giving errors #175

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Merged
kdestin merged 1 commit into from
Jan 6, 2025
Merged

Fixed Issue #173 - Evaluate Base Model Endpoints giving errors #175

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123 changes: 3 additions & 120 deletions ...ported_Evaluation_Targets/Evaluate_Base_Model_Endpoint/Evaluate_Base_Model_Endpoint.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -158,125 +158,7 @@
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from typing_extensions import Self\n",
"from typing import TypedDict\n",
"from promptflow.tracing import trace\n",
"\n",
"\n",
"class ModelEndpoints:\n",
" def __init__(self: Self, env: dict, model_type: str) -> str:\n",
" self.env = env\n",
" self.model_type = model_type\n",
"\n",
" class Response(TypedDict):\n",
" query: str\n",
" response: str\n",
"\n",
" @trace\n",
" def __call__(self: Self, query: str) -> Response:\n",
" if self.model_type == \"gpt4-0613\":\n",
" output = self.call_gpt4_endpoint(query)\n",
" elif self.model_type == \"gpt35-turbo\":\n",
" output = self.call_gpt35_turbo_endpoint(query)\n",
" elif self.model_type == \"mistral7b\":\n",
" output = self.call_mistral_endpoint(query)\n",
" elif self.model_type == \"tiny_llama\":\n",
" output = self.call_tiny_llama_endpoint(query)\n",
" elif self.model_type == \"phi3_mini_serverless\":\n",
" output = self.call_phi3_mini_serverless_endpoint(query)\n",
" elif self.model_type == \"gpt2\":\n",
" output = self.call_gpt2_endpoint(query)\n",
" else:\n",
" output = self.call_default_endpoint(query)\n",
"\n",
" return output\n",
"\n",
" def query(self: Self, endpoint: str, headers: str, payload: str) -> str:\n",
" response = requests.post(url=endpoint, headers=headers, json=payload)\n",
" return response.json()\n",
"\n",
" def call_gpt4_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"gpt4-0613\"][\"endpoint\"]\n",
" key = self.env[\"gpt4-0613\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"api-key\": key}\n",
"\n",
" payload = {\"messages\": [{\"role\": \"user\", \"content\": query}], \"max_tokens\": 500}\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[\"choices\"][0][\"message\"][\"content\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_gpt35_turbo_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"gpt35-turbo\"][\"endpoint\"]\n",
" key = self.env[\"gpt35-turbo\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"api-key\": key}\n",
"\n",
" payload = {\"messages\": [{\"role\": \"user\", \"content\": query}], \"max_tokens\": 500}\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[\"choices\"][0][\"message\"][\"content\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_tiny_llama_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"tiny_llama\"][\"endpoint\"]\n",
" key = self.env[\"tiny_llama\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"Authorization\": (\"Bearer \" + key)}\n",
"\n",
" payload = {\n",
" \"model\": \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\",\n",
" \"messages\": [{\"role\": \"user\", \"content\": query}],\n",
" \"max_tokens\": 500,\n",
" \"stream\": False,\n",
" }\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[\"choices\"][0][\"message\"][\"content\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_phi3_mini_serverless_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"phi3_mini_serverless\"][\"endpoint\"]\n",
" key = self.env[\"phi3_mini_serverless\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"Authorization\": (\"Bearer \" + key)}\n",
"\n",
" payload = {\"messages\": [{\"role\": \"user\", \"content\": query}], \"max_tokens\": 500}\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[\"choices\"][0][\"message\"][\"content\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_gpt2_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"gpt2\"][\"endpoint\"]\n",
" key = self.env[\"gpt2\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"Authorization\": (\"Bearer \" + key)}\n",
"\n",
" payload = {\n",
" \"inputs\": query,\n",
" }\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[0][\"generated_text\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_mistral_endpoint(self: Self, query: str) -> Response:\n",
" endpoint = self.env[\"mistral7b\"][\"endpoint\"]\n",
" key = self.env[\"mistral7b\"][\"key\"]\n",
"\n",
" headers = {\"Content-Type\": \"application/json\", \"Authorization\": (\"Bearer \" + key)}\n",
"\n",
" payload = {\"messages\": [{\"content\": query, \"role\": \"user\"}], \"max_tokens\": 50}\n",
"\n",
" output = self.query(endpoint=endpoint, headers=headers, payload=payload)\n",
" response = output[\"choices\"][0][\"message\"][\"content\"]\n",
" return {\"query\": query, \"response\": response}\n",
"\n",
" def call_default_endpoint(query: str) -> Response:\n",
" return {\"query\": \"What is the capital of France?\", \"response\": \"Paris\"}"
"!pygmentize model_endpoints.py"
]
},
{
Expand Down Expand Up @@ -349,6 +231,7 @@
"from azure.ai.evaluation import (\n",
" RelevanceEvaluator,\n",
")\n",
"from model_endpoints import ModelEndpoints\n",
"\n",
"relevance_evaluator = RelevanceEvaluator(model_config)\n",
"\n",
Expand Down Expand Up @@ -412,7 +295,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "venv-azureai-samples",
"display_name": ".venv",
"language": "python",
"name": "python3"
},
Expand Down
119 changes: 119 additions & 0 deletions ...ios/evaluate/Supported_Evaluation_Targets/Evaluate_Base_Model_Endpoint/model_endpoints.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,119 @@
import requests
from typing_extensions import Self
from typing import TypedDict
from promptflow.tracing import trace


class ModelEndpoints:
def __init__(self: Self, env: dict, model_type: str) -> str:
self.env = env
self.model_type = model_type

class Response(TypedDict):
query: str
response: str

@trace
def __call__(self: Self, query: str) -> Response:
if self.model_type == "gpt4-0821":
output = self.call_gpt4_endpoint(query)
elif self.model_type == "gpt35-turbo":
output = self.call_gpt35_turbo_endpoint(query)
elif self.model_type == "mistral7b":
output = self.call_mistral_endpoint(query)
elif self.model_type == "tiny_llama":
output = self.call_tiny_llama_endpoint(query)
elif self.model_type == "phi3_mini_serverless":
output = self.call_phi3_mini_serverless_endpoint(query)
elif self.model_type == "gpt2":
output = self.call_gpt2_endpoint(query)
else:
output = self.call_default_endpoint(query)

return output

def query(self: Self, endpoint: str, headers: str, payload: str) -> str:
response = requests.post(url=endpoint, headers=headers, json=payload)
return response.json()

def call_gpt4_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["gpt4-0821"]["endpoint"]
key = self.env["gpt4-0821"]["key"]

headers = {"Content-Type": "application/json", "api-key": key}

payload = {"messages": [{"role": "user", "content": query}], "max_tokens": 500}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output["choices"][0]["message"]["content"]
return {"query": query, "response": response}

def call_gpt35_turbo_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["gpt35-turbo"]["endpoint"]
key = self.env["gpt35-turbo"]["key"]

headers = {"Content-Type": "application/json", "api-key": key}

payload = {"messages": [{"role": "user", "content": query}], "max_tokens": 500}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output["choices"][0]["message"]["content"]
return {"query": query, "response": response}

def call_tiny_llama_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["tiny_llama"]["endpoint"]
key = self.env["tiny_llama"]["key"]

headers = {"Content-Type": "application/json", "Authorization": ("Bearer " + key)}

payload = {
"model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"messages": [{"role": "user", "content": query}],
"max_tokens": 500,
"stream": False,
}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output["choices"][0]["message"]["content"]
return {"query": query, "response": response}

def call_phi3_mini_serverless_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["phi3_mini_serverless"]["endpoint"]
key = self.env["phi3_mini_serverless"]["key"]

headers = {"Content-Type": "application/json", "Authorization": ("Bearer " + key)}

payload = {"messages": [{"role": "user", "content": query}], "max_tokens": 500}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output["choices"][0]["message"]["content"]
return {"query": query, "response": response}

def call_gpt2_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["gpt2"]["endpoint"]
key = self.env["gpt2"]["key"]

headers = {"Content-Type": "application/json", "Authorization": ("Bearer " + key)}

payload = {
"inputs": query,
}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output[0]["generated_text"]
return {"query": query, "response": response}

def call_mistral_endpoint(self: Self, query: str) -> Response:
endpoint = self.env["mistral7b"]["endpoint"]
key = self.env["mistral7b"]["key"]

headers = {"Content-Type": "application/json", "Authorization": ("Bearer " + key)}

payload = {"messages": [{"content": query, "role": "user"}], "max_tokens": 50}

output = self.query(endpoint=endpoint, headers=headers, payload=payload)
response = output["choices"][0]["message"]["content"]
return {"query": query, "response": response}

def call_default_endpoint(query: str) -> Response:
return {"query": "What is the capital of France?", "response": "Paris"}
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