Technical Pipeline / Project Architecture
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
        The overall process for implementing the project is structured in the following pipeline:

    </i></span></p>


1. Global Project Architecture
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1. Data Collection & Preprocessing
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
      Extract data from existing sources such as PDF files, Excel spreadsheets, technical documents, and internal databases.
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    <p style="text-align: justify;"><span style="color:#000080;"><i>  
      Clean, structure, and normalize textual data to make it usable by AI models.
   
   </i></span></p>
    <p style="text-align: justify;"><span style="color:#000080;"><i>  
     Tools used may include <strong>PyMuPDF</strong>, <strong>Pandas</strong>, <strong>OpenRefine</strong>, etc.

    </i></span></p>
    
2. Knowledge Base Creation
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
   Organize the extracted data into a Knowledge Base.
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    <p style="text-align: justify;"><span style="color:#000080;"><i>  
    Structure the information by component, requirement, test type, and associated documentation.
   </i></span></p>
    <p style="text-align: justify;"><span style="color:#000080;"><i>  
    Possible technologies include <strong>PostgreSQL</strong>, <strong>MongoDB</strong>, and vector databases like <strong>FAISS</strong> or <strong>Weaviate</strong>.
    </i></span></p>
    
3. Benchmarking & Integration of LLM / RAG Models
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  

    Select and experiment with different language models (LLMs) such as GPT-4, Mistral, LLaMA, etc.
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    <p style="text-align: justify;"><span style="color:#000080;"><i>  
   Integrate a <strong>Retrieval-Augmented Generation (RAG)</strong>architecture that combines knowledge base search with text generation.
   </i></span></p>
    <p style="text-align: justify;"><span style="color:#000080;"><i>  
   Evaluate performance, accuracy of responses, and domain adaptation for the automotive context.
    </i></span></p>
    
4. Automatic Document Generation
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
   Use generative AI models to automatically produce:
   </i></span></p>

- Technical specifications
- Test plans
- Validation reports


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     <p style="text-align: justify;"><span style="color:#000080;"><i>  

    Supported formats: </span> <span style="color:#008000;">PDF,  </span><span style="color:#008000;">DOCX,  </span><span style="color:#008000;">JSON.
   </i></span></p>
    <p style="text-align: justify;"><span style="color:#000080;"><i> 
    Integration options: <strong>LangChain</strong>, <strong>LlamaIndex</strong>, or <strong>HuggingFace Transformers</strong>.
   </i></span></p>

5. Development of the Web Platform
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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
   - Create a user-friendly interface allowing:
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- Input of the component name
- Submission of questions
- Download of generated documents


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     <p style="text-align: justify;"><span style="color:#000080;"><i>  
   - Proposed technologies include: <strong>React.js</strong> or <strong>Vue.js</strong> (frontend), <strong>FastAPI</strong> or <strong>Django</strong> (backend), with <strong>Docker</strong> used for deployment.
   </i></span></p>


2. Key Application Features
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    <table>
            <tr>
                <th>Feature</th>
                <th>Description</th>
            </tr>
            <tr>
                <td><strong>Intelligent Search</strong></td>
                <td>Find requirements based on the component name.</td>
            </tr>
            <tr>
                <td><strong>AI Q&amp;A Agent</strong></td>
                <td>Answer questions regarding the component's technical details.</td>
            </tr>
            <tr>
                <td><strong>Document Generation</strong></td>
                <td>Generate test sheets, specifications, and validation reports.</td>
            </tr>
            <tr>
                <td><strong>Requirement Explanation</strong></td>
                <td>Provide in-depth explanations of requirement functions, impacts, and associated tests.</td>
            </tr>
        </table>

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 Expected Outcome
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    <p style="text-align: justify;"><span style="color:#000080;"><i>    
    An  <span style="color:#008000;">intelligent web platform</span><span style="color:#000080;"> that transforms a simple component name into a rich set of technical information and documents. This will significantly reduce the time required for analysis, documentation, and validation in the automotive domain.
    </i></span></p>


Technologies & Tools
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        <table>
            <tr>
                <th>Domain</th>
                <th>Tools / Technologies</th>
            </tr>
            <tr>
                <td><strong>Data Extraction</strong></td>
                <td>PyMuPDF, textract, Pandas</td>
            </tr>
            <tr>
                <td><strong>AI & NLP</strong></td>
                <td>OpenAI GPT, LangChain, HuggingFace</td>
            </tr>
            <tr>
                <td><strong>Vector Databases</strong></td>
                <td>FAISS, ChromaDB, Weaviate</td>
            </tr>
            <tr>
                <td><strong>Backend</strong></td>
                <td>FastAPI, Flask, Django</td>
            </tr>
            <tr>
                <td><strong>Frontend</strong></td>
                <td>React, Vue.js</td>
            </tr>
            <tr>
                <td><strong>Documentation</strong></td>
                <td>MkDocs, Sphinx, Notion</td>
            </tr>
        </table>

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