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refactor: streamline OpenAI_Chat initialization and deprecate old parameters #734

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refactor: streamline OpenAI_Chat initialization and deprecate old parameters #734

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refactor: streamline OpenAI_Chat initialization and deprecate old par…
PucaVaz Dec 15, 2024
a787cf7
refactor: improve error handling and streamline response generation i…
PucaVaz Dec 26, 2024
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126 changes: 38 additions & 88 deletions src/vanna/openai/openai_chat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,120 +9,70 @@ class OpenAI_Chat(VannaBase):
def __init__(self, client=None, config=None):
VannaBase.__init__(self, config=config)

# default parameters - can be overrided using config
self.temperature = 0.7
# Ensure config is a dictionary
config = config or {}

if "temperature" in config:
self.temperature = config["temperature"]
# Default parameters - can be overridden using config
self.temperature = config.get("temperature", 0.7)

if "api_type" in config:
raise Exception(
"Passing api_type is now deprecated. Please pass an OpenAI client instead."
)

if "api_base" in config:
raise Exception(
"Passing api_base is now deprecated. Please pass an OpenAI client instead."
)

if "api_version" in config:
raise Exception(
"Passing api_version is now deprecated. Please pass an OpenAI client instead."
)
# Raise exceptions for deprecated parameters
for deprecated_param in ["api_type", "api_base", "api_version"]:
if deprecated_param in config:
raise ValueError(
f"Passing {deprecated_param} is now deprecated. Please pass an OpenAI client instead."
)

if client is not None:
self.client = client
return

if config is None and client is None:
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
return

if "api_key" in config:
self.client = OpenAI(api_key=config["api_key"])
# Initialize the OpenAI client with optional overrides from config
self.client = OpenAI(
api_key=config.get("api_key"),
base_url=config.get("base_url")
)

def system_message(self, message: str) -> any:
def system_message(self, message: str) -> dict:
return {"role": "system", "content": message}

def user_message(self, message: str) -> any:
def user_message(self, message: str) -> dict:
return {"role": "user", "content": message}

def assistant_message(self, message: str) -> any:
def assistant_message(self, message: str) -> dict:
return {"role": "assistant", "content": message}

def generate_response(self, prompt, num_tokens):
model = self.config.get("model", "gpt-4o-mini")
print(f"Using model {model} for {num_tokens} tokens (approx)")
response = self.client.chat.completions.create(
model=model,
messages=prompt,
stop=None,
temperature=self.temperature,
)
return response

def submit_prompt(self, prompt, **kwargs) -> str:
if prompt is None:
raise Exception("Prompt is None")
raise ValueError("Prompt is None")

if len(prompt) == 0:
raise Exception("Prompt is empty")
raise ValueError("Prompt is empty")

# Count the number of tokens in the message log
# Use 4 as an approximation for the number of characters per token
num_tokens = 0
for message in prompt:
num_tokens += len(message["content"]) / 4

if kwargs.get("model", None) is not None:
model = kwargs.get("model", None)
print(
f"Using model {model} for {num_tokens} tokens (approx)"
)
response = self.client.chat.completions.create(
model=model,
messages=prompt,
stop=None,
temperature=self.temperature,
)
elif kwargs.get("engine", None) is not None:
engine = kwargs.get("engine", None)
print(
f"Using model {engine} for {num_tokens} tokens (approx)"
)
response = self.client.chat.completions.create(
engine=engine,
messages=prompt,
stop=None,
temperature=self.temperature,
)
elif self.config is not None and "engine" in self.config:
print(
f"Using engine {self.config['engine']} for {num_tokens} tokens (approx)"
)
response = self.client.chat.completions.create(
engine=self.config["engine"],
messages=prompt,
stop=None,
temperature=self.temperature,
)
elif self.config is not None and "model" in self.config:
print(
f"Using model {self.config['model']} for {num_tokens} tokens (approx)"
)
response = self.client.chat.completions.create(
model=self.config["model"],
messages=prompt,
stop=None,
temperature=self.temperature,
)
else:
if num_tokens > 3500:
model = "gpt-3.5-turbo-16k"
else:
model = "gpt-3.5-turbo"

print(f"Using model {model} for {num_tokens} tokens (approx)")
response = self.client.chat.completions.create(
model=model,
messages=prompt,
stop=None,
temperature=self.temperature,
)

# Find the first response from the chatbot that has text in it (some responses may not have text)
# Use the generate_response method to get the response
response = self.generate_response(prompt, num_tokens)

# Find the first response from the chatbot that has text in it
# (some responses may not have text)
for choice in response.choices:
if "text" in choice:
return choice.text

# If no response with text is found, return the first response's content (which may be empty)
return response.choices[0].message.content
# If no response with text is found, return the first response's content
return response.choices[0].message.content