Add LiteLLMAICaller class for AI model interactions, integrate settin…

…gs with config loader, and update dependencies

cover_agent/LiteLLMAICaller.py

@@ -0,0 +1,115 @@
+import tiktoken
+from litellm import completion
+import openai
+import litellm
+
+from settings.config_loader import get_settings
+
+
+class LiteLLMAICaller:
+    def __init__(self, model):
+        self.azure = False
+        self.aws_bedrock_client = None
+        self.api_base = None
+        self.repetition_penalty = None
+        if get_settings().get("OPENAI_API_KEY", None):
+            openai.api_key = get_settings().OPENAI_API_KEY
+            litellm.openai_key = get_settings().OPENAI_API_KEY
+        if get_settings().get("LITELLM.DROP_PARAMS", None):
+            litellm.drop_params = get_settings().litellm.drop_params
+        if get_settings().get("OPENAI.ORG", None):
+            litellm.organization = get_settings().openai.org
+        if get_settings().get("OPENAI.API_TYPE", None):
+            if get_settings().openai.api_type == "azure":
+                self.azure = True
+                litellm.azure_key = get_settings().openai.key
+        if get_settings().get("OPENAI.API_VERSION", None):
+            litellm.api_version = get_settings().openai.api_version
+        if get_settings().get("OPENAI.API_BASE", None):
+            litellm.api_base = get_settings().openai.api_base
+        if get_settings().get("ANTHROPIC.KEY", None):
+            litellm.anthropic_key = get_settings().anthropic.key
+        if get_settings().get("COHERE.KEY", None):
+            litellm.cohere_key = get_settings().cohere.key
+        if get_settings().get("GROQ.KEY", None):
+            litellm.api_key = get_settings().groq.key
+        if get_settings().get("REPLICATE.KEY", None):
+            litellm.replicate_key = get_settings().replicate.key
+        if get_settings().get("HUGGINGFACE.KEY", None):
+            litellm.huggingface_key = get_settings().huggingface.key
+        if get_settings().get("HUGGINGFACE.API_BASE", None) and 'huggingface' in get_settings().config.model:
+            litellm.api_base = get_settings().huggingface.api_base
+            self.api_base = get_settings().huggingface.api_base
+        if get_settings().get("OLLAMA.API_BASE", None):
+            litellm.api_base = get_settings().ollama.api_base
+            self.api_base = get_settings().ollama.api_base
+        if get_settings().get("HUGGINGFACE.REPITITION_PENALTY", None):
+            self.repetition_penalty = float(get_settings().huggingface.repetition_penalty)
+        if get_settings().get("VERTEXAI.VERTEX_PROJECT", None):
+            litellm.vertex_project = get_settings().vertexai.vertex_project
+            litellm.vertex_location = get_settings().get(
+                "VERTEXAI.VERTEX_LOCATION", None
+            )
+        if get_settings().get("AWS.BEDROCK_REGION", None):
+            litellm.AmazonAnthropicConfig.max_tokens_to_sample = 2000
+            litellm.AmazonAnthropicClaude3Config.max_tokens = 2000
+            self.aws_bedrock_client = boto3.client(
+                service_name="bedrock-runtime",
+                region_name=get_settings().aws.bedrock_region,
+            )
+
+        self.model = model
+
+        # Initialize the encoding for the model
+        try:
+            self.encoding = tiktoken.get_encoding("cl100k_base")
+        except Exception as e:
+            raise ValueError(f"Failed to get encoding: {e}")
+
+    def call_model(self, prompt, max_tokens=4096):
+        """
+        Calls the OpenAI API with streaming enabled to get completions for a given prompt
+        and streams the output to the console in real time, while also accumulating the response to return.
+
+        Parameters:
+            prompt (str): The prompt to send to the model.
+            max_tokens (int, optional): The maximum number of tokens to generate. Defaults to 4096.
+
+        Returns:
+            str: The text generated by the model.
+        """
+
+        model = self.model
+        if self.azure:
+            model = 'azure/' + model
+        messages = [{"role": "user", "content": prompt}]
+        kwargs = {
+            "model": model,
+            "messages": messages,
+            "temperature": 0,
+            "force_timeout": 180,
+            "api_base": self.api_base,
+        }
+        if self.aws_bedrock_client:
+            kwargs["aws_bedrock_client"] = self.aws_bedrock_client
+        if self.repetition_penalty:
+            kwargs["repetition_penalty"] = self.repetition_penalty
+
+        response =  completion(**kwargs)
+        full_text = response.choices[0].model_extra['message']['content']
+        return full_text.strip()
+
+    def count_tokens(self, text):
+        """
+        Counts the number of tokens in the given text using the model's encoding.
+
+        Parameters:
+            text (str): The text to encode.
+
+        Returns:
+            int: The number of tokens.
+        """
+        try:
+            return len(self.encoding.encode(text))
+        except Exception as e:
+            raise ValueError(f"Error encoding text: {e}")

cover_agent/UnitTestGenerator.py

@@ -5,7 +5,7 @@
 from cover_agent.CoverageProcessor import CoverageProcessor
 from cover_agent.CustomLogger import CustomLogger
 from cover_agent.PromptBuilder import PromptBuilder
-from cover_agent.AICaller import AICaller
+from cover_agent.LiteLLMAICaller import LiteLLMAICaller as AICaller
 from cover_agent.FilePreprocessor import FilePreprocessor
 
 

cover_agent/main.py

@@ -4,6 +4,7 @@
 from cover_agent.ReportGenerator import ReportGenerator
 from cover_agent.UnitTestGenerator import UnitTestGenerator
 from cover_agent.version import __version__
+from settings.config_loader import get_settings
 
 
 def parse_args():
@@ -140,8 +141,12 @@ def main():
             logger.info(f"Desired Coverage: {test_gen.desired_coverage}%")
 
             # Generate tests by making a call to the LLM
+            if get_settings().get("config.model", None):
+                model = get_settings().get("config.model")
+            else:
+                model = args.openai_model
             generated_tests = test_gen.generate_tests(
-                LLM_model=args.openai_model, max_tokens=4096
+                LLM_model=model, max_tokens=4096
             )
 
             # Write test_gen.prompt to a debug markdown file