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| # 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): | |
| global cohere_api_key | |
| cohere_api_key = api_key | |
| loaded_config_data = load_and_log_configs() | |
| try: | |
| # API key validation | |
| if not api_key: | |
| logging.info("cohere: API key not provided as parameter") | |
| logging.info("cohere: Attempting to use API key from config file") | |
| cohere_api_key = loaded_config_data['api_keys']['cohere'] | |
| if not api_key or api_key.strip() == "": | |
| logging.error("cohere: API key not found or is empty") | |
| return "cohere: API Key Not Provided/Found in Config file or is empty" | |
| logging.debug(f"cohere: Using API Key: {api_key[:5]}...{api_key[-5:]}") | |
| logging.debug(f"Cohere: Loaded data: {input_data}") | |
| logging.debug(f"Cohere: Type of data: {type(input_data)}") | |
| cohere_model = loaded_config_data['models']['cohere'] | |
| headers = { | |
| 'accept': 'application/json', | |
| 'content-type': 'application/json', | |
| 'Authorization': f'Bearer {cohere_api_key}' | |
| } | |
| if system_prompt is not None: | |
| logging.debug("Anthropic: Using provided system prompt") | |
| pass | |
| else: | |
| system_prompt = "You are a helpful assistant" | |
| cohere_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}" | |
| logging.debug(f"cohere: User Prompt being sent is {cohere_prompt}") | |
| logging.debug(f"cohere: System Prompt being sent is {system_prompt}") | |
| data = { | |
| "chat_history": [ | |
| {"role": "SYSTEM", "message": f"system_prompt"}, | |
| ], | |
| "message": f"{cohere_prompt}", | |
| "model": model, | |
| "connectors": [{"id": "web-search"}] | |
| } | |
| logging.debug("cohere: Submitting request to API endpoint") | |
| print("cohere: Submitting request to API endpoint") | |
| response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data) | |
| response_data = response.json() | |
| logging.debug(f"Full API response data: {response_data}") | |
| if response.status_code == 200: | |
| if 'text' in response_data: | |
| chat_response = response_data['text'].strip() | |
| logging.debug("cohere: Chat request successful") | |
| print("Chat request processed successfully.") | |
| return chat_response | |
| else: | |
| logging.error("Expected data not found in API response.") | |
| return "Expected data not found in API response." | |
| else: | |
| logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}") | |
| print(f"Failed to process summary, status code {response.status_code}: {response.text}") | |
| return f"cohere: API request failed: {response.text}" | |
| except Exception as e: | |
| logging.error("cohere: Error in processing: %s", str(e)) | |
| return f"cohere: Error occurred while processing summary 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): | |
| loaded_config_data = load_and_log_configs() | |
| global huggingface_api_key | |
| logging.debug(f"huggingface: Summarization process starting...") | |
| try: | |
| # API key validation | |
| if not api_key: | |
| logging.info("HuggingFace: API key not provided as parameter") | |
| logging.info("HuggingFace: Attempting to use API key from config file") | |
| huggingface_api_key = loaded_config_data['api_keys']['openai'] | |
| if not api_key or api_key.strip() == "": | |
| logging.error("HuggingFace: API key not found or is empty") | |
| return "HuggingFace: API Key Not Provided/Found in Config file or is empty" | |
| logging.debug(f"HuggingFace: Using API Key: {api_key[:5]}...{api_key[-5:]}") | |
| headers = { | |
| "Authorization": f"Bearer {api_key}" | |
| } | |
| # Setup model | |
| huggingface_model = loaded_config_data['models']['huggingface'] | |
| API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}" | |
| if system_prompt is not None: | |
| logging.debug("HuggingFace: Using provided system prompt") | |
| pass | |
| else: | |
| system_prompt = "You are a helpful assistant" | |
| huggingface_prompt = f"{input_data}\n\n\n\n{custom_prompt_arg}" | |
| logging.debug("huggingface: Prompt being sent is {huggingface_prompt}") | |
| data = { | |
| "inputs": f"{input_data}", | |
| "parameters": {"max_length": 8192, "min_length": 100} # You can adjust max_length and min_length as needed | |
| } | |
| logging.debug("huggingface: Submitting request...") | |
| response = requests.post(API_URL, headers=headers, json=data) | |
| logging.debug(f"Full API response data: {response}") | |
| if response.status_code == 200: | |
| summary = response.json()[0]['generated_text'].strip() | |
| logging.debug("huggingface: Chat request successful") | |
| print("Chat request successful.") | |
| return summary | |
| else: | |
| logging.error(f"huggingface: Chat request failed with status code {response.status_code}: {response.text}") | |
| return f"Failed to process chat request, status code {response.status_code}: {response.text}" | |
| except Exception as e: | |
| logging.error("huggingface: Error in processing: %s", str(e)) | |
| print(f"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 | |
| # | |
| # | |
| ####################################################################################################################### |