Alwrity - WIP - main_config

lib/gpt_providers/gemini_pro_text.py

@@ -1,40 +0,0 @@
-# Using Gemini Pro LLM model
-import os
-import logging
-from pathlib import Path
-
-import google.generativeai as genai
-logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(levelname)s-%(module)s-%(lineno)d-%(message)s')
-from dotenv import load_dotenv
-load_dotenv(Path('../../.env'))
-from .mistral_chat_completion import mistral_text_response
-
-from tenacity import (
-    retry,
-    stop_after_attempt,
-    wait_random_exponential,
-)  # for exponential backoff
-
-
-@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def gemini_text_response(prompt):
-    """ Common functiont to get response from gemini pro Text. """
-    genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
-
-    # Set up the model
-    generation_config = {
-        "temperature": 1,
-        "top_p": 1,
-        "top_k": 1,
-        "max_output_tokens": 6096,
-    }
-
-    model = genai.GenerativeModel(model_name="gemini-pro", generation_config=generation_config)
-    try:
-        response = model.generate_content(prompt)
-    except Exception as err:
-        logger.error(f"Failed to get response from Gemini: {err}. Retrying.")
-        # Try with minstral.
-        #response = mistral_text_response(prompt)
-        #return response
-    return response.text

lib/gpt_providers/text_generation/gemini_pro_text.py

@@ -0,0 +1,46 @@
+# Using Gemini Pro LLM model
+import os
+import sys
+from pathlib import Path
+
+import google.generativeai as genai
+from dotenv import load_dotenv
+load_dotenv(Path('../../../.env'))
+from loguru import logger
+logger.remove()
+logger.add(sys.stdout,
+        colorize=True,
+        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
+    )
+
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_random_exponential,
+)  # for exponential backoff
+
+
+@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
+def gemini_text_response(prompt, temperature, top_p, n, max_tokens):
+    """ Common functiont to get response from gemini pro Text. """
+    try:
+        genai.configure(api_key=os.getenv('GEMINI_API_KEY'))
+    except Exception as err:
+        logger.error(f"Failed to configure Gemini: {err}")
+    logger.info(f"Temp: {temperature}, MaxTokens: {max_tokens}, TopP: {top_p}, N: {n}")
+    # Set up the model
+    generation_config = {
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": n,
+        "max_output_tokens": max_tokens
+    }
+    model = genai.GenerativeModel(model_name="gemini-pro", generation_config=generation_config)
+    try:
+        response = model.generate_content(prompt, stream=True)
+        for chunk in response:
+            print(chunk.text)
+        return response.text
+    except Exception as err:
+        logger.error(response)
+        logger.error(f"Failed to get response from Gemini: {err}. Retrying.")

lib/gpt_providers/text_generation/main_text_generation.py

@@ -0,0 +1,151 @@
+import os
+import sys
+import configparser
+from pathlib import Path
+from dotenv import load_dotenv
+load_dotenv(Path('../.env'))
+
+from loguru import logger
+logger.remove()
+logger.add(sys.stdout,
+        colorize=True,
+        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
+    )
+
+from .openai_text_gen import openai_chatgpt
+from .gemini_pro_text import gemini_text_response
+
+
+def llm_text_gen(prompt):
+    """
+    Generate text using Language Model (LLM) based on the provided prompt.
+    Args:
+        prompt (str): The prompt to generate text from.
+    Returns:
+        str: Generated text based on the prompt.
+    """
+    try:
+        config_path = Path(__file__).resolve().parents[3] / "main_config"
+        gpt_provider, model, temperature, max_tokens, top_p, n, fp = read_llm_parameters(config_path)
+
+        gpt_provider = check_gpt_provider(gpt_provider)
+        # Check if API key is provided for the given gpt_provider
+        get_api_key(gpt_provider)
+
+        logger.info(f"Model: {model}, Temp: {temperature}, MaxTokens: {max_tokens}, TopP: {top_p}, N: {n}, FrequencyPenalty: {fp}")
+        # Perform text generation using the specified LLM parameters and prompt
+        if 'google' in gpt_provider.lower():
+            try:
+                logger.info("Using Google Gemini Pro text generation model.")
+                response = gemini_text_response(prompt, temperature, top_p, n, max_tokens)
+                return response
+            except Exception as err:
+                logger.error(f"Failed to get response from gemini: {err}")
+                raise err
+        elif 'openai' in gpt_provider.lower():
+            try:
+                logger.info(f"Using OpenAI Model: {model} for text Generation.")
+                response = openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp)
+                return response
+            except Exception as err:
+                logger.error(f"Failed to get response from Openai: {err}")
+                raise err
+
+    except Exception as err:
+        logger.error(f"Failed to read LLM parameters: {err}")
+        raise
+
+
+def check_gpt_provider(gpt_provider):
+    """
+    Check if the specified GPT provider matches the environment variable GPT_PROVIDER,
+    assign and export the GPT_PROVIDER value from the config file if missing,
+    and continue.
+
+    Args:
+        gpt_provider (str): The specified GPT provider.
+
+    Raises:
+        ValueError: If both the specified GPT provider and environment variable GPT_PROVIDER are missing.
+    """
+    env_gpt_provider = os.getenv('GPT_PROVIDER')
+
+    if gpt_provider:
+        os.environ['GPT_PROVIDER'] = gpt_provider
+    elif env_gpt_provider:
+        gpt_provider = env_gpt_provider
+    else:
+        raise ValueError("Both specified GPT provider and environment variable 'GPT_PROVIDER' are missing.")
+
+    if gpt_provider != env_gpt_provider:
+        logger.warning(f"Config: '{gpt_provider}' different to environment variable 'GPT_PROVIDER' '{env_gpt_provider}'")
+    logger.info(f"Using GPT provider: {gpt_provider}")
+    return gpt_provider
+
+
+
+def get_api_key(gpt_provider):
+    """
+    Get the API key for the specified GPT provider.
+
+    Args:
+        gpt_provider (str): The specified GPT provider.
+
+    Returns:
+        str: The API key for the specified GPT provider.
+
+    Raises:
+        ValueError: If no API key is found for the specified GPT provider.
+    """
+    api_key = None
+
+    if gpt_provider.lower() == 'google':
+        api_key = os.getenv('GEMINI_API_KEY')
+    elif gpt_provider.lower() == 'openai':
+        api_key = os.getenv('OPENAI_API_KEY')
+
+    if not api_key:
+        raise ValueError(f"No API key found for the specified GPT provider: '{gpt_provider}'")
+
+    logger.info(f"Using API key for {gpt_provider}")
+    return api_key
+
+
+
+def read_llm_parameters(config_path: str) -> tuple:
+    """
+    Read Language Model (LLM) parameters from the configuration file.
+
+    Args:
+        config_path (str): The path to the configuration file.
+
+    Returns:
+        tuple: A tuple containing the LLM parameters (gpt_provider, model, temperature, max_tokens, top_p, n, frequency_penalty).
+
+    Raises:
+        FileNotFoundError: If the configuration file is not found.
+        configparser.Error: If there is an error parsing the configuration file.
+    """
+    try:
+        config = configparser.ConfigParser()
+        config.read(config_path)
+
+        gpt_provider = config.get('llm_options', 'gpt_provider')
+        model = config.get('llm_options', 'model')
+        temperature = config.getfloat('llm_options', 'temperature')
+        max_tokens = config.getint('llm_options', 'max_tokens')
+        top_p = config.getfloat('llm_options', 'top_p')
+        n = config.getint('llm_options', 'n')
+        frequency_penalty = config.getfloat('llm_options', 'frequency_penalty')
+
+        return gpt_provider, model, temperature, max_tokens, top_p, n, frequency_penalty
+
+    except FileNotFoundError:
+        logger.error(f"Configuration file not found: {config_path}")
+        raise
+    except configparser.Error as err:
+        logger.error(f"Error reading LLM parameters from config file: {err}")
+        raise
+    except Exception as err:
+        logger.error(f"An unexpected error occurred: {err}")
+        raise

lib/gpt_providers/text_generation/openai_text_gen.py

@@ -16,7 +16,7 @@ from tenacity import (
  
 
 @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
-def openai_chatgpt(prompt):
+def openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp):
     """
     Wrapper function for OpenAI's ChatGPT completion.
 
@@ -34,26 +34,16 @@ def openai_chatgpt(prompt):
     Raises:
         SystemExit: If an API error, connection error, or rate limit error occurs.
     """
-    try:
-        config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'main_config'))
-
-        config = configparser.ConfigParser()
-        config.read(config_path)
-
-        model = config.get('llm_options', 'model')
-        temperature = config.getfloat('llm_options', 'temperature')
-        max_tokens = config.getint('llm_options', 'max_tokens')
-        top_p = config.getfloat('llm_options', 'top_p')
-        n = config.getint('llm_options', 'n')
-        fp = config.getfloat('llm_options', 'frequency_penalty')
-    except Exception as err:
-        logger.error(f"Unable to read Openai parameters from config file:{err}")
-    
     # Wait for 10 seconds to comply with rate limits
     for _ in range(5):
         time.sleep(1)
 
     try:
+        # Create variables to collect the stream of chunks
+        collected_chunks = []
+        collected_messages = []
+        full_reply_content = None
+        
         client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
         response = client.chat.completions.create(
             model=model,
@@ -65,17 +55,15 @@ def openai_chatgpt(prompt):
             frequency_penalty=fp
             # Additional parameters can be included here
         )
-        # create variables to collect the stream of chunks
-        collected_chunks = []
-        collected_messages = []
-        # iterate through the stream of events
+        
+        # Iterate through the stream of events
         for chunk in response:
             collected_chunks.append(chunk)  # save the event response
             chunk_message = chunk.choices[0].delta.content  # extract the message
             collected_messages.append(chunk_message)  # save the message
             print(chunk.choices[0].delta.content, end = "", flush = True)
         
-        # clean None in collected_messages
+        # Clean None in collected_messages
         collected_messages = [m for m in collected_messages if m is not None]
         full_reply_content = ''.join([m for m in collected_messages])
         return full_reply_content