Commit 764228
2025-10-12 09:32:51 Pooja Thorat: Updating V1.0, facing issues with uploading images| /dev/null .. Projects/Flowcast/Enovate IT-QMS-PL01-Project Plan-[FlowCast]-V10.md | |
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| + | # Enovate IT-QMS-PL01-Project Plan-[FlowCast]-V1.0 |
| + | |
| + | # **Tools & Services** |
| + | |
| + | 1. Gemini Flash 2.5/Gemini Flash 2.5 Lite/Gemini Flash 2.5 Pro |
| + | 1. Code analysis |
| + | 2. Steps and flow generation |
| + | 3. Video analysis |
| + | 4. Generation of timestamps, subtitles, transcripts |
| + | 5. Generate FFMPEG script to edit the video |
| + | 2. Gemini embedding model |
| + | 1. Steps and flow embedding |
| + | 3. Gemini Flash 2.5 TTS OR Tortoise TTS |
| + | 1. Voiceover generation |
| + | 4. Langchain to build automatic agents that will handle standardized communication with the AI models |
| + | 5. Minio docker to store video files, audio files, uploaded code |
| + | 6. Postgres database |
| + | 7. Postgres plugin pgvector for vector database OR Qdrant vector database OR Weaviate vector database |
| + | 8. **Paid service required [Gemini Developer API Paid Tier](https://ai.google.dev/gemini-api/docs/pricing)** |
| + | |
| + | # **Project Flow** |
| + | |
| + | 1. Setup Flow |
| + | 1. Cypress/Playwright repo uploaded by QA/project owner |
| + | 2. AI indexes the code-base and generates the steps being performed by the automation |
| + | 3. AI creates vector embedding for the steps and uses it as the source of truth |
| + | 4. Generate Vector embedding and metadata for the scripts |
| + | 1. Store the vector embedding into the vector database |
| + | 2. Store the metadata about all the steps into the database |
| + | 3. Store the code into storage bucket |
| + | 2. Prompting Flow |
| + | 1. User/QA Input Prompts(ex: Generate demo for adding user to org ) / Prompts through API |
| + | 2. Check if the video for the requested flow exists using filenames and metadata |
| + | 1. (YES)Serve the video |
| + | 3. Check if the requested flow exists in the script/repo |
| + | 1. (NO)Inform that the video for such flow cannot be generated in a positive manner |
| + | 4. Runs the automation script in the server |
| + | 1. Use video output flags to generate video for the automation |
| + | 5. Video output along with metadata is saved |
| + | 6. AI Agents |
| + | 1. Analysis for the video by AI |
| + | 2. Generate data for events: |
| + | 1. Timestamps |
| + | 2. Locations |
| + | 3. Type |
| + | 3. Generate transcripts, subtitles and voice-over for the video |
| + | 4. Generate FFMPEG command/script to combine the video overlays, voice-over, subtitles to generate new video |
| + | 7. Generate the edited demo video |
| + | 8. Output the demo video and serve to the user |
| + | 3. [URL](https://excalidraw.com/#json=4i66sXvJ1zdvc_WPLEtsK,okBD43RRjL2DifWRSahj6g) |
| + | |
| + | ![] |
| + | |
| + | ![][image2] |
| + | |
| + | # **AI Parts of the Challenge** |
| + | |
| + | 1. Code understanding and generating vector embedding and metadata for the flows |
| + | 2. Video analysis (events, transitions, etc) |
| + | 3. Video metadata generation |
| + | 4. Transcripts, subtitle, voiceover generation |
| + | 5. FFMPEG script/command generation |
| + | |
| + | # **Team plans and goals** |
| + | |
| + | 1. First 2 weeks goals to achieve: |
| + | 1. Finalise the backend architecture (monolith/microservices, vector db, cache, docs, ai tools) |
| + | 2. Finish the boilerplate of the backend |
| + | 3. Remove unnecessary elements from the UI and finalize the UI |
| + | 4. Have some testing scenarios for the qa ready |
| + | 2. First month goals to achieve: |
| + | 1. Have all the public and private apis ready |
| + | 2. Have the code upload \-\> indexing \-\> embedding flow ready (at least in some capacity) |
| + | 3. Have all the test scenarios ready and begin testing for the code upload flow |
| + | 4. Start working on the video generation and editing part |
| + | |
| + | # **Sequence Diagram** |
| + | |
| + | ![][image3] |
| + | |
| + | # **ERD** |
| + | |
| + | ![][image4] |
| + | |
| + | # **Technology Stack** |
| + | |
| + | 1. Frontend: React |
| + | 2. Backend: Microservice of Java and Python |
| + | 3. LLM: Gemini 2.5-FLash, gemini-embedding-001 |
| + | 4. AI Orchestration: LangChain |
| + | 5. Integrations: FFMPEG, Automations |
| + | 6. DB: Postgres (with pgvector) |
| + | 7. Deployment: Docker, Gitlab CI |
| + | |
| + | # |