feat(forum-auto-response): Implement forum auto response #7766
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- pgvector for psql to support vector storage and operations - neigbor for code easier db migrations including vectors - langchainrb and ruby-openai for LLM services - pdf-reader for reading text from pdff
- openai api key is not complete
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- initialise LLM models that will be used in code - LANGCHAIN_OPENAI model is used for normal RAG operations - RAGAS (Retrieval Augmented Generation Assessment) model is used for evaluation of RAG
- add course_material_text_chunks which belongs to course_materials - add course_material_text_chunkings which belongs to course_materials and trackable_jobs - add workflow_state column to course_materials table.
- add text_chunk model to represent segments of material after chunking, including content and associated embeddings - add text_chunking model to represent trackable text_chunking jobs
- trackable job that tracks text chunking of course material
- add create_text_chunks that create text chunks from material - add destory_text_chunks that destroy materials's text chunks - modify update so that if the file contents was updated (i.e new file upload) it will destroy current text chunks related to previous material - modify destroy to ensure that material cannot be deleted while material is still undergoing text chunk job
- only course owner or manager will be allowed to manage text chunks
- add workflow state to material model - material has_many text_chunks and has_one text_chunking
- add chunking service that handles the chunking of text and file - add llm service that handles services provided by llm which are text embedding and getting image caption
- update material view to include workflow state - update folder and subfolder permission view to include canManageKnowledgeBase
- create_text_chunks handle creation of text_chunks - destroy text_chunks handle deletion of text_chunks
- switch that create or destroy course material text chunks
Integrate new backend API changes by updating the UI components and related files: - Updated `store.ts` to handle new state management for the backend changes - Modified `operations.ts` to incorporate new API calls - Adjusted types in `types.ts` to reflect backend schema changes - Updated React components to work with the updated state and API logic
- add course_material_text_chunk_references table - update course_material_text_chunks table
- update psql image to allow for pgvector
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- create course_forum_rag_auto_answerings table to keep track of auto answering jobs - add is_ai_generated, original_text and scores columns to course_discussion_posts
- rag_workflow_service handles response generation - response_evaluation_service evaluates quality of generated response using faithfulness and answer_relevance scores
- auto_answering controller logic is located in auto_answering_concern - reply will only be automatically generated for first post in forum topic - non course manager/owner will not be able to see drafted post - identity of automated response will be the course creator - identity of manually requested AI response will be the manger/owner that clicks the publish/mark as answer and publish button
- add generate reply button allows for manual request of draft AI generated responses - add mark as answer and publish button that marks and publish draft AI generated responses - add publish button that publish draft AI generated responses - only course manager and course owner can see draft AI generated responses and generate reply button
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Feature: Forum Auto Response
Overview
Implemented forum auto-response functionality with the following key features:
Changes
Setup Note
In order for the text chunking ability to work you need to have 2 things:
Limitations
TODO