Merge branch 'release/v4.1.0' into ft_process_docs

.github/pull_request_template.md

@@ -0,0 +1,40 @@
+- **Title**: [Descriptive title of the changes]
+- **Description**: [Provide a clear description of the changes and their purpose]
+- **Related Issues**: #[issue number]
+- **Type**:
+  - [ ] Feature
+  - [ ] Bug Fix
+  - [ ] Refactor
+  - [ ] Documentation
+  - [ ] Other:
+
+## Implementation Details
+
+- [ ] Changes are focused and solve the stated problem
+- [ ] Code follows project style guides
+- [ ] Complex logic is documented
+- [ ] No unnecessary complexity introduced
+
+## Testing
+
+- [ ] Unit tests added/updated
+- [ ] Integration tests added/updated
+- [ ] Manual testing completed
+- [ ] All tests passing
+
+## Documentation & Quality
+
+- [ ] Project documentation updated
+- [ ] Code reviewed for clarity
+- [ ] Breaking changes clearly marked
+- [ ] Dependencies documented
+
+## Deployment Impact
+
+- [ ] Database migrations required? [Yes/No]
+- [ ] Configuration changes needed? [Yes/No]
+- [ ] Breaking changes? [Yes/No]
+
+## Additional Notes
+
+[Add any other context or notes for reviewers]

README.md

@@ -178,7 +178,7 @@ Auto_Jobs_Applier_AIHawk steps in as a game-changing solution to these challenge
 
 This file contains sensitive information. Never share or commit this file to version control.
 
-- `llm_api_key: [Your OpenAI or Ollama API key or Gemini API key]`
+- `llm_api_key: [Your OpenAI or Ollama API key or Gemini API key or Groq API key or AI/ML 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).
@@ -188,6 +188,8 @@ This file contains sensitive information. Never share or commit this file to ver
     OpenAI will update your account automatically, but it might take some time, ranging from a couple of hours to a few days.  
     You can find more about your organization limits on the [official page](https://platform.openai.com/settings/organization/limits).
   - For obtaining Gemini API key visit [Google AI for Devs](https://ai.google.dev/gemini-api/docs/api-key)
+  - For obtaining Groq API key visit [Groq API](https://api.groq.com/v1)
+  - For obtaining AI/ML API key visite [AI/ML API](https://aimlapi.com/app/)
 
 ### 2. work_preferences.yaml
 
@@ -265,19 +267,23 @@ This file defines your job search parameters and bot behavior. Each section cont
 #### 2.1 config.py - Customize LLM model endpoint
 
 - `LLM_MODEL_TYPE`:
-  - Choose the model type, supported: openai / ollama / claude / gemini
+  - Choose the model type, supported: openai / ollama / claude / gemini / groq / aiml
 - `LLM_MODEL`:
   - Choose the LLM model, currently supported:
     - openai: gpt-4o
     - ollama: llama2, mistral:v0.3
     - claude: any model
     - gemini: any model
-- `LLM_API_URL`:
-  - Link of the API endpoint for the LLM model
-    - openai: <https://api.pawan.krd/cosmosrp/v1>
+    - groq: llama3-groq-70b-8192-tool-use-preview, llama3-groq-8b-8192-tool-use-preview, llama-3.1-70b-versatile, llama-3.1-8b-instant, llama-3.2-3b-preview, llama3-70b-8192, llama3-8b-8192, mixtral-8x7b-32768
+    - aiml: any model
+
+- `llm_api_url`:
+  - Link of the API endpoint for the LLM model. (only requried for ollama)
     - ollama: <http://127.0.0.1:11434/>
     - claude: <https://api.anthropic.com/v1>
     - gemini: <https://aistudio.google.com/app/apikey>
+    - groq: <https://api.groq.com/v1>
+    - aiml: <https://api.aimlapi.com/v2>
 - Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama)
 
 ### 3. plain_text_resume.yaml
@@ -719,6 +725,8 @@ For further assistance, please create an issue on the [GitHub repository](https:
   - Written by Rushi, [Linkedin](https://www.linkedin.com/in/rushichaganti/), support him by following.
 
 - [OpenAI API Documentation](https://platform.openai.com/docs/)
+
+- [AI/ML API Documentation](https://docs.aimlapi.com/)
 
 ### For Developers
 

constants.py

@@ -64,8 +64,10 @@
 LLM_API_URL = "llm_api_url"
 LLM_MODEL = "llm_model"
 OPENAI = "openai"
+AIML="aiml"
 CLAUDE = "claude"
 OLLAMA = "ollama"
 GEMINI = "gemini"
+GROQ = "groq"
 HUGGINGFACE = "huggingface"
 PERPLEXITY = "perplexity"

requirements.txt

@@ -6,6 +6,7 @@ jsonschema==4.23.0
 jsonschema-specifications==2023.12.1
 langchain==0.2.11
 langchain-anthropic
+langchain-groq==0.1.9
 langchain-huggingface
 langchain-community==0.2.10
 langchain-core==0.2.36

src/ai_hawk/job_manager.py

@@ -5,6 +5,7 @@
 from itertools import product
 from pathlib import Path
 from turtle import color
+from datetime import datetime
 
 from inputimeout import inputimeout, TimeoutOccurred
 from selenium.common.exceptions import NoSuchElementException
@@ -400,13 +401,15 @@ def write_to_file(self, job : Job, file_name, reason=None):
         logger.debug(f"Writing job application result to file: {file_name}")
         pdf_path = Path(job.resume_path).resolve()
         pdf_path = pdf_path.as_uri()
+        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         data = {
             "company": job.company,
             "job_title": job.title,
             "link": job.link,
             "job_recruiter": job.recruiter_link,
             "job_location": job.location,
-            "pdf_path": pdf_path
+            "pdf_path": pdf_path,
+            "time": current_time
         }
 
         if reason:

src/ai_hawk/llm/llm_manager.py

@@ -16,6 +16,7 @@
 from langchain_core.prompt_values import StringPromptValue
 from langchain_core.prompts import ChatPromptTemplate
 from Levenshtein import distance
+from loguru import logger
 
 import ai_hawk.llm.prompts as prompts
 from config import JOB_SUITABILITY_SCORE
@@ -30,6 +31,7 @@
     EXPERIENCE_DETAILS,
     FINISH_REASON,
     GEMINI,
+    GROQ,
     HUGGINGFACE,
     ID,
     INPUT_TOKENS,
@@ -38,7 +40,6 @@
     JOB_DESCRIPTION,
     LANGUAGES,
     LEGAL_AUTHORIZATION,
-    LLM_MODEL_TYPE,
     LOGPROBS,
     MODEL,
     MODEL_NAME,
@@ -69,9 +70,9 @@
     TOTAL_TOKENS,
     USAGE_METADATA,
     WORK_PREFERENCES,
+    AIML,
 )
 from src.job import Job
-from src.logging import logger
 import config as cfg
 
 load_dotenv()
@@ -82,6 +83,16 @@ class AIModel(ABC):
     def invoke(self, prompt: str) -> str:
         pass
 
+class GroqAIModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str):
+        from langchain_groq import ChatGroq
+        self.model = ChatGroq(model=llm_model, api_key=api_key,
+                                temperature=0.4)
+
+    def invoke(self, prompt: str) -> BaseMessage:
+        response = self.model.invoke(prompt)
+        logger.debug("Invoking GroqAI API")
+        return response
 
 class OpenAIModel(AIModel):
     def __init__(self, api_key: str, llm_model: str):
@@ -97,6 +108,24 @@ def invoke(self, prompt: str) -> BaseMessage:
         return response
 
 
+class AIMLModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str):
+        from langchain_openai import ChatOpenAI
+
+        self.base_url = "https://api.aimlapi.com/v2"
+        self.model = ChatOpenAI(
+            model_name=llm_model,
+            openai_api_key=api_key,
+            temperature=0.7,
+            base_url=self.base_url,
+        )
+
+    def invoke(self, prompt: str) -> BaseMessage:
+        logger.debug("Invoking AIML API")
+        response = self.model.invoke(prompt)
+        return response
+
+
 class ClaudeModel(AIModel):
     def __init__(self, api_key: str, llm_model: str):
         from langchain_anthropic import ChatAnthropic
@@ -195,12 +224,16 @@ def _create_model(self, config: dict, api_key: str) -> AIModel:
 
         if llm_model_type == OPENAI:
             return OpenAIModel(api_key, llm_model)
+        elif llm_model_type == AIML:
+            return AIMLModel(api_key, llm_model)
         elif llm_model_type == CLAUDE:
             return ClaudeModel(api_key, llm_model)
         elif llm_model_type == OLLAMA:
             return OllamaModel(llm_model, llm_api_url)
         elif llm_model_type == GEMINI:
             return GeminiModel(api_key, llm_model)
+        elif llm_model_type == GROQ:
+            return GroqAIModel(api_key, llm_model)     
         elif llm_model_type == HUGGINGFACE:
             return HuggingFaceModel(api_key, llm_model)
         elif llm_model_type == PERPLEXITY:
@@ -213,7 +246,8 @@ def invoke(self, prompt: str) -> str:
 
 
 class LLMLogger:
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
+
+    def __init__(self, llm: AIModel):
         self.llm = llm
         logger.debug(f"LLMLogger successfully initialized with LLM: {llm}")
 
@@ -325,7 +359,8 @@ def log_request(prompts, parsed_reply: Dict[str, Dict]):
 
 
 class LoggerChatModel:
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
+
+    def __init__(self, llm: AIModel):
         self.llm = llm
         logger.debug(f"LoggerChatModel successfully initialized with LLM: {llm}")
 

src/ai_hawk/llm/prompts.py

@@ -262,17 +262,17 @@
 - Do not include any introductions, explanations, or additional information.
 - The letter should be formatted into paragraph.
 
+## My resume:
+```
+{resume}
+```
+
 ## Company Name:
 {company}
-
 ## Job Description:
 ```
 {job_description}
 ```
-## My resume:
-```
-{resume}
-```
 """
 
 numeric_question_template = """
@@ -432,10 +432,10 @@
 is_relavant_position_template = """
    Evaluate whether the provided resume meets the requirements outlined in the job description. Determine if the candidate is suitable for the job based on the information provided.
 
-Job Description: {job_description}
-
 Resume: {resume}
 
+Job Description: {job_description}
+
 Instructions:
 1. Extract the key requirements from the job description, identifying hard requirements (must-haves) and soft requirements (nice-to-haves).
 2. Identify the relevant qualifications from the resume.