Update App_Function_Libraries/LLM_API_Calls.py

App_Function_Libraries/LLM_API_Calls.py

@@ -1,977 +1,966 @@
-# Summarization_General_Lib.py
-#########################################
-# General Summarization Library
-# This library is used to perform summarization.
-#
-####
-####################
-# Function List
-#
-# 1. extract_text_from_segments(segments: List[Dict]) -> str
-# 2. chat_with_openai(api_key, file_path, custom_prompt_arg)
-# 3. chat_with_anthropic(api_key, file_path, model, custom_prompt_arg, max_retries=3, retry_delay=5)
-# 4. chat_with_cohere(api_key, file_path, model, custom_prompt_arg)
-# 5. chat_with_groq(api_key, input_data, custom_prompt_arg, system_prompt=None):
-# 6. chat_with_openrouter(api_key, input_data, custom_prompt_arg, system_prompt=None)
-# 7. chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None)
-# 8. chat_with_deepseek(api_key, input_data, custom_prompt_arg, system_prompt=None)
-# 9. chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None)
-#
-#
-####################
-#
-# Import necessary libraries
-import json
-import logging
-import os
-import time
-from typing import List
-
-import requests
-#
-# Import 3rd-Party Libraries
-from requests import RequestException
-#
-# Import Local libraries
-from App_Function_Libraries.Utils.Utils import load_and_log_configs
-#
-#######################################################################################################################
-# Function Definitions
-#
-
-#FIXME: Update to include full arguments
-
-def extract_text_from_segments(segments):
-    logging.debug(f"Segments received: {segments}")
-    logging.debug(f"Type of segments: {type(segments)}")
-
-    text = ""
-
-    if isinstance(segments, list):
-        for segment in segments:
-            logging.debug(f"Current segment: {segment}")
-            logging.debug(f"Type of segment: {type(segment)}")
-            if 'Text' in segment:
-                text += segment['Text'] + " "
-            else:
-                logging.warning(f"Skipping segment due to missing 'Text' key: {segment}")
-    else:
-        logging.warning(f"Unexpected type of 'segments': {type(segments)}")
-
-    return text.strip()
-
-
-
-def get_openai_embeddings(input_data: str, model: str) -> List[float]:
-    """
-    Get embeddings for the input text from OpenAI API.
-
-    Args:
-        input_data (str): The input text to get embeddings for.
-        model (str): The model to use for generating embeddings.
-
-    Returns:
-        List[float]: The embeddings generated by the API.
-    """
-    loaded_config_data = load_and_log_configs()
-    api_key = loaded_config_data['api_keys']['openai']
-
-    if not api_key:
-        logging.error("OpenAI: API key not found or is empty")
-        raise ValueError("OpenAI: API Key Not Provided/Found in Config file or is empty")
-
-    logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
-    logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
-    logging.debug(f"OpenAI: Using model: {model}")
-
-    headers = {
-        'Authorization': f'Bearer {api_key}',
-        'Content-Type': 'application/json'
-    }
-
-    request_data = {
-        "input": input_data,
-        "model": model,
-    }
-
-    try:
-        logging.debug("OpenAI: Posting request to embeddings API")
-        response = requests.post('https://api.openai.com/v1/embeddings', headers=headers, json=request_data)
-        logging.debug(f"Full API response data: {response}")
-        if response.status_code == 200:
-            response_data = response.json()
-            if 'data' in response_data and len(response_data['data']) > 0:
-                embedding = response_data['data'][0]['embedding']
-                logging.debug("OpenAI: Embeddings retrieved successfully")
-                return embedding
-            else:
-                logging.warning("OpenAI: Embedding data not found in the response")
-                raise ValueError("OpenAI: Embedding data not available in the response")
-        else:
-            logging.error(f"OpenAI: Embeddings request failed with status code {response.status_code}")
-            logging.error(f"OpenAI: Error response: {response.text}")
-            raise ValueError(f"OpenAI: Failed to retrieve embeddings. Status code: {response.status_code}")
-    except requests.RequestException as e:
-        logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
-        raise ValueError(f"OpenAI: Error making API request: {str(e)}")
-    except Exception as e:
-        logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
-        raise ValueError(f"OpenAI: Unexpected error occurred: {str(e)}")
-
-
-def chat_with_openai(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
-    loaded_config_data = load_and_log_configs()
-    openai_api_key = api_key
-    try:
-        # API key validation
-        if not openai_api_key:
-            logging.info("OpenAI: API key not provided as parameter")
-            logging.info("OpenAI: Attempting to use API key from config file")
-            openai_api_key = loaded_config_data['api_keys']['openai']
-
-        if not openai_api_key:
-            logging.error("OpenAI: API key not found or is empty")
-            return "OpenAI: API Key Not Provided/Found in Config file or is empty"
-
-        logging.debug(f"OpenAI: Using API Key: {openai_api_key[:5]}...{openai_api_key[-5:]}")
-
-        # Input data handling
-        logging.debug(f"OpenAI: Raw input data type: {type(input_data)}")
-        logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
-
-        if isinstance(input_data, str):
-            if input_data.strip().startswith('{'):
-                # It's likely a JSON string
-                logging.debug("OpenAI: Parsing provided JSON string data for summarization")
-                try:
-                    data = json.loads(input_data)
-                except json.JSONDecodeError as e:
-                    logging.error(f"OpenAI: Error parsing JSON string: {str(e)}")
-                    return f"OpenAI: Error parsing JSON input: {str(e)}"
-            elif os.path.isfile(input_data):
-                logging.debug("OpenAI: Loading JSON data from file for summarization")
-                with open(input_data, 'r') as file:
-                    data = json.load(file)
-            else:
-                logging.debug("OpenAI: Using provided string data for summarization")
-                data = input_data
-        else:
-            data = input_data
-
-        logging.debug(f"OpenAI: Processed data type: {type(data)}")
-        logging.debug(f"OpenAI: Processed data (first 500 chars): {str(data)[:500]}...")
-
-        # Text extraction
-        if isinstance(data, dict):
-            if 'summary' in data:
-                logging.debug("OpenAI: Summary already exists in the loaded data")
-                return data['summary']
-            elif 'segments' in data:
-                text = extract_text_from_segments(data['segments'])
-            else:
-                text = json.dumps(data)  # Convert dict to string if no specific format
-        elif isinstance(data, list):
-            text = extract_text_from_segments(data)
-        elif isinstance(data, str):
-            text = data
-        else:
-            raise ValueError(f"OpenAI: Invalid input data format: {type(data)}")
-
-        logging.debug(f"OpenAI: Extracted text (first 500 chars): {text[:500]}...")
-        logging.debug(f"OpenAI: Custom prompt: {custom_prompt_arg}")
-
-        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
-        logging.debug(f"OpenAI: Using model: {openai_model}")
-
-        headers = {
-            'Authorization': f'Bearer {openai_api_key}',
-            'Content-Type': 'application/json'
-        }
-
-        logging.debug(
-            f"OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
-        logging.debug("openai: Preparing data + prompt for submittal")
-        openai_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
-        if temp is None:
-            temp = 0.7
-        if system_message is None:
-            system_message = "You are a helpful AI assistant who does whatever the user requests."
-        temp = float(temp)
-        data = {
-            "model": openai_model,
-            "messages": [
-                {"role": "system", "content": system_message},
-                {"role": "user", "content": openai_prompt}
-            ],
-            "max_tokens": 4096,
-            "temperature": temp
-        }
-
-        logging.debug("OpenAI: Posting request")
-        response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
-        logging.debug(f"Full API response data: {response}")
-        if response.status_code == 200:
-            response_data = response.json()
-            logging.debug(response_data)
-            if 'choices' in response_data and len(response_data['choices']) > 0:
-                chat_response = response_data['choices'][0]['message']['content'].strip()
-                logging.debug("openai: Chat Sent successfully")
-                logging.debug(f"openai: Chat response: {chat_response}")
-                return chat_response
-            else:
-                logging.warning("openai: Chat response not found in the response data")
-                return "openai: Chat not available"
-        else:
-            logging.error(f"OpenAI: Chat request failed with status code {response.status_code}")
-            logging.error(f"OpenAI: Error response: {response.text}")
-            return f"OpenAI: Failed to process chat response. Status code: {response.status_code}"
-    except json.JSONDecodeError as e:
-        logging.error(f"OpenAI: Error decoding JSON: {str(e)}", exc_info=True)
-        return f"OpenAI: Error decoding JSON input: {str(e)}"
-    except requests.RequestException as e:
-        logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
-        return f"OpenAI: Error making API request: {str(e)}"
-    except Exception as e:
-        logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
-        return f"OpenAI: Unexpected error occurred: {str(e)}"
-
-
-def chat_with_anthropic(api_key, input_data, model, custom_prompt_arg, max_retries=3, retry_delay=5, system_prompt=None):
-    try:
-        loaded_config_data = load_and_log_configs()
-        global anthropic_api_key
-        anthropic_api_key = api_key
-        # API key validation
-        if not api_key:
-            logging.info("Anthropic: API key not provided as parameter")
-            logging.info("Anthropic: Attempting to use API key from config file")
-            anthropic_api_key = loaded_config_data['api_keys']['anthropic']
-
-        if not api_key or api_key.strip() == "":
-            logging.error("Anthropic: API key not found or is empty")
-            return "Anthropic: API Key Not Provided/Found in Config file or is empty"
-
-        logging.debug(f"Anthropic: Using API Key: {api_key[:5]}...{api_key[-5:]}")
-
-        if system_prompt is not None:
-            logging.debug("Anthropic: Using provided system prompt")
-            pass
-        else:
-            system_prompt = "You are a helpful assistant"
-
-        logging.debug(f"AnthropicAI: Loaded data: {input_data}")
-        logging.debug(f"AnthropicAI: Type of data: {type(input_data)}")
-
-        anthropic_model = loaded_config_data['models']['anthropic']
-
-        headers = {
-            'x-api-key': anthropic_api_key,
-            'anthropic-version': '2023-06-01',
-            'Content-Type': 'application/json'
-        }
-
-        anthropic_user_prompt = custom_prompt_arg
-        logging.debug(f"Anthropic: User Prompt is {anthropic_user_prompt}")
-        user_message = {
-            "role": "user",
-            "content": f"{input_data} \n\n\n\n{anthropic_user_prompt}"
-        }
-
-        data = {
-            "model": model,
-            "max_tokens": 4096,  # max _possible_ tokens to return
-            "messages": [user_message],
-            "stop_sequences": ["\n\nHuman:"],
-            "temperature": 0.1,
-            "top_k": 0,
-            "top_p": 1.0,
-            "metadata": {
-                "user_id": "example_user_id",
-            },
-            "stream": False,
-            "system": f"{system_prompt}"
-        }
-
-        for attempt in range(max_retries):
-            try:
-                logging.debug("anthropic: Posting request to API")
-                response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
-                logging.debug(f"Full API response data: {response}")
-                # Check if the status code indicates success
-                if response.status_code == 200:
-                    logging.debug("anthropic: Post submittal successful")
-                    response_data = response.json()
-                    try:
-                        chat_response = response_data['content'][0]['text'].strip()
-                        logging.debug("anthropic: Chat request successful")
-                        print("Chat request processed successfully.")
-                        return chat_response
-                    except (IndexError, KeyError) as e:
-                        logging.debug("anthropic: Unexpected data in response")
-                        print("Unexpected response format from Anthropic API:", response.text)
-                        return None
-                elif response.status_code == 500:  # Handle internal server error specifically
-                    logging.debug("anthropic: Internal server error")
-                    print("Internal server error from API. Retrying may be necessary.")
-                    time.sleep(retry_delay)
-                else:
-                    logging.debug(
-                        f"anthropic: Failed to process chat request, status code {response.status_code}: {response.text}")
-                    print(f"Failed to process chat request, status code {response.status_code}: {response.text}")
-                    return None
-
-            except RequestException as e:
-                logging.error(f"anthropic: Network error during attempt {attempt + 1}/{max_retries}: {str(e)}")
-                if attempt < max_retries - 1:
-                    time.sleep(retry_delay)
-                else:
-                    return f"anthropic: Network error: {str(e)}"
-    except Exception as e:
-        logging.error(f"anthropic: Error in processing: {str(e)}")
-        return f"anthropic: Error occurred while processing summary with Anthropic: {str(e)}"
-
-
-# Summarize with Cohere
-def chat_with_cohere(api_key, input_data, model, custom_prompt_arg, system_prompt=None):
-    loaded_config_data = load_and_log_configs()
-    if api_key is not None:
-        logging.debug(f"Cohere Chat: API Key from parameter: {api_key[:3]}...{api_key[-3:]}")
-    logging.debug(f"Cohere Chat: Cohere API Key from config: {loaded_config_data['api_keys']['cohere']}")
-    try:
-        # API key validation
-        if api_key is None:
-            logging.info("Cohere Chat: API key not provided as parameter")
-            logging.info("Cohere Chat: Attempting to use API key from config file")
-            cohere_api_key = loaded_config_data.get('api_keys', {}).get('cohere')
-            if not cohere_api_key:
-                logging.error("Cohere Chat: API key not found or is empty")
-                return "Cohere Chat: API Key Not Provided/Found in Config file or is empty"
-
-        logging.debug(f"Cohere Chat: Using API Key: {cohere_api_key[:3]}...{cohere_api_key[-3:]}")
-
-        logging.debug(f"Cohere Chat: Loaded data: {input_data}")
-        logging.debug(f"Cohere Chat: Type of data: {type(input_data)}")
-
-        # Ensure model is set
-        if not model:
-            model = loaded_config_data['models']['cohere']
-        logging.debug(f"Cohere Chat: Using model: {model}")
-
-        headers = {
-            'accept': 'application/json',
-            'content-type': 'application/json',
-            'Authorization': f'Bearer {cohere_api_key}'
-        }
-
-        # Ensure system_prompt is set
-        if not system_prompt:
-            system_prompt = "You are a helpful assistant"
-        logging.debug(f"Cohere Chat: System Prompt being sent is: '{system_prompt}'")
-
-        cohere_prompt = input_data
-        if custom_prompt_arg:
-            cohere_prompt += f"\n\n{custom_prompt_arg}"
-        logging.debug(f"Cohere Chat: User Prompt being sent is: '{cohere_prompt}'")
-
-        data = {
-            "chat_history": [
-                {"role": "SYSTEM", "message": system_prompt},
-            ],
-            "message": cohere_prompt,
-            "model": model,
-            "connectors": [{"id": "web-search"}]
-        }
-        logging.debug(f"Cohere Chat: Request data: {json.dumps(data, indent=2)}")
-
-        logging.debug("cohere chat: Submitting request to API endpoint")
-        print("cohere chat: Submitting request to API endpoint")
-
-        try:
-            response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data)
-            logging.debug(f"Cohere Chat: Raw API response: {response.text}")
-        except requests.RequestException as e:
-            logging.error(f"Cohere Chat: Error making API request: {str(e)}")
-            return f"Cohere Chat: Error making API request: {str(e)}"
-
-        if response.status_code == 200:
-            try:
-                response_data = response.json()
-            except json.JSONDecodeError:
-                logging.error("Cohere Chat: Failed to decode JSON response")
-                return "Cohere Chat: Failed to decode JSON response"
-
-            if response_data is None:
-                logging.error("Cohere Chat: No response data received.")
-                return "Cohere Chat: No response data received."
-
-            logging.debug(f"cohere chat: Full API response data: {json.dumps(response_data, indent=2)}")
-
-            if 'text' in response_data:
-                chat_response = response_data['text'].strip()
-                logging.debug("Cohere Chat: Chat request successful")
-                print("Cohere Chat request processed successfully.")
-                return chat_response
-            else:
-                logging.error("Cohere Chat: Expected 'text' key not found in API response.")
-                return "Cohere Chat: Expected data not found in API response."
-        else:
-            logging.error(f"Cohere Chat: API request failed with status code {response.status_code}: {response.text}")
-            print(f"Cohere Chat: Failed to process chat response, status code {response.status_code}: {response.text}")
-            return f"Cohere Chat: API request failed: {response.text}"
-
-    except Exception as e:
-        logging.error(f"Cohere Chat: Error in processing: {str(e)}", exc_info=True)
-        return f"Cohere Chat: Error occurred while processing chat request with Cohere: {str(e)}"
-
-
-# https://console.groq.com/docs/quickstart
-def chat_with_groq(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
-    logging.debug("Groq: Summarization process starting...")
-    try:
-        logging.debug("Groq: Loading and validating configurations")
-        loaded_config_data = load_and_log_configs()
-        if loaded_config_data is None:
-            logging.error("Failed to load configuration data")
-            groq_api_key = None
-        else:
-            # Prioritize the API key passed as a parameter
-            if api_key and api_key.strip():
-                groq_api_key = api_key
-                logging.info("Groq: Using API key provided as parameter")
-            else:
-                # If no parameter is provided, use the key from the config
-                groq_api_key = loaded_config_data['api_keys'].get('groq')
-                if groq_api_key:
-                    logging.info("Groq: Using API key from config file")
-                else:
-                    logging.warning("Groq: No API key found in config file")
-
-        # Final check to ensure we have a valid API key
-        if not groq_api_key or not groq_api_key.strip():
-            logging.error("Anthropic: No valid API key available")
-            # You might want to raise an exception here or handle this case as appropriate for your application
-            # For example: raise ValueError("No valid Anthropic API key available")
-
-        logging.debug(f"Groq: Using API Key: {groq_api_key[:5]}...{groq_api_key[-5:]}")
-
-        # Transcript data handling & Validation
-        if isinstance(input_data, str) and os.path.isfile(input_data):
-            logging.debug("Groq: Loading json data for summarization")
-            with open(input_data, 'r') as file:
-                data = json.load(file)
-        else:
-            logging.debug("Groq: Using provided string data for summarization")
-            data = input_data
-
-        # DEBUG - Debug logging to identify sent data
-        logging.debug(f"Groq: Loaded data: {data[:500]}...(snipped to first 500 chars)")
-        logging.debug(f"Groq: Type of data: {type(data)}")
-
-        if isinstance(data, dict) and 'summary' in data:
-            # If the loaded data is a dictionary and already contains a summary, return it
-            logging.debug("Groq: Summary already exists in the loaded data")
-            return data['summary']
-
-        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
-        if isinstance(data, list):
-            segments = data
-            text = extract_text_from_segments(segments)
-        elif isinstance(data, str):
-            text = data
-        else:
-            raise ValueError("Groq: Invalid input data format")
-
-        # Set the model to be used
-        groq_model = loaded_config_data['models']['groq']
-
-        if temp is None:
-            temp = 0.2
-        temp = float(temp)
-        if system_message is None:
-            system_message = "You are a helpful AI assistant who does whatever the user requests."
-
-        headers = {
-            'Authorization': f'Bearer {groq_api_key}',
-            'Content-Type': 'application/json'
-        }
-
-        groq_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
-        logging.debug("groq: Prompt being sent is {groq_prompt}")
-
-        data = {
-            "messages": [
-                {
-                    "role": "system",
-                    "content": system_message,
-                },
-                {
-                    "role": "user",
-                    "content": groq_prompt,
-                }
-            ],
-            "model": groq_model,
-            "temperature": temp
-        }
-
-        logging.debug("groq: Submitting request to API endpoint")
-        print("groq: Submitting request to API endpoint")
-        response = requests.post('https://api.groq.com/openai/v1/chat/completions', headers=headers, json=data)
-
-        response_data = response.json()
-        logging.debug(f"Full API response data: {response_data}")
-
-        if response.status_code == 200:
-            logging.debug(response_data)
-            if 'choices' in response_data and len(response_data['choices']) > 0:
-                summary = response_data['choices'][0]['message']['content'].strip()
-                logging.debug("groq: Chat request successful")
-                print("Groq: Chat request successful.")
-                return summary
-            else:
-                logging.error("Groq(chat): Expected data not found in API response.")
-                return "Groq(chat): Expected data not found in API response."
-        else:
-            logging.error(f"groq: API request failed with status code {response.status_code}: {response.text}")
-            return f"groq: API request failed: {response.text}"
-
-    except Exception as e:
-        logging.error("groq: Error in processing: %s", str(e))
-        return f"groq: Error occurred while processing summary with groq: {str(e)}"
-
-
-def chat_with_openrouter(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
-    import requests
-    import json
-    global openrouter_model, openrouter_api_key
-    try:
-        logging.debug("OpenRouter: Loading and validating configurations")
-        loaded_config_data = load_and_log_configs()
-        if loaded_config_data is None:
-            logging.error("Failed to load configuration data")
-            openrouter_api_key = None
-        else:
-            # Prioritize the API key passed as a parameter
-            if api_key and api_key.strip():
-                openrouter_api_key = api_key
-                logging.info("OpenRouter: Using API key provided as parameter")
-            else:
-                # If no parameter is provided, use the key from the config
-                openrouter_api_key = loaded_config_data['api_keys'].get('openrouter')
-                if openrouter_api_key:
-                    logging.info("OpenRouter: Using API key from config file")
-                else:
-                    logging.warning("OpenRouter: No API key found in config file")
-
-        # Model Selection validation
-        logging.debug("OpenRouter: Validating model selection")
-        loaded_config_data = load_and_log_configs()
-        openrouter_model = loaded_config_data['models']['openrouter']
-        logging.debug(f"OpenRouter: Using model from config file: {openrouter_model}")
-
-        # Final check to ensure we have a valid API key
-        if not openrouter_api_key or not openrouter_api_key.strip():
-            logging.error("OpenRouter: No valid API key available")
-            raise ValueError("No valid Anthropic API key available")
-    except Exception as e:
-        logging.error("OpenRouter: Error in processing: %s", str(e))
-        return f"OpenRouter: Error occurred while processing config file with OpenRouter: {str(e)}"
-
-    logging.debug(f"OpenRouter: Using API Key: {openrouter_api_key[:5]}...{openrouter_api_key[-5:]}")
-
-    logging.debug(f"OpenRouter: Using Model: {openrouter_model}")
-
-    if isinstance(input_data, str) and os.path.isfile(input_data):
-        logging.debug("OpenRouter: Loading json data for summarization")
-        with open(input_data, 'r') as file:
-            data = json.load(file)
-    else:
-        logging.debug("OpenRouter: Using provided string data for summarization")
-        data = input_data
-
-    # DEBUG - Debug logging to identify sent data
-    logging.debug(f"OpenRouter: Loaded data: {data[:500]}...(snipped to first 500 chars)")
-    logging.debug(f"OpenRouter: Type of data: {type(data)}")
-
-    if isinstance(data, dict) and 'summary' in data:
-        # If the loaded data is a dictionary and already contains a summary, return it
-        logging.debug("OpenRouter: Summary already exists in the loaded data")
-        return data['summary']
-
-    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
-    if isinstance(data, list):
-        segments = data
-        text = extract_text_from_segments(segments)
-    elif isinstance(data, str):
-        text = data
-    else:
-        raise ValueError("OpenRouter: Invalid input data format")
-
-    openrouter_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
-    logging.debug(f"openrouter: User Prompt being sent is {openrouter_prompt}")
-
-    if temp is None:
-        temp = 0.1
-    temp = float(temp)
-    if system_message is None:
-        system_message = "You are a helpful AI assistant who does whatever the user requests."
-
-    try:
-        logging.debug("OpenRouter: Submitting request to API endpoint")
-        print("OpenRouter: Submitting request to API endpoint")
-        response = requests.post(
-            url="https://openrouter.ai/api/v1/chat/completions",
-            headers={
-                "Authorization": f"Bearer {openrouter_api_key}",
-            },
-            data=json.dumps({
-                "model": openrouter_model,
-                "messages": [
-                    {"role": "system", "content": system_message},
-                    {"role": "user", "content": openrouter_prompt}
-                ],
-                "temperature": temp
-            })
-        )
-
-        response_data = response.json()
-        logging.debug("Full API Response Data: %s", response_data)
-
-        if response.status_code == 200:
-            if 'choices' in response_data and len(response_data['choices']) > 0:
-                summary = response_data['choices'][0]['message']['content'].strip()
-                logging.debug("openrouter: Chat request successful")
-                print("openrouter: Chat request successful.")
-                return summary
-            else:
-                logging.error("openrouter: Expected data not found in API response.")
-                return "openrouter: Expected data not found in API response."
-        else:
-            logging.error(f"openrouter:  API request failed with status code {response.status_code}: {response.text}")
-            return f"openrouter: API request failed: {response.text}"
-    except Exception as e:
-        logging.error("openrouter: Error in processing: %s", str(e))
-        return f"openrouter: Error occurred while processing chat request with openrouter: {str(e)}"
-
-
-# FIXME: This function is not yet implemented properly
-def chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None, temp=None):
-    loaded_config_data = load_and_log_configs()
-    logging.debug(f"huggingface Chat: Chat request process starting...")
-    try:
-        # API key validation
-        if not api_key or api_key.strip() == "":
-            logging.info("HuggingFace Chat: API key not provided as parameter")
-            logging.info("HuggingFace Chat: Attempting to use API key from config file")
-
-        huggingface_api_key = loaded_config_data['api_keys'].get('huggingface')
-        logging.debug(f"HuggingFace Chat: API key from config: {huggingface_api_key[:5]}...{huggingface_api_key[-5:]}")
-
-        if huggingface_api_key is None or huggingface_api_key.strip() == "":
-            logging.error("HuggingFace Chat: API key not found or is empty")
-            return "HuggingFace Chat: API Key Not Provided/Found in Config file or is empty"
-        if huggingface_api_key:
-            logging.info("HuggingFace Chat: Using API key from config file")
-        headers = {
-            "Authorization": f"Bearer {huggingface_api_key}"
-        }
-
-        # Setup model
-        huggingface_model = loaded_config_data['models']['huggingface']
-
-        API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}/v1/chat/completions"
-        if temp is None:
-            temp = 1.0
-        temp = float(temp)
-        huggingface_prompt = f"{custom_prompt_arg}\n\n\n{input_data}"
-        logging.debug(f"HuggingFace chat: Prompt being sent is {huggingface_prompt}")
-        data = {
-            "model": f"{huggingface_model}",
-            "messages": [{"role": "user", "content": f"{huggingface_prompt}"}],
-            "max_tokens": 4096,
-            "stream": False,
-            "temperature": temp
-        }
-
-        logging.debug("HuggingFace Chat: Submitting request...")
-        response = requests.post(API_URL, headers=headers, json=data)
-        logging.debug(f"Full API response data: {response.text}")
-
-        if response.status_code == 200:
-            response_json = response.json()
-            if "choices" in response_json and len(response_json["choices"]) > 0:
-                generated_text = response_json["choices"][0]["message"]["content"]
-                logging.debug("HuggingFace Chat: Chat request successful")
-                print("HuggingFace Chat: Chat request successful.")
-                return generated_text.strip()
-            else:
-                logging.error("HuggingFace Chat: No generated text in the response")
-                return "HuggingFace Chat: No generated text in the response"
-        else:
-            logging.error(
-                f"HuggingFace Chat: Chat request failed with status code {response.status_code}: {response.text}")
-            return f"HuggingFace Chat: Failed to process chat request, status code {response.status_code}: {response.text}"
-    except Exception as e:
-        logging.error(f"HuggingFace Chat: Error in processing: {str(e)}")
-        print(f"HuggingFace Chat: Error occurred while processing chat request with huggingface: {str(e)}")
-        return None
-
-
-def chat_with_deepseek(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
-    logging.debug("DeepSeek: Summarization process starting...")
-    try:
-        logging.debug("DeepSeek: Loading and validating configurations")
-        loaded_config_data = load_and_log_configs()
-        if loaded_config_data is None:
-            logging.error("Failed to load configuration data")
-            deepseek_api_key = None
-        else:
-            # Prioritize the API key passed as a parameter
-            if api_key and api_key.strip():
-                deepseek_api_key = api_key
-                logging.info("DeepSeek: Using API key provided as parameter")
-            else:
-                # If no parameter is provided, use the key from the config
-                deepseek_api_key = loaded_config_data['api_keys'].get('deepseek')
-                if deepseek_api_key:
-                    logging.info("DeepSeek: Using API key from config file")
-                else:
-                    logging.warning("DeepSeek: No API key found in config file")
-
-        # Final check to ensure we have a valid API key
-        if not deepseek_api_key or not deepseek_api_key.strip():
-            logging.error("DeepSeek: No valid API key available")
-            # You might want to raise an exception here or handle this case as appropriate for your application
-            # For example: raise ValueError("No valid deepseek API key available")
-
-
-        logging.debug(f"DeepSeek: Using API Key: {deepseek_api_key[:5]}...{deepseek_api_key[-5:]}")
-
-        # Input data handling
-        if isinstance(input_data, str) and os.path.isfile(input_data):
-            logging.debug("DeepSeek: Loading json data for summarization")
-            with open(input_data, 'r') as file:
-                data = json.load(file)
-        else:
-            logging.debug("DeepSeek: Using provided string data for summarization")
-            data = input_data
-
-        # DEBUG - Debug logging to identify sent data
-        logging.debug(f"DeepSeek: Loaded data: {data[:500]}...(snipped to first 500 chars)")
-        logging.debug(f"DeepSeek: Type of data: {type(data)}")
-
-        if isinstance(data, dict) and 'summary' in data:
-            # If the loaded data is a dictionary and already contains a summary, return it
-            logging.debug("DeepSeek: Summary already exists in the loaded data")
-            return data['summary']
-
-        # Text extraction
-        if isinstance(data, list):
-            segments = data
-            text = extract_text_from_segments(segments)
-        elif isinstance(data, str):
-            text = data
-        else:
-            raise ValueError("DeepSeek: Invalid input data format")
-
-        deepseek_model = loaded_config_data['models']['deepseek'] or "deepseek-chat"
-
-        if temp is None:
-            temp = 0.1
-        temp = float(temp)
-        if system_message is None:
-            system_message = "You are a helpful AI assistant who does whatever the user requests."
-
-        headers = {
-            'Authorization': f'Bearer {api_key}',
-            'Content-Type': 'application/json'
-        }
-
-        logging.debug(
-            f"Deepseek API Key: {api_key[:5]}...{api_key[-5:] if api_key else None}")
-        logging.debug("DeepSeek: Preparing data + prompt for submittal")
-        deepseek_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
-        data = {
-            "model": deepseek_model,
-            "messages": [
-                {"role": "system", "content": system_message},
-                {"role": "user", "content": deepseek_prompt}
-            ],
-            "stream": False,
-            "temperature": temp
-        }
-
-        logging.debug("DeepSeek: Posting request")
-        response = requests.post('https://api.deepseek.com/chat/completions', headers=headers, json=data)
-        logging.debug(f"Full API response data: {response}")
-        if response.status_code == 200:
-            response_data = response.json()
-            logging.debug(response_data)
-            if 'choices' in response_data and len(response_data['choices']) > 0:
-                summary = response_data['choices'][0]['message']['content'].strip()
-                logging.debug("DeepSeek: Chat request successful")
-                return summary
-            else:
-                logging.warning("DeepSeek: Chat response not found in the response data")
-                return "DeepSeek: Chat response not available"
-        else:
-            logging.error(f"DeepSeek: Chat request failed with status code {response.status_code}")
-            logging.error(f"DeepSeek: Error response: {response.text}")
-            return f"DeepSeek: Failed to chat request summary. Status code: {response.status_code}"
-    except Exception as e:
-        logging.error(f"DeepSeek: Error in processing: {str(e)}", exc_info=True)
-        return f"DeepSeek: Error occurred while processing chat request: {str(e)}"
-
-
-def chat_with_mistral(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
-    logging.debug("Mistral: Chat request made")
-    try:
-        logging.debug("Mistral: Loading and validating configurations")
-        loaded_config_data = load_and_log_configs()
-        if loaded_config_data is None:
-            logging.error("Failed to load configuration data")
-            mistral_api_key = None
-        else:
-            # Prioritize the API key passed as a parameter
-            if api_key and api_key.strip():
-                mistral_api_key = api_key
-                logging.info("Mistral: Using API key provided as parameter")
-            else:
-                # If no parameter is provided, use the key from the config
-                mistral_api_key = loaded_config_data['api_keys'].get('mistral')
-                if mistral_api_key:
-                    logging.info("Mistral: Using API key from config file")
-                else:
-                    logging.warning("Mistral: No API key found in config file")
-
-        # Final check to ensure we have a valid API key
-        if not mistral_api_key or not mistral_api_key.strip():
-            logging.error("Mistral: No valid API key available")
-            return "Mistral: No valid API key available"
-
-        logging.debug(f"Mistral: Using API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:]}")
-
-        logging.debug("Mistral: Using provided string data")
-        data = input_data
-
-        # Text extraction
-        if isinstance(input_data, list):
-            text = extract_text_from_segments(input_data)
-        elif isinstance(input_data, str):
-            text = input_data
-        else:
-            raise ValueError("Mistral: Invalid input data format")
-
-        mistral_model = loaded_config_data['models'].get('mistral', "mistral-large-latest")
-
-        temp = float(temp) if temp is not None else 0.2
-        if system_message is None:
-            system_message = "You are a helpful AI assistant who does whatever the user requests."
-
-        headers = {
-            'Authorization': f'Bearer {mistral_api_key}',
-            'Content-Type': 'application/json'
-        }
-
-        logging.debug(
-            f"Deepseek API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:] if mistral_api_key else None}")
-        logging.debug("Mistral: Preparing data + prompt for submittal")
-        mistral_prompt = f"{custom_prompt_arg}\n\n\n\n{text} "
-        data = {
-            "model": mistral_model,
-            "messages": [
-                {"role": "system",
-                 "content": system_message},
-                {"role": "user",
-                "content": mistral_prompt}
-            ],
-            "temperature": temp,
-            "top_p": 1,
-            "max_tokens": 4096,
-            "stream": False,
-            "safe_prompt": False
-        }
-
-        logging.debug("Mistral: Posting request")
-        response = requests.post('https://api.mistral.ai/v1/chat/completions', headers=headers, json=data)
-        logging.debug(f"Full API response data: {response}")
-        if response.status_code == 200:
-            response_data = response.json()
-            logging.debug(response_data)
-            if 'choices' in response_data and len(response_data['choices']) > 0:
-                summary = response_data['choices'][0]['message']['content'].strip()
-                logging.debug("Mistral: request successful")
-                return summary
-            else:
-                logging.warning("Mistral: Chat response not found in the response data")
-                return "Mistral: Chat response not available"
-        else:
-            logging.error(f"Mistral: Chat request failed with status code {response.status_code}")
-            logging.error(f"Mistral: Error response: {response.text}")
-            return f"Mistral: Failed to process summary. Status code: {response.status_code}. Error: {response.text}"
-    except Exception as e:
-        logging.error(f"Mistral: Error in processing: {str(e)}", exc_info=True)
-        return f"Mistral: Error occurred while processing Chat: {str(e)}"
-
-
-
-# Stashed in here since OpenAI usage.... #FIXME
-# FIXME - https://docs.vllm.ai/en/latest/getting_started/quickstart.html .... Great docs.
-# def chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None):
-#     loaded_config_data = load_and_log_configs()
-#     llm_model = loaded_config_data['models']['vllm']
-#     # API key validation
-#     if api_key is None:
-#         logging.info("vLLM: API key not provided as parameter")
-#         logging.info("vLLM: Attempting to use API key from config file")
-#         api_key = loaded_config_data['api_keys']['llama']
-#
-#     if api_key is None or api_key.strip() == "":
-#         logging.info("vLLM: API key not found or is empty")
-#     vllm_client = OpenAI(
-#         base_url=vllm_api_url,
-#         api_key=custom_prompt_input
-#     )
-#
-#     if isinstance(input_data, str) and os.path.isfile(input_data):
-#         logging.debug("vLLM: Loading json data for summarization")
-#         with open(input_data, 'r') as file:
-#             data = json.load(file)
-#     else:
-#         logging.debug("vLLM: Using provided string data for summarization")
-#         data = input_data
-#
-#     logging.debug(f"vLLM: Loaded data: {data}")
-#     logging.debug(f"vLLM: Type of data: {type(data)}")
-#
-#     if isinstance(data, dict) and 'summary' in data:
-#         # If the loaded data is a dictionary and already contains a summary, return it
-#         logging.debug("vLLM: Summary already exists in the loaded data")
-#         return data['summary']
-#
-#     # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
-#     if isinstance(data, list):
-#         segments = data
-#         text = extract_text_from_segments(segments)
-#     elif isinstance(data, str):
-#         text = data
-#     else:
-#         raise ValueError("Invalid input data format")
-#
-#
-#     custom_prompt = custom_prompt_input
-#
-#     completion = client.chat.completions.create(
-#         model=llm_model,
-#         messages=[
-#             {"role": "system", "content": f"{system_prompt}"},
-#             {"role": "user", "content": f"{text} \n\n\n\n{custom_prompt}"}
-#         ]
-#     )
-#     vllm_summary = completion.choices[0].message.content
-#     return vllm_summary
-
-
-
-#
-#
+# Summarization_General_Lib.py
+#########################################
+# General Summarization Library
+# This library is used to perform summarization.
+#
+####
+####################
+# Function List
+#
+# 1. extract_text_from_segments(segments: List[Dict]) -> str
+# 2. chat_with_openai(api_key, file_path, custom_prompt_arg)
+# 3. chat_with_anthropic(api_key, file_path, model, custom_prompt_arg, max_retries=3, retry_delay=5)
+# 4. chat_with_cohere(api_key, file_path, model, custom_prompt_arg)
+# 5. chat_with_groq(api_key, input_data, custom_prompt_arg, system_prompt=None):
+# 6. chat_with_openrouter(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 7. chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 8. chat_with_deepseek(api_key, input_data, custom_prompt_arg, system_prompt=None)
+# 9. chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None)
+#
+#
+####################
+#
+# Import necessary libraries
+import json
+import logging
+import os
+import time
+from typing import List
+
+import requests
+#
+# Import 3rd-Party Libraries
+from requests import RequestException
+#
+# Import Local libraries
+from App_Function_Libraries.Utils.Utils import load_and_log_configs
+#
+#######################################################################################################################
+# Function Definitions
+#
+
+#FIXME: Update to include full arguments
+
+def extract_text_from_segments(segments):
+    logging.debug(f"Segments received: {segments}")
+    logging.debug(f"Type of segments: {type(segments)}")
+
+    text = ""
+
+    if isinstance(segments, list):
+        for segment in segments:
+            logging.debug(f"Current segment: {segment}")
+            logging.debug(f"Type of segment: {type(segment)}")
+            if 'Text' in segment:
+                text += segment['Text'] + " "
+            else:
+                logging.warning(f"Skipping segment due to missing 'Text' key: {segment}")
+    else:
+        logging.warning(f"Unexpected type of 'segments': {type(segments)}")
+
+    return text.strip()
+
+
+
+def get_openai_embeddings(input_data: str, model: str) -> List[float]:
+    """
+    Get embeddings for the input text from OpenAI API.
+
+    Args:
+        input_data (str): The input text to get embeddings for.
+        model (str): The model to use for generating embeddings.
+
+    Returns:
+        List[float]: The embeddings generated by the API.
+    """
+    loaded_config_data = load_and_log_configs()
+    api_key = loaded_config_data['api_keys']['openai']
+
+    if not api_key:
+        logging.error("OpenAI: API key not found or is empty")
+        raise ValueError("OpenAI: API Key Not Provided/Found in Config file or is empty")
+
+    logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+    logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
+    logging.debug(f"OpenAI: Using model: {model}")
+
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+
+    request_data = {
+        "input": input_data,
+        "model": model,
+    }
+
+    try:
+        logging.debug("OpenAI: Posting request to embeddings API")
+        response = requests.post('https://api.openai.com/v1/embeddings', headers=headers, json=request_data)
+        logging.debug(f"Full API response data: {response}")
+        if response.status_code == 200:
+            response_data = response.json()
+            if 'data' in response_data and len(response_data['data']) > 0:
+                embedding = response_data['data'][0]['embedding']
+                logging.debug("OpenAI: Embeddings retrieved successfully")
+                return embedding
+            else:
+                logging.warning("OpenAI: Embedding data not found in the response")
+                raise ValueError("OpenAI: Embedding data not available in the response")
+        else:
+            logging.error(f"OpenAI: Embeddings request failed with status code {response.status_code}")
+            logging.error(f"OpenAI: Error response: {response.text}")
+            raise ValueError(f"OpenAI: Failed to retrieve embeddings. Status code: {response.status_code}")
+    except requests.RequestException as e:
+        logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
+        raise ValueError(f"OpenAI: Error making API request: {str(e)}")
+    except Exception as e:
+        logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
+        raise ValueError(f"OpenAI: Unexpected error occurred: {str(e)}")
+
+
+def chat_with_openai(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
+    loaded_config_data = load_and_log_configs()
+    openai_api_key = api_key
+    try:
+        # API key validation
+        if not openai_api_key:
+            logging.info("OpenAI: API key not provided as parameter")
+            logging.info("OpenAI: Attempting to use API key from config file")
+            openai_api_key = loaded_config_data['api_keys']['openai']
+
+        if not openai_api_key:
+            logging.error("OpenAI: API key not found or is empty")
+            return "OpenAI: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"OpenAI: Using API Key: {openai_api_key[:5]}...{openai_api_key[-5:]}")
+
+        # Input data handling
+        logging.debug(f"OpenAI: Raw input data type: {type(input_data)}")
+        logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
+
+        if isinstance(input_data, str):
+            if input_data.strip().startswith('{'):
+                # It's likely a JSON string
+                logging.debug("OpenAI: Parsing provided JSON string data for summarization")
+                try:
+                    data = json.loads(input_data)
+                except json.JSONDecodeError as e:
+                    logging.error(f"OpenAI: Error parsing JSON string: {str(e)}")
+                    return f"OpenAI: Error parsing JSON input: {str(e)}"
+            elif os.path.isfile(input_data):
+                logging.debug("OpenAI: Loading JSON data from file for summarization")
+                with open(input_data, 'r') as file:
+                    data = json.load(file)
+            else:
+                logging.debug("OpenAI: Using provided string data for summarization")
+                data = input_data
+        else:
+            data = input_data
+
+        logging.debug(f"OpenAI: Processed data type: {type(data)}")
+        logging.debug(f"OpenAI: Processed data (first 500 chars): {str(data)[:500]}...")
+
+        # Text extraction
+        if isinstance(data, dict):
+            if 'summary' in data:
+                logging.debug("OpenAI: Summary already exists in the loaded data")
+                return data['summary']
+            elif 'segments' in data:
+                text = extract_text_from_segments(data['segments'])
+            else:
+                text = json.dumps(data)  # Convert dict to string if no specific format
+        elif isinstance(data, list):
+            text = extract_text_from_segments(data)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError(f"OpenAI: Invalid input data format: {type(data)}")
+
+        logging.debug(f"OpenAI: Extracted text (first 500 chars): {text[:500]}...")
+        logging.debug(f"OpenAI: Custom prompt: {custom_prompt_arg}")
+
+        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
+        logging.debug(f"OpenAI: Using model: {openai_model}")
+
+        headers = {
+            'Authorization': f'Bearer {openai_api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
+        logging.debug("openai: Preparing data + prompt for submittal")
+        openai_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        if temp is None:
+            temp = 0.7
+        if system_message is None:
+            system_message = "You are a helpful AI assistant who does whatever the user requests."
+        temp = float(temp)
+        data = {
+            "model": openai_model,
+            "messages": [
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": openai_prompt}
+            ],
+            "max_tokens": 4096,
+            "temperature": temp
+        }
+
+        logging.debug("OpenAI: Posting request")
+        response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
+        logging.debug(f"Full API response data: {response}")
+        if response.status_code == 200:
+            response_data = response.json()
+            logging.debug(response_data)
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                chat_response = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("openai: Chat Sent successfully")
+                logging.debug(f"openai: Chat response: {chat_response}")
+                return chat_response
+            else:
+                logging.warning("openai: Chat response not found in the response data")
+                return "openai: Chat not available"
+        else:
+            logging.error(f"OpenAI: Chat request failed with status code {response.status_code}")
+            logging.error(f"OpenAI: Error response: {response.text}")
+            return f"OpenAI: Failed to process chat response. Status code: {response.status_code}"
+    except json.JSONDecodeError as e:
+        logging.error(f"OpenAI: Error decoding JSON: {str(e)}", exc_info=True)
+        return f"OpenAI: Error decoding JSON input: {str(e)}"
+    except requests.RequestException as e:
+        logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
+        return f"OpenAI: Error making API request: {str(e)}"
+    except Exception as e:
+        logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
+        return f"OpenAI: Unexpected error occurred: {str(e)}"
+
+
+def chat_with_anthropic(api_key, input_data, model, custom_prompt_arg, max_retries=3, retry_delay=5, system_prompt=None):
+    try:
+        loaded_config_data = load_and_log_configs()
+        global anthropic_api_key
+        anthropic_api_key = api_key
+        # API key validation
+        if not api_key:
+            logging.info("Anthropic: API key not provided as parameter")
+            logging.info("Anthropic: Attempting to use API key from config file")
+            anthropic_api_key = loaded_config_data['api_keys']['anthropic']
+
+        if not api_key or api_key.strip() == "":
+            logging.error("Anthropic: API key not found or is empty")
+            return "Anthropic: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Anthropic: Using API Key: {api_key[:5]}...{api_key[-5:]}")
+
+        if system_prompt is not None:
+            logging.debug("Anthropic: Using provided system prompt")
+            pass
+        else:
+            system_prompt = "You are a helpful assistant"
+
+        logging.debug(f"AnthropicAI: Loaded data: {input_data}")
+        logging.debug(f"AnthropicAI: Type of data: {type(input_data)}")
+
+        anthropic_model = loaded_config_data['models']['anthropic']
+
+        headers = {
+            'x-api-key': anthropic_api_key,
+            'anthropic-version': '2023-06-01',
+            'Content-Type': 'application/json'
+        }
+
+        anthropic_user_prompt = custom_prompt_arg
+        logging.debug(f"Anthropic: User Prompt is {anthropic_user_prompt}")
+        user_message = {
+            "role": "user",
+            "content": f"{input_data} \n\n\n\n{anthropic_user_prompt}"
+        }
+
+        data = {
+            "model": model,
+            "max_tokens": 4096,  # max _possible_ tokens to return
+            "messages": [user_message],
+            "stop_sequences": ["\n\nHuman:"],
+            "temperature": 0.1,
+            "top_k": 0,
+            "top_p": 1.0,
+            "metadata": {
+                "user_id": "example_user_id",
+            },
+            "stream": False,
+            "system": f"{system_prompt}"
+        }
+
+        for attempt in range(max_retries):
+            try:
+                logging.debug("anthropic: Posting request to API")
+                response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
+                logging.debug(f"Full API response data: {response}")
+                # Check if the status code indicates success
+                if response.status_code == 200:
+                    logging.debug("anthropic: Post submittal successful")
+                    response_data = response.json()
+                    try:
+                        chat_response = response_data['content'][0]['text'].strip()
+                        logging.debug("anthropic: Chat request successful")
+                        print("Chat request processed successfully.")
+                        return chat_response
+                    except (IndexError, KeyError) as e:
+                        logging.debug("anthropic: Unexpected data in response")
+                        print("Unexpected response format from Anthropic API:", response.text)
+                        return None
+                elif response.status_code == 500:  # Handle internal server error specifically
+                    logging.debug("anthropic: Internal server error")
+                    print("Internal server error from API. Retrying may be necessary.")
+                    time.sleep(retry_delay)
+                else:
+                    logging.debug(
+                        f"anthropic: Failed to process chat request, status code {response.status_code}: {response.text}")
+                    print(f"Failed to process chat request, status code {response.status_code}: {response.text}")
+                    return None
+
+            except RequestException as e:
+                logging.error(f"anthropic: Network error during attempt {attempt + 1}/{max_retries}: {str(e)}")
+                if attempt < max_retries - 1:
+                    time.sleep(retry_delay)
+                else:
+                    return f"anthropic: Network error: {str(e)}"
+    except Exception as e:
+        logging.error(f"anthropic: Error in processing: {str(e)}")
+        return f"anthropic: Error occurred while processing summary with Anthropic: {str(e)}"
+
+
+# Summarize with Cohere
+def chat_with_cohere(api_key, input_data, model, custom_prompt_arg, system_prompt=None):
+    loaded_config_data = load_and_log_configs()
+    if api_key is not None:
+        logging.debug(f"Cohere Chat: API Key from parameter: {api_key[:3]}...{api_key[-3:]}")
+    logging.debug(f"Cohere Chat: Cohere API Key from config: {loaded_config_data['api_keys']['cohere']}")
+    try:
+        # API key validation
+        if api_key is None:
+            logging.info("Cohere Chat: API key not provided as parameter")
+            logging.info("Cohere Chat: Attempting to use API key from config file")
+            cohere_api_key = loaded_config_data.get('api_keys', {}).get('cohere')
+            if not cohere_api_key:
+                logging.error("Cohere Chat: API key not found or is empty")
+                return "Cohere Chat: API Key Not Provided/Found in Config file or is empty"
+
+        logging.debug(f"Cohere Chat: Using API Key: {cohere_api_key[:3]}...{cohere_api_key[-3:]}")
+
+        logging.debug(f"Cohere Chat: Loaded data: {input_data}")
+        logging.debug(f"Cohere Chat: Type of data: {type(input_data)}")
+
+        # Ensure model is set
+        if not model:
+            model = loaded_config_data['models']['cohere']
+        logging.debug(f"Cohere Chat: Using model: {model}")
+
+        headers = {
+            'accept': 'application/json',
+            'content-type': 'application/json',
+            'Authorization': f'Bearer {cohere_api_key}'
+        }
+
+        # Ensure system_prompt is set
+        if not system_prompt:
+            system_prompt = "You are a helpful assistant"
+        logging.debug(f"Cohere Chat: System Prompt being sent is: '{system_prompt}'")
+
+        cohere_prompt = input_data
+        if custom_prompt_arg:
+            cohere_prompt += f"\n\n{custom_prompt_arg}"
+        logging.debug(f"Cohere Chat: User Prompt being sent is: '{cohere_prompt}'")
+
+        data = {
+            "chat_history": [
+                {"role": "SYSTEM", "message": system_prompt},
+            ],
+            "message": cohere_prompt,
+            "model": model,
+            "connectors": [{"id": "web-search"}]
+        }
+        logging.debug(f"Cohere Chat: Request data: {json.dumps(data, indent=2)}")
+
+        logging.debug("cohere chat: Submitting request to API endpoint")
+        print("cohere chat: Submitting request to API endpoint")
+
+        try:
+            response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data)
+            logging.debug(f"Cohere Chat: Raw API response: {response.text}")
+        except requests.RequestException as e:
+            logging.error(f"Cohere Chat: Error making API request: {str(e)}")
+            return f"Cohere Chat: Error making API request: {str(e)}"
+
+        if response.status_code == 200:
+            try:
+                response_data = response.json()
+            except json.JSONDecodeError:
+                logging.error("Cohere Chat: Failed to decode JSON response")
+                return "Cohere Chat: Failed to decode JSON response"
+
+            if response_data is None:
+                logging.error("Cohere Chat: No response data received.")
+                return "Cohere Chat: No response data received."
+
+            logging.debug(f"cohere chat: Full API response data: {json.dumps(response_data, indent=2)}")
+
+            if 'text' in response_data:
+                chat_response = response_data['text'].strip()
+                logging.debug("Cohere Chat: Chat request successful")
+                print("Cohere Chat request processed successfully.")
+                return chat_response
+            else:
+                logging.error("Cohere Chat: Expected 'text' key not found in API response.")
+                return "Cohere Chat: Expected data not found in API response."
+        else:
+            logging.error(f"Cohere Chat: API request failed with status code {response.status_code}: {response.text}")
+            print(f"Cohere Chat: Failed to process chat response, status code {response.status_code}: {response.text}")
+            return f"Cohere Chat: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error(f"Cohere Chat: Error in processing: {str(e)}", exc_info=True)
+        return f"Cohere Chat: Error occurred while processing chat request with Cohere: {str(e)}"
+
+
+# https://console.groq.com/docs/quickstart
+def chat_with_groq(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
+    logging.debug("Groq: Summarization process starting...")
+    try:
+        logging.debug("Groq: Loading and validating configurations")
+        loaded_config_data = load_and_log_configs()
+        if loaded_config_data is None:
+            logging.error("Failed to load configuration data")
+            groq_api_key = None
+        else:
+            # Prioritize the API key passed as a parameter
+            if api_key and api_key.strip():
+                groq_api_key = api_key
+                logging.info("Groq: Using API key provided as parameter")
+            else:
+                # If no parameter is provided, use the key from the config
+                groq_api_key = loaded_config_data['api_keys'].get('groq')
+                if groq_api_key:
+                    logging.info("Groq: Using API key from config file")
+                else:
+                    logging.warning("Groq: No API key found in config file")
+
+        # Final check to ensure we have a valid API key
+        if not groq_api_key or not groq_api_key.strip():
+            logging.error("Anthropic: No valid API key available")
+            # You might want to raise an exception here or handle this case as appropriate for your application
+            # For example: raise ValueError("No valid Anthropic API key available")
+
+        logging.debug(f"Groq: Using API Key: {groq_api_key[:5]}...{groq_api_key[-5:]}")
+
+        # Transcript data handling & Validation
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("Groq: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("Groq: Using provided string data for summarization")
+            data = input_data
+
+        # DEBUG - Debug logging to identify sent data
+        logging.debug(f"Groq: Loaded data: {data[:500]}...(snipped to first 500 chars)")
+        logging.debug(f"Groq: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("Groq: Summary already exists in the loaded data")
+            return data['summary']
+
+        # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("Groq: Invalid input data format")
+
+        # Set the model to be used
+        groq_model = loaded_config_data['models']['groq']
+
+        if temp is None:
+            temp = 0.2
+        temp = float(temp)
+        if system_message is None:
+            system_message = "You are a helpful AI assistant who does whatever the user requests."
+
+        headers = {
+            'Authorization': f'Bearer {groq_api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        groq_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        logging.debug("groq: Prompt being sent is {groq_prompt}")
+
+        data = {
+            "messages": [
+                {
+                    "role": "system",
+                    "content": system_message,
+                },
+                {
+                    "role": "user",
+                    "content": groq_prompt,
+                }
+            ],
+            "model": groq_model,
+            "temperature": temp
+        }
+
+        logging.debug("groq: Submitting request to API endpoint")
+        print("groq: Submitting request to API endpoint")
+        response = requests.post('https://api.groq.com/openai/v1/chat/completions', headers=headers, json=data)
+
+        response_data = response.json()
+        logging.debug(f"Full API response data: {response_data}")
+
+        if response.status_code == 200:
+            logging.debug(response_data)
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("groq: Chat request successful")
+                print("Groq: Chat request successful.")
+                return summary
+            else:
+                logging.error("Groq(chat): Expected data not found in API response.")
+                return "Groq(chat): Expected data not found in API response."
+        else:
+            logging.error(f"groq: API request failed with status code {response.status_code}: {response.text}")
+            return f"groq: API request failed: {response.text}"
+
+    except Exception as e:
+        logging.error("groq: Error in processing: %s", str(e))
+        return f"groq: Error occurred while processing summary with groq: {str(e)}"
+
+
+def chat_with_openrouter(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
+    import requests
+    import json
+    global openrouter_model, openrouter_api_key
+    try:
+        logging.debug("OpenRouter: Loading and validating configurations")
+        loaded_config_data = load_and_log_configs()
+        if loaded_config_data is None:
+            logging.error("Failed to load configuration data")
+            openrouter_api_key = None
+        else:
+            # Prioritize the API key passed as a parameter
+            if api_key and api_key.strip():
+                openrouter_api_key = api_key
+                logging.info("OpenRouter: Using API key provided as parameter")
+            else:
+                # If no parameter is provided, use the key from the config
+                openrouter_api_key = loaded_config_data['api_keys'].get('openrouter')
+                if openrouter_api_key:
+                    logging.info("OpenRouter: Using API key from config file")
+                else:
+                    logging.warning("OpenRouter: No API key found in config file")
+
+        # Model Selection validation
+        logging.debug("OpenRouter: Validating model selection")
+        loaded_config_data = load_and_log_configs()
+        openrouter_model = loaded_config_data['models']['openrouter']
+        logging.debug(f"OpenRouter: Using model from config file: {openrouter_model}")
+
+        # Final check to ensure we have a valid API key
+        if not openrouter_api_key or not openrouter_api_key.strip():
+            logging.error("OpenRouter: No valid API key available")
+            raise ValueError("No valid Anthropic API key available")
+    except Exception as e:
+        logging.error("OpenRouter: Error in processing: %s", str(e))
+        return f"OpenRouter: Error occurred while processing config file with OpenRouter: {str(e)}"
+
+    logging.debug(f"OpenRouter: Using API Key: {openrouter_api_key[:5]}...{openrouter_api_key[-5:]}")
+
+    logging.debug(f"OpenRouter: Using Model: {openrouter_model}")
+
+    if isinstance(input_data, str) and os.path.isfile(input_data):
+        logging.debug("OpenRouter: Loading json data for summarization")
+        with open(input_data, 'r') as file:
+            data = json.load(file)
+    else:
+        logging.debug("OpenRouter: Using provided string data for summarization")
+        data = input_data
+
+    # DEBUG - Debug logging to identify sent data
+    logging.debug(f"OpenRouter: Loaded data: {data[:500]}...(snipped to first 500 chars)")
+    logging.debug(f"OpenRouter: Type of data: {type(data)}")
+
+    if isinstance(data, dict) and 'summary' in data:
+        # If the loaded data is a dictionary and already contains a summary, return it
+        logging.debug("OpenRouter: Summary already exists in the loaded data")
+        return data['summary']
+
+    # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+    if isinstance(data, list):
+        segments = data
+        text = extract_text_from_segments(segments)
+    elif isinstance(data, str):
+        text = data
+    else:
+        raise ValueError("OpenRouter: Invalid input data format")
+
+    openrouter_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
+    logging.debug(f"openrouter: User Prompt being sent is {openrouter_prompt}")
+
+    if temp is None:
+        temp = 0.1
+    temp = float(temp)
+    if system_message is None:
+        system_message = "You are a helpful AI assistant who does whatever the user requests."
+
+    try:
+        logging.debug("OpenRouter: Submitting request to API endpoint")
+        print("OpenRouter: Submitting request to API endpoint")
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {openrouter_api_key}",
+            },
+            data=json.dumps({
+                "model": openrouter_model,
+                "messages": [
+                    {"role": "system", "content": system_message},
+                    {"role": "user", "content": openrouter_prompt}
+                ],
+                "temperature": temp
+            })
+        )
+
+        response_data = response.json()
+        logging.debug("Full API Response Data: %s", response_data)
+
+        if response.status_code == 200:
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("openrouter: Chat request successful")
+                print("openrouter: Chat request successful.")
+                return summary
+            else:
+                logging.error("openrouter: Expected data not found in API response.")
+                return "openrouter: Expected data not found in API response."
+        else:
+            logging.error(f"openrouter:  API request failed with status code {response.status_code}: {response.text}")
+            return f"openrouter: API request failed: {response.text}"
+    except Exception as e:
+        logging.error("openrouter: Error in processing: %s", str(e))
+        return f"openrouter: Error occurred while processing chat request with openrouter: {str(e)}"
+
+
+# FIXME: This function is not yet implemented properly
+def chat_with_huggingface(api_key, input_data, custom_prompt_arg, system_prompt=None, temp=None):
+    loaded_config_data = load_and_log_configs()
+    logging.debug(f"huggingface Chat: Chat request process starting...")
+    try:
+        huggingface_api_key = global_huggingface_api_key
+        
+        headers = {
+            "Authorization": f"Bearer {huggingface_api_key}"
+        }
+
+        # Setup model
+        huggingface_model = loaded_config_data['models']['huggingface']
+
+        API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}/v1/chat/completions"
+        if temp is None:
+            temp = 1.0
+        temp = float(temp)
+        huggingface_prompt = f"{custom_prompt_arg}\n\n\n{input_data}"
+        logging.debug(f"HuggingFace chat: Prompt being sent is {huggingface_prompt}")
+        data = {
+            "model": f"{huggingface_model}",
+            "messages": [{"role": "user", "content": f"{huggingface_prompt}"}],
+            "max_tokens": 4096,
+            "stream": False,
+            "temperature": temp
+        }
+
+        logging.debug("HuggingFace Chat: Submitting request...")
+        response = requests.post(API_URL, headers=headers, json=data)
+        logging.debug(f"Full API response data: {response.text}")
+
+        if response.status_code == 200:
+            response_json = response.json()
+            if "choices" in response_json and len(response_json["choices"]) > 0:
+                generated_text = response_json["choices"][0]["message"]["content"]
+                logging.debug("HuggingFace Chat: Chat request successful")
+                print("HuggingFace Chat: Chat request successful.")
+                return generated_text.strip()
+            else:
+                logging.error("HuggingFace Chat: No generated text in the response")
+                return "HuggingFace Chat: No generated text in the response"
+        else:
+            logging.error(
+                f"HuggingFace Chat: Chat request failed with status code {response.status_code}: {response.text}")
+            return f"HuggingFace Chat: Failed to process chat request, status code {response.status_code}: {response.text}"
+    except Exception as e:
+        logging.error(f"HuggingFace Chat: Error in processing: {str(e)}")
+        print(f"HuggingFace Chat: Error occurred while processing chat request with huggingface: {str(e)}")
+        return None
+
+
+def chat_with_deepseek(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
+    logging.debug("DeepSeek: Summarization process starting...")
+    try:
+        logging.debug("DeepSeek: Loading and validating configurations")
+        loaded_config_data = load_and_log_configs()
+        if loaded_config_data is None:
+            logging.error("Failed to load configuration data")
+            deepseek_api_key = None
+        else:
+            # Prioritize the API key passed as a parameter
+            if api_key and api_key.strip():
+                deepseek_api_key = api_key
+                logging.info("DeepSeek: Using API key provided as parameter")
+            else:
+                # If no parameter is provided, use the key from the config
+                deepseek_api_key = loaded_config_data['api_keys'].get('deepseek')
+                if deepseek_api_key:
+                    logging.info("DeepSeek: Using API key from config file")
+                else:
+                    logging.warning("DeepSeek: No API key found in config file")
+
+        # Final check to ensure we have a valid API key
+        if not deepseek_api_key or not deepseek_api_key.strip():
+            logging.error("DeepSeek: No valid API key available")
+            # You might want to raise an exception here or handle this case as appropriate for your application
+            # For example: raise ValueError("No valid deepseek API key available")
+
+
+        logging.debug(f"DeepSeek: Using API Key: {deepseek_api_key[:5]}...{deepseek_api_key[-5:]}")
+
+        # Input data handling
+        if isinstance(input_data, str) and os.path.isfile(input_data):
+            logging.debug("DeepSeek: Loading json data for summarization")
+            with open(input_data, 'r') as file:
+                data = json.load(file)
+        else:
+            logging.debug("DeepSeek: Using provided string data for summarization")
+            data = input_data
+
+        # DEBUG - Debug logging to identify sent data
+        logging.debug(f"DeepSeek: Loaded data: {data[:500]}...(snipped to first 500 chars)")
+        logging.debug(f"DeepSeek: Type of data: {type(data)}")
+
+        if isinstance(data, dict) and 'summary' in data:
+            # If the loaded data is a dictionary and already contains a summary, return it
+            logging.debug("DeepSeek: Summary already exists in the loaded data")
+            return data['summary']
+
+        # Text extraction
+        if isinstance(data, list):
+            segments = data
+            text = extract_text_from_segments(segments)
+        elif isinstance(data, str):
+            text = data
+        else:
+            raise ValueError("DeepSeek: Invalid input data format")
+
+        deepseek_model = loaded_config_data['models']['deepseek'] or "deepseek-chat"
+
+        if temp is None:
+            temp = 0.1
+        temp = float(temp)
+        if system_message is None:
+            system_message = "You are a helpful AI assistant who does whatever the user requests."
+
+        headers = {
+            'Authorization': f'Bearer {api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"Deepseek API Key: {api_key[:5]}...{api_key[-5:] if api_key else None}")
+        logging.debug("DeepSeek: Preparing data + prompt for submittal")
+        deepseek_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
+        data = {
+            "model": deepseek_model,
+            "messages": [
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": deepseek_prompt}
+            ],
+            "stream": False,
+            "temperature": temp
+        }
+
+        logging.debug("DeepSeek: Posting request")
+        response = requests.post('https://api.deepseek.com/chat/completions', headers=headers, json=data)
+        logging.debug(f"Full API response data: {response}")
+        if response.status_code == 200:
+            response_data = response.json()
+            logging.debug(response_data)
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("DeepSeek: Chat request successful")
+                return summary
+            else:
+                logging.warning("DeepSeek: Chat response not found in the response data")
+                return "DeepSeek: Chat response not available"
+        else:
+            logging.error(f"DeepSeek: Chat request failed with status code {response.status_code}")
+            logging.error(f"DeepSeek: Error response: {response.text}")
+            return f"DeepSeek: Failed to chat request summary. Status code: {response.status_code}"
+    except Exception as e:
+        logging.error(f"DeepSeek: Error in processing: {str(e)}", exc_info=True)
+        return f"DeepSeek: Error occurred while processing chat request: {str(e)}"
+
+
+def chat_with_mistral(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
+    logging.debug("Mistral: Chat request made")
+    try:
+        logging.debug("Mistral: Loading and validating configurations")
+        loaded_config_data = load_and_log_configs()
+        if loaded_config_data is None:
+            logging.error("Failed to load configuration data")
+            mistral_api_key = None
+        else:
+            # Prioritize the API key passed as a parameter
+            if api_key and api_key.strip():
+                mistral_api_key = api_key
+                logging.info("Mistral: Using API key provided as parameter")
+            else:
+                # If no parameter is provided, use the key from the config
+                mistral_api_key = loaded_config_data['api_keys'].get('mistral')
+                if mistral_api_key:
+                    logging.info("Mistral: Using API key from config file")
+                else:
+                    logging.warning("Mistral: No API key found in config file")
+
+        # Final check to ensure we have a valid API key
+        if not mistral_api_key or not mistral_api_key.strip():
+            logging.error("Mistral: No valid API key available")
+            return "Mistral: No valid API key available"
+
+        logging.debug(f"Mistral: Using API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:]}")
+
+        logging.debug("Mistral: Using provided string data")
+        data = input_data
+
+        # Text extraction
+        if isinstance(input_data, list):
+            text = extract_text_from_segments(input_data)
+        elif isinstance(input_data, str):
+            text = input_data
+        else:
+            raise ValueError("Mistral: Invalid input data format")
+
+        mistral_model = loaded_config_data['models'].get('mistral', "mistral-large-latest")
+
+        temp = float(temp) if temp is not None else 0.2
+        if system_message is None:
+            system_message = "You are a helpful AI assistant who does whatever the user requests."
+
+        headers = {
+            'Authorization': f'Bearer {mistral_api_key}',
+            'Content-Type': 'application/json'
+        }
+
+        logging.debug(
+            f"Deepseek API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:] if mistral_api_key else None}")
+        logging.debug("Mistral: Preparing data + prompt for submittal")
+        mistral_prompt = f"{custom_prompt_arg}\n\n\n\n{text} "
+        data = {
+            "model": mistral_model,
+            "messages": [
+                {"role": "system",
+                 "content": system_message},
+                {"role": "user",
+                "content": mistral_prompt}
+            ],
+            "temperature": temp,
+            "top_p": 1,
+            "max_tokens": 4096,
+            "stream": False,
+            "safe_prompt": False
+        }
+
+        logging.debug("Mistral: Posting request")
+        response = requests.post('https://api.mistral.ai/v1/chat/completions', headers=headers, json=data)
+        logging.debug(f"Full API response data: {response}")
+        if response.status_code == 200:
+            response_data = response.json()
+            logging.debug(response_data)
+            if 'choices' in response_data and len(response_data['choices']) > 0:
+                summary = response_data['choices'][0]['message']['content'].strip()
+                logging.debug("Mistral: request successful")
+                return summary
+            else:
+                logging.warning("Mistral: Chat response not found in the response data")
+                return "Mistral: Chat response not available"
+        else:
+            logging.error(f"Mistral: Chat request failed with status code {response.status_code}")
+            logging.error(f"Mistral: Error response: {response.text}")
+            return f"Mistral: Failed to process summary. Status code: {response.status_code}. Error: {response.text}"
+    except Exception as e:
+        logging.error(f"Mistral: Error in processing: {str(e)}", exc_info=True)
+        return f"Mistral: Error occurred while processing Chat: {str(e)}"
+
+
+
+# Stashed in here since OpenAI usage.... #FIXME
+# FIXME - https://docs.vllm.ai/en/latest/getting_started/quickstart.html .... Great docs.
+# def chat_with_vllm(input_data, custom_prompt_input, api_key=None, vllm_api_url="http://127.0.0.1:8000/v1/chat/completions", system_prompt=None):
+#     loaded_config_data = load_and_log_configs()
+#     llm_model = loaded_config_data['models']['vllm']
+#     # API key validation
+#     if api_key is None:
+#         logging.info("vLLM: API key not provided as parameter")
+#         logging.info("vLLM: Attempting to use API key from config file")
+#         api_key = loaded_config_data['api_keys']['llama']
+#
+#     if api_key is None or api_key.strip() == "":
+#         logging.info("vLLM: API key not found or is empty")
+#     vllm_client = OpenAI(
+#         base_url=vllm_api_url,
+#         api_key=custom_prompt_input
+#     )
+#
+#     if isinstance(input_data, str) and os.path.isfile(input_data):
+#         logging.debug("vLLM: Loading json data for summarization")
+#         with open(input_data, 'r') as file:
+#             data = json.load(file)
+#     else:
+#         logging.debug("vLLM: Using provided string data for summarization")
+#         data = input_data
+#
+#     logging.debug(f"vLLM: Loaded data: {data}")
+#     logging.debug(f"vLLM: Type of data: {type(data)}")
+#
+#     if isinstance(data, dict) and 'summary' in data:
+#         # If the loaded data is a dictionary and already contains a summary, return it
+#         logging.debug("vLLM: Summary already exists in the loaded data")
+#         return data['summary']
+#
+#     # If the loaded data is a list of segment dictionaries or a string, proceed with summarization
+#     if isinstance(data, list):
+#         segments = data
+#         text = extract_text_from_segments(segments)
+#     elif isinstance(data, str):
+#         text = data
+#     else:
+#         raise ValueError("Invalid input data format")
+#
+#
+#     custom_prompt = custom_prompt_input
+#
+#     completion = client.chat.completions.create(
+#         model=llm_model,
+#         messages=[
+#             {"role": "system", "content": f"{system_prompt}"},
+#             {"role": "user", "content": f"{text} \n\n\n\n{custom_prompt}"}
+#         ]
+#     )
+#     vllm_summary = completion.choices[0].message.content
+#     return vllm_summary
+
+
+
+#
+#
 #######################################################################################################################