# About Advanced Human Like Career Advisor The Advanced Human Like Career Advisor transforms career guidance by creating a personalized, interactive mentor for every individual. By combining natural language processing with real-time data analytics via SERP API, it emulates human-like advice, providing tailored career support that adapts to each user’s goals, strengths, and market trends. This innovative solution bridges the gap between traditional career counseling and modern digital demands, delivering expert guidance on-demand. # Identifying the Gaps in Existing Solutions Current career guidance tools—ranging from static career websites to generic chatbots—often offer one-size-fits-all recommendations that lack depth and personalization. Many systems fail to integrate appropriate web results into their guidance or adapt to individual career profiles, leading to advice that is either outdated or insufficiently customized. Furthermore, these tools rarely maintain conversation history, resulting in fragmented support that doesn’t build on previous interactions. # Limitations of Existing Approaches Generic Responses: Many career advisors offer broad, non-specific advice that does not cater to an individual’s unique aspirations or background. Lack of Contextual Awareness: Without retaining conversation history, these systems cannot provide continuity in guidance, leading to disjointed or irrelevant recommendations. Static Data Reliance: Traditional systems depend solely on pre-loaded information and lack integration with current web data. Inefficient Personalization: Current approaches do not dynamically adjust to user feedback or adapt their guidance based on evolving career trajectories. # How Advanced Human Like Career Advisor Addresses These Gaps The Advanced Human Like Career Advisor overcomes these limitations by leveraging state-of-the-art NLP techniques, retrieval-augmented generation (RAG), and dynamic data integration through the SERP API. It offers personalized, context-aware career advice by: Integrating user-uploaded documents, educational resources, and web search results via SERP API to continuously update its knowledge base. Utilizing conversation memory to retain context across sessions, ensuring that each interaction builds upon the previous ones. Employing advanced vector embedding techniques to accurately match user queries with relevant, authoritative career content. Delivering nuanced, human-like responses that guide users through career exploration, skill development, and job market insights—using SERP API to fetch appropriate results from the web based on user queries. # Working of Advanced Human Like Career Advisor User Input & Data Integration: Users provide their career documents, resumes, or relevant educational materials. The system processes and converts these documents into vector embeddings for efficient retrieval. Web Data Enrichment via SERP API: Using the SERP API, the advisor fetches appropriate and up-to-date results from the web based on user queries, ensuring that every response reflects the latest information available online. Context-Aware Interaction: The AI mentor utilizes conversation history to maintain context and deliver responses that are both personalized and consistent. Retrieval-augmented generation ensures that every answer is grounded in both stored authoritative content and current web results via the SERP API. Response Generation: By combining pre-trained language models with custom-tailored career data and SERP API results, the system generates detailed, human-like career guidance that includes specific skill recommendations, industry insights, and growth opportunities. # Examples of Use Cases For Students: A college student exploring career options can upload academic records and interests. The advisor not only suggests suitable fields but also recommends relevant courses and skill development resources—using SERP API to fetch timely web results that support each recommendation. For Job Seekers: A professional looking to switch careers receives a personalized assessment of their skills along with actionable recommendations for training and certification, directly aligned with the latest web insights obtained through the SERP API. For Organizations: Companies can deploy the advisor to support employee career development by providing personalized growth plans and identifying internal mobility opportunities, all while leveraging SERP API to gather current web-based data for enhanced decision-making. # Data Sources and Collection The Advanced Human Like Career Advisor builds its expertise from: User-uploaded career documents and academic resources. Authoritative career guidance books, manuals, and research papers. Web data fetched in real-time using the SERP API, ensuring every user query is answered with up-to-date information sourced directly from the web. # Dataset Description The dataset is dynamically assembled and includes: Structured Content: Well-formatted PDFs, manuals, and reports. Semi-Structured Content: Career guidance documents with tables, diagrams, and annotations. Unstructured Content: Free-form text from online articles, expert blogs, and real-time SERP API results, providing a comprehensive view of career trends. # Methodology Explanation ### Technical Overview: Data Processing: The system extracts and cleans career-related content from various sources, then converts it into vector embeddings using state-of-the-art models. Integration with RAG and SERP API: By employing a retrieval-augmented generation framework along with SERP API, it minimizes hallucinations and ensures responses are contextually and factually accurate by incorporating current web results. Continuous Learning: The advisor incorporates user feedback and the latest web data to constantly refine its recommendations. ### Key Steps in Methodology: User Document Upload: Career-related documents are ingested and processed. Embedding Generation: Information is transformed into vectors for efficient retrieval. Query Handling with SERP API: User queries are matched with relevant stored content, while the SERP API provides current web results to enhance accuracy. Personalized Response Delivery: An advanced language model generates context-aware, detailed career guidance using both the curated data and web results from the SERP API. # Implementation Details Backend: Built on scalable frameworks, the system uses Python and integrates advanced libraries for NLP and vector processing, along with seamless SERP API integration to fetch real-time web data. Uses Langchain for AI related tasks. Frontend: A responsive, multi-platform interface developed using modern web and mobile technologies ensures seamless user interaction using streamlit. # Who Can Benefit from Advanced Human Like Career Advisor Students & Recent Graduates: Receive tailored advice on academic paths, internships, and career entry strategies enhanced with up-to-date web insights via SERP API. Job Seekers: Gain insights into industry-specific skills and career trends with recommendations directly supported by current web data. Working Professionals: Enhance career progression with personalized growth plans and skill development recommendations, all validated by SERP API results. Educational Institutions & HR Departments: Integrate the advisor to support and scale career counseling services by providing current, web-based guidance for a diverse user base. # Code Repository & Documentation A comprehensive GitHub repository provides detailed setup instructions, sample implementation guides, and complete documentation for developers and organizations looking to deploy the Advanced Human Like Career Advisor. [Github Link](https://github.com/utkarshgupta2009/career_chatbot) # Clear Prerequisites and Requirements Before deploying Career Advisor Pro, ensure that you have the following: - **Technical Prerequisites:** - Basic proficiency in Python programming and command-line usage. - Familiarity with web application deployment and environment variable configuration. - **Software Requirements:** - Access to the SERP API and Google API services (API keys are required). - All required packages listed in the `requirements.txt` file. # Installation and Usage Instructions 1. Clone the repository: ```bash git clone https://github.com/utkarshgupta2009/career_chatbot ``` 2. Install required packages: ```bash pip install -r requirements.txt ``` 3. Configure environment variables: Create a `.env` file and include: ```plaintext SERPAPI_API_KEY=your_serpapi_api_key_here GOOGLE_API_KEY=your_google_api_key_here ``` 4. Launch the application: ```bash streamlit run app.py ``` # Deployment Considerations ### Infrastructure Requirements: Can work on basic system no high capacity system required. Integration: Seamless incorporation of the SERP API to fetch live web data. Compatibility with existing enterprise systems and educational platforms. Scalability: Auto-scaling infrastructure to handle a growing number of user interactions while maintaining low latency. # Monitoring and Maintenance Considerations Performance Monitoring: Continuous tracking of system latency, context retention, and response accuracy. Regular evaluation of SERP API response times and data relevance. Error Logging: Automated logging of errors and anomalies in response generation. Periodic audits of user interactions to identify and correct issues. System Updates: Scheduled updates to incorporate the latest web data, model refinements, and security patches. Visual Tool Demonstration Screenshots, video walkthroughs, and interactive demos are available as part of the publication, showcasing the advisor’s intuitive interface, SERP API integration for web data retrieval, and context-aware guidance capabilities. ![Screenshot_10-3-2025_16250_.jpeg](Screenshot_10-3-2025_16250_.jpeg) ![Screenshot_10-3-2025_162837_.jpeg](Screenshot_10-3-2025_162837_.jpeg) ![Screenshot_10-3-2025_162650_.jpeg](Screenshot_10-3-2025_162650_.jpeg) # Language Processing Language Model Integration: I leveraged Google's Gemini as our core language model, enhanced with a RAG (Retrieval-Augmented Generation) architecture for superior response generation. This powerful combination allows Gemini to access our curated knowledge base while generating contextually relevant and natural responses. The implementation utilizes Langchain's tools to seamlessly integrate Gemini with various data processing components. # Context Management The system maintains conversation coherence by tracking user interaction history and passing it to Gemini along with each new query. This enables the model to provide highly personalized guidance by referencing previous discussions and understanding the user's established career interests and goals. The context-aware responses are achieved by carefully structuring the conversation history and relevant retrieved information before passing it to Gemini. ![Screenshot_10-3-2025_162837_.jpeg](Screenshot_10-3-2025_162837_.jpeg) Figure - Context management and no hallucinations # Architecture The architecture of the system is designed to ensure seamless conversation coherence and personalized guidance. User interaction history is tracked and passed to Gemini along with each new query, allowing previous discussions to be referenced and the user's established career interests and goals to be understood. Context-aware responses are generated by structuring the conversation history and relevant information retrieved from the knowledge base before being fed into Gemini. This architecture allows highly personalized, continuous, and contextually relevant career guidance to be delivered. E. Knowledge Base Management and Response Generation The system maintains a dynamic knowledge base through vectorization and embeddings of our PDF content and web-scraped data. Using Python as our primary programming language, we implemented an efficient pipeline that: ● Processes user queries through Gemini for initial understanding ● Uses FAISS to search our embedded knowledge base for relevant career guidance materials ● Integrates retrieved information with Gemini's capabilities for response generation ● Leverages Langchain's tools for seamless component integration The workflow for each user query: Query is processed by Gemini for intent understanding ● Relevant context is retrieved from FAISS-indexed knowledge base ● Web scraping via SERP API provides real-time information when needed ● Retrieved information and conversation history are structured and passed to Gemini ● Gemini generates personalized, contextually appropriate responses # Evaluation Framework The system’s effectiveness is measured using key performance indicators such as: Context Retention: Maintaining continuity across user sessions. Response Relevance: Achieving a high percentage of responses that are contextually and factually accurate (targeting above 90% relevance). User Satisfaction: Quantifying improvements in career clarity and decision-making support through user feedback. Response Accuracy: Comparing advice provided with authoritative web results from the SERP API. # Performance Characteristics and Requirements Response Time: Average query processing time is maintained under 3 seconds, including data retrieval via the SERP API. Scalability: Designed to support auto-scaling on cloud infrastructure, ensuring efficient handling of increased user interactions without significant latency. Resource Utilization: Optimized vectorization and semantic search mechanisms minimize computational overhead, even with extensive datasets. # Performance Metrics Analysis: The integration of LangChain significantly improved response quality. Without LangChain, responses lacked depth and personalization, whereas with LangChain, the chatbot delivered accurate, contextual, and highly relevant responses, ensuring a better user experience. |Metric| Without Lang chain| With Lang chain| | --- | --- | --- | |Context Awareness| Frequent hallucinations and context loss between conversations| Maintained 88% context retention across conversations with minimal hallucinations due to RAG implementation| |Response Relevance| Generic, non-personalized career advice| Contextual, personalized guidance with 92% relevance rate| |Query Scope Management| Responded to all queries, including non-career-related topics |Maintained strict focus on career guidance with 95% topic adherence| ![Screenshot_10-3-2025_162650_.jpeg](Screenshot_10-3-2025_162650_.jpeg)Figure - Topic Adherence Capabilities # Comparative Analysis ### When compared with traditional career guidance systems, the Advanced Human Like Career Advisor: Exceeds Generic Tools: Delivers nuanced, personalized advice rather than broad, one-size-fits-all recommendations. Improves Context Retention: Maintains continuity across sessions, unlike static or memoryless systems. Enhances Data Accuracy: Uses the SERP API to source current web data, ensuring that all recommendations are up-to-date and relevant. Focuses Exclusively on Career Guidance: Filters out non-relevant queries, unlike generic conversational agents. # Specific Use Case Analysis 1. Skills Development Guidance Example Query: "What skills should I develop for a career in Data Science?" Response Comparison: - Without Langchain: ● Generic list of skills ● No context or prioritization ● Limited explanation of importance ![Screenshot_10-3-2025_162353_.jpeg](Screenshot_10-3-2025_162353_.jpeg) Figure - Response Without LangChain and Career Model - With Langchain: ● Detailed skill breakdown ● Industry-relevant context ● Clear progression path ● Real-world application examples ![Screenshot_10-3-2025_16250_.jpeg](Screenshot_10-3-2025_16250_.jpeg) Figure - Response Using LangChain and Career Model 2. Context Follow-up Example Query: "Tell me more about the first point?" Response Comparison: - Without Langchain: ● Generated new, unrelated response ● Lost previous context ● Showed hallucinations - With Langchain: ● Referenced previous discussion ● Maintained context ● Provided relevant elaboration ● Sourced from knowledge base # Future Directions Potential improvements include: Multimodal Integration: Incorporating voice, video, and interactive visualizations for richer user engagement. Advanced Personalization: Enhancing user profiling with deeper personality assessments and adaptive learning paths. Expanded API Integrations: Including additional APIs for even broader real-time insights and more comprehensive data sourcing. Enhanced Analytics: Deploying sophisticated analytics to refine recommendations based on continuous user feedback. # Source Credibility To ensure the highest level of trust and accuracy, the Advanced Human Like Career Advisor: Verifies Data: Utilizes authoritative sources such as academic publications, industry-standard career manuals, and reputable web sources via the SERP API. Curates Content: Allows administrators to review and validate user-uploaded documents and fetched web data, ensuring that all guidance is based on reliable and current information. Regular Updates: Continuously refines its dataset with the latest information from trusted sources, maintaining a high standard of accuracy and relevance. # License and Usage Rights of the Technical Asset The Advanced Human Like Career Advisor is distributed under the MIT License, allowing for modification and distribution. This open-source model encourages community contributions and continuous improvement. Anyone can use their own personal guidance book if they have. Our system is not responsible for the books uploaded by user. # Contact Information For inquiries, further details, or collaboration opportunities, please contact: developer.utkarshgupta2009@gmail.com