Add LMStudioClient and update __init__.py #210
Conversation
| assert isinstance(input, Sequence), "input must be a sequence of text" | ||
| final_model_kwargs["input"] = input | ||
| elif model_type == ModelType.LLM: | ||
| messages = [] |
There was a problem hiding this comment.
Should use type hints for messages
| messages = [] | |
| messages: List[Dict[str, str]] = [] |
ref: model_client/openai_client.py line 234
| if input is not None and input != "": | ||
| messages.append({"role": "system", "content": "You are a helpful assistant. Provide a direct and concise answer to the user's question. Do not include any URLs or references in your response."}) | ||
| messages.append({"role": "user", "content": input}) | ||
| assert isinstance(messages, Sequence), "input must be a sequence of messages" |
There was a problem hiding this comment.
I don't think this assert statement is needed since messages is explicitly created as a list just a few lines above.
| assert isinstance(messages, Sequence), "input must be a sequence of messages" | ||
| final_model_kwargs["messages"] = messages | ||
|
|
||
| # Set default values for controlling response length if not provided |
There was a problem hiding this comment.
Consider using a for-loop instead
| # Set default values for controlling response length if not provided | |
| default_values = [("temperature", 0.1), ("frequency_penalty", 0.0), ("presence_penalty", 0.0), ("stop", ["\n", "###", "://"])] | |
| for key, val in default_values: | |
| final_model_kwargs.setdefault(key, val) |
| response.raise_for_status() | ||
| return response.json() | ||
|
|
||
| def parse_chat_completion(self, completion: Dict) -> GeneratorOutput: |
There was a problem hiding this comment.
I suggest writting more precise error messages
| def parse_chat_completion(self, completion: Dict) -> GeneratorOutput: | |
| def parse_chat_completion(self, completion: Dict) -> GeneratorOutput: | |
| """Parse the completion to a GeneratorOutput.""" | |
| try: | |
| if "choices" not in completion: | |
| return GeneratorOutput(data=None, error="Error parsing the completion: 'choices' not in 'completion'.", raw_response=content) | |
| elif not len(completion["choices"]) > 0: | |
| return GeneratorOutput(data=None, error="Error parsing the completion: 'choices' length is 0.", raw_response=content) | |
| else: | |
| content = completion["choices"][0]["message"]["content"] | |
| # Clean up the content | |
| content = self._clean_response(content) | |
| return GeneratorOutput(data=None, raw_response=content) | |
| except Exception as e: | |
| log.error(f"Error parsing the completion: {e}") | |
| return GeneratorOutput(data=None, error=str(e), raw_response=completion) |
| elif model_type == ModelType.LLM: | ||
| messages = [] | ||
| if input is not None and input != "": | ||
| messages.append({"role": "system", "content": "You are a helpful assistant. Provide a direct and concise answer to the user's question. Do not include any URLs or references in your response."}) |
There was a problem hiding this comment.
As we use uni-prompt where both system and user prompt are in the same jinja2 syntax, we only need one message here, and in default, we use role system.
messages.append({"role":"system", "content": input})
Please modify it to this.
|
|
||
| # Set default values for controlling response length if not provided | ||
| final_model_kwargs.setdefault("max_tokens", 50) | ||
| final_model_kwargs.setdefault("temperature", 0.1) |
There was a problem hiding this comment.
Please use 0 as default temperature
There was a problem hiding this comment.
or we should let the model provider decides the default behavior.
| @@ -60,6 +60,10 @@ | |||
| "adalflow.components.model_client.openai_client.get_probabilities", | |||
| OptionalPackages.OPENAI, | |||
| ) | |||
| LMStudioClient = LazyImport( | |||
| "adalflow.components.model_client.lm_studio_client.LMStudioClient", | |||
| OptionalPackages.LMSTUDIO, | |||
There was a problem hiding this comment.
The Optional package is not defined, please add it.
This dependency will also be added in the pyproejct.toml under /adalflow
| log = logging.getLogger(__name__) | ||
|
|
||
| class LMStudioClient(ModelClient): | ||
| """A component wrapper for the LM Studio API client.""" |
There was a problem hiding this comment.
Please add relevant links and instructions on how to set up the client and additionally some example in this doc_string.
LMStudioClient works with LM Studio provider of local LLM