Merge pull request #199 from zaverichintan/v3

Added support for Ollama running locally or publicly hosted api

README.md

@@ -148,13 +148,12 @@ This file contains sensitive information. Never share or commit this file to ver
   - Replace with your LinkedIn account email address
 - `password: [Your LinkedIn password]`
   - Replace with your LinkedIn account password
-- `openai_api_key: [Your OpenAI API key]`
+- `llm_api_key: [Your OpenAI or Ollama API key]`
   - Replace with your OpenAI API key for GPT integration
   - To obtain an API key, follow the tutorial at: https://medium.com/@lorenzozar/how-to-get-your-own-openai-api-key-f4d44e60c327
   - Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the [OpenAI billing dashboard](https://platform.openai.com/account/billing).
 
 
-
 ### 2. config.yaml
 
 This file defines your job search parameters and bot behavior. Each section contains options that you can customize:
@@ -211,7 +210,22 @@ This file defines your job search parameters and bot behavior. Each section cont
       - Sales
       - Marketing
     ```
+#### 2.1 config.yaml - Customize LLM model endpoint
 
+- `llm_model_type`:
+  - Choose the model type, supported: openai / ollama / claude
+- `llm_model`: 
+  - Choose the LLM model, currently supported: 
+    - openai: gpt-4o
+    - ollama: llama2, mistral:v0.3
+    - claude: any model 
+- `llm_api_url`: 
+  - Link of the API endpoint for the LLM model
+    - openai: https://api.pawan.krd/cosmosrp/v1
+    - ollama: http://127.0.0.1:11434/
+    - claude: https://api.anthropic.com/v1
+ - Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama)
+  
 ### 3. plain_text_resume.yaml
 
 This file contains your resume information in a structured format. Fill it out with your personal details, education, work experience, and skills. This information is used to auto-fill application forms and generate customized resumes.

data_folder/config.yaml

@@ -39,4 +39,8 @@ companyBlacklist:
 
 titleBlacklist:
   - word1
-  - word2
+  - word2
+  
+llm_model_type: openai
+llm_model: gpt-4o 
+llm_api_url: https://api.pawan.krd/cosmosrp/v1

data_folder/secrets.yaml

@@ -1,3 +1,3 @@
 email: myemaillinkedin@gmail.com
 password: ImpossiblePassowrd10
-openai_api_key: sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR
+llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'

data_folder_example/config.yaml

@@ -37,3 +37,7 @@ companyBlacklist:
   - Crossover
 
 titleBlacklist:
+
+llm_model_type: openai
+llm_model: 'gpt-4o'
+llm_api_url: https://api.pawan.krd/cosmosrp/v1'

data_folder_example/secrets.yaml

@@ -1,3 +1,3 @@
 email: myemaillinkedin@gmail.com
 password: ImpossiblePassowrd10
-openai_api_key: sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR
+llm_api_key: 'sk-11KRr4uuTwpRGfeRTfj1T9BlbkFJjP8QTrswHU1yGruru2FR'

main.py

@@ -101,7 +101,7 @@ class ConfigValidator:
     @staticmethod
     def validate_secrets(secrets_yaml_path: Path) -> tuple:
         secrets = ConfigValidator.validate_yaml_file(secrets_yaml_path)
-        mandatory_secrets = ['email', 'password', 'openai_api_key']
+        mandatory_secrets = ['email', 'password']
 
         for secret in mandatory_secrets:
             if secret not in secrets:
@@ -111,10 +111,7 @@ class ConfigValidator:
             raise ConfigError(f"Invalid email format in secrets file {secrets_yaml_path}.")
         if not secrets['password']:
             raise ConfigError(f"Password cannot be empty in secrets file {secrets_yaml_path}.")
-        if not secrets['openai_api_key']:
-            raise ConfigError(f"OpenAI API key cannot be empty in secrets file {secrets_yaml_path}.")
-
-        return secrets['email'], str(secrets['password']), secrets['openai_api_key']
+        return secrets['email'], str(secrets['password']), secrets['llm_api_key']
 
 class FileManager:
     @staticmethod
@@ -158,14 +155,14 @@ def init_browser() -> webdriver.Chrome:
     except Exception as e:
         raise RuntimeError(f"Failed to initialize browser: {str(e)}")
 
-def create_and_run_bot(email: str, password: str, parameters: dict, openai_api_key: str):
+def create_and_run_bot(email, password, parameters, llm_api_key):
     try:
         style_manager = StyleManager()
         resume_generator = ResumeGenerator()
         with open(parameters['uploads']['plainTextResume'], "r") as file:
             plain_text_resume = file.read()
         resume_object = Resume(plain_text_resume)
-        resume_generator_manager = FacadeManager(openai_api_key, style_manager, resume_generator, resume_object, Path("data_folder/output"))
+        resume_generator_manager = FacadeManager(llm_api_key, style_manager, resume_generator, resume_object, Path("data_folder/output"))
         os.system('cls' if os.name == 'nt' else 'clear')
         resume_generator_manager.choose_style()
         os.system('cls' if os.name == 'nt' else 'clear')
@@ -175,7 +172,7 @@ def create_and_run_bot(email: str, password: str, parameters: dict, openai_api_k
         browser = init_browser()
         login_component = LinkedInAuthenticator(browser)
         apply_component = LinkedInJobManager(browser)
-        gpt_answerer_component = GPTAnswerer(openai_api_key)
+        gpt_answerer_component = GPTAnswerer(parameters, llm_api_key)
         bot = LinkedInBotFacade(login_component, apply_component)
         bot.set_secrets(email, password)
         bot.set_job_application_profile_and_resume(job_application_profile_object, resume_object)
@@ -197,12 +194,12 @@ def main(resume: Path = None):
         secrets_file, config_file, plain_text_resume_file, output_folder = FileManager.validate_data_folder(data_folder)
         
         parameters = ConfigValidator.validate_config(config_file)
-        email, password, openai_api_key = ConfigValidator.validate_secrets(secrets_file)
+        email, password, llm_api_key = ConfigValidator.validate_secrets(secrets_file)
         
         parameters['uploads'] = FileManager.file_paths_to_dict(resume, plain_text_resume_file)
         parameters['outputFileDirectory'] = output_folder
         
-        create_and_run_bot(email, password, parameters, openai_api_key)
+        create_and_run_bot(email, password, parameters, llm_api_key)
     except ConfigError as ce:
         print(f"Configuration error: {str(ce)}")
         print("Refer to the configuration guide for troubleshooting: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")

src/gpt.py

@@ -3,7 +3,8 @@ import os
 import re
 import textwrap
 from datetime import datetime
-from typing import Dict, List
+from abc import ABC, abstractmethod
+from typing import Dict, List, Union
 from pathlib import Path
 from dotenv import load_dotenv
 from langchain_core.messages.ai import AIMessage
@@ -17,10 +18,66 @@ import src.strings as strings
 
 load_dotenv()
 
+class AIModel(ABC):
+    @abstractmethod
+    def invoke(self, prompt: str) -> str:
+        pass
+
+class OpenAIModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
+        from langchain_openai import ChatOpenAI
+        self.model = ChatOpenAI(model_name=llm_model, openai_api_key=api_key,
+                                temperature=0.4, base_url=llm_api_url)
+ 
+    def invoke(self, prompt: str) -> str:
+        print("invoke in openai")
+        response = self.model.invoke(prompt)
+        return response
+
+class ClaudeModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
+        from langchain_anthropic import ChatAnthropic
+        self.model = ChatAnthropic(model=llm_model, api_key=api_key,
+                                temperature=0.4, base_url=llm_api_url)
+
+    def invoke(self, prompt: str) -> str:
+        response = self.model.invoke(prompt)
+        return response
+
+class OllamaModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
+        from langchain_ollama import ChatOllama
+        self.model = ChatOllama(model=llm_model, base_url=llm_api_url)
+
+    def invoke(self, prompt: str) -> str:
+        response = self.model.invoke(prompt)
+        return response
+
+class AIAdapter:
+    def __init__(self, config: dict, api_key: str):
+        self.model = self._create_model(config, api_key)
+
+    def _create_model(self, config: dict, api_key: str) -> AIModel:
+        llm_model_type = config['llm_model_type']
+        llm_model = config['llm_model']
+        llm_api_url = config['llm_api_url']
+        print('Using {0} with {1} from {2}'.format(llm_model_type, llm_model, llm_api_url))
+        
+        if llm_model_type == "openai":
+            return OpenAIModel(api_key, llm_model, llm_api_url)
+        elif llm_model_type == "claude":
+            return ClaudeModel(api_key, llm_model, llm_api_url)
+        elif llm_model_type == "ollama":
+            return OllamaModel(api_key, llm_model, llm_api_url)
+        else:
+            raise ValueError(f"Unsupported model type: {model_type}")
+
+    def invoke(self, prompt: str) -> str:
+        return self.model.invoke(prompt)
 
 class LLMLogger:
     
-    def __init__(self, llm: ChatOpenAI):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
         self.llm = llm
 
     @staticmethod
@@ -78,12 +135,12 @@ class LLMLogger:
 
 class LoggerChatModel:
 
-    def __init__(self, llm: ChatOpenAI):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
         self.llm = llm
 
     def __call__(self, messages: List[Dict[str, str]]) -> str:
         # Call the LLM with the provided messages and log the response.
-        reply = self.llm(messages)
+        reply = self.llm.invoke(messages)
         parsed_reply = self.parse_llmresult(reply)
         LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
         return reply
@@ -113,10 +170,9 @@ class LoggerChatModel:
 
 
 class GPTAnswerer:
-    def __init__(self, openai_api_key):
-        self.llm_cheap = LoggerChatModel(
-            ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)
-        )
+    def __init__(self, config, llm_api_key):
+        self.ai_adapter = AIAdapter(config, llm_api_key)
+        self.llm_cheap = LoggerChatModel(self.ai_adapter)
     @property
     def job_description(self):
         return self.job.description