Knowledge Graph System Design

So here is what I am trying to build so I can rubber duck this. FastAPI backend using uvicorn chromadb sql_alchemy networkx which takes from the next.js frontend's react-dropzone any .md file I input Then I implement chunking of the .md file in order for it to extract concepts from the .md files stored as embeddings in the chromadb with id, name, description, embedding and any other meta data I want. Then using networkx I can get all relationships and use those as edges and the concepts generated along with the metadata as the nodes for the graph. Then I can add the rest of the logic for the knowledge graph in the KnowledgeGraph class, like get_prerequisites, get_next_concept, get_learning_path, recommend_next_concept I could have another service for the llm calls using ollama, local mistral usually ends up being the best balance for my set up at least. I can include all of the llm calls in a class like LLMService so I can easily add or edit the prompts. Then I could have an embedding service using sentence_transformers to generate embeddings for when I add db entries to the vector store. I can use sqlalchemy to create the models. The whole concept is that you can upload your .md files and it will construct a knowledge graph which you can interact with a chat window in the next.js frontend which will generate lessons based on the content of the .md files. So what I need to do next in planning this out... The flow is you upload .md files. Display the knowledge graph using ReactFlow with a chat window to interact with the knowledge graph is what I think the frontend should be. Then the backend is FastAPI which accepts the .md files at an API endpoint. It then chunks and processes the sections/concepts from each .md file. It uses SentenceTransformers to generate the embeddings and stores it in the chromadb with the metadata. Then using networkx I would construct the graph with the nodes being the concepts extracted from the .md files and the edges being the relationships between concepts. The knowledge graph API would have get_prequisites get_next_concept get_learning_path recommend_next_concept etc endpoints. The LLM will be Ollama with Mistral. It will generate the summaries, quizzes and lessons from each node. I will then be able to query the knowledge graph via the chat interface. The database will be sqlalchemy to store the knowledge graph relationships and then store the metadata and embeddings in ChromaDB. So next I need to define the chunking strategy, I posted about that earlier, I can reference that. Then I have to decide on what metadata I want for the nodes and what types of relationships between concepts I want to track. I need to chunk based on concepts rather than a more simple method. I need to figure out how I am going to weigh the edges of the graph. I need to define the prompt templates for the llm service and the API structure. Something like /generate_lesson, /summarize_concept etc. I need to finalize the sqlalchemy models and define the fastapi routes. Then I could test it. For chunking I want to use the sliding window approach for continuity ensuring overlapping context. For the nodes the metadata should be the id, name, description, embedding from sentence transformers, dependencies, examples and related concepts. The edges should be relationships so like the prerequisite, supporting, contrasting or sequential relationships could be defined. For weighing the edges I could use cosine similarity of embeddings to determine relatedness. I could use frequencey of cooccurrence in .md sections. I could also create LLM generated scores to rank concept importance. The API could be. /upload_md(POST) which accepts the .md extracts concepts, generates embeddigns and updates the graph. Then /get_prerequisites/{concept_id} /get_next_concept/{concept_id} get_learning_path/{concept_id} and /recommend_next_concept could all be for the graph aPI. For LLM services I could create /generate_lesson/{concept_id} /summarize_concept/{concept_id} and /generate_quiz/{concept_id} Then for the chat I could do a /chat(POST) to query the knowledge graph. The sqlalchemy models would be concept as nodes relationships as edges and concept info and embeddings as metadata. The frontend would use reactflow for visualization and a chat interface. Thanks for helping me think this through.

This is a solid plan, and it looks like you've thought through the architecture well. A few things to consider as you move forward: ### 1. **Chunking Strategy (Concept-Based Approach)** - Since you want concept-based chunking, you could use an LLM to classify sections of the `.md` files into distinct concepts before embedding. - Using a **sliding window approach** ensures continuity. You can also experiment with **hierarchical chunking** (titles, subtitles, bullet points). ### 2. **Metadata for Nodes & Edges** - **Nodes:** - `id`, `name`, `description`, `embedding`, `dependencies`, `examples`, `related_concepts`. - **Edges (Relationships):** - **Prerequisite** (Concept A → Concept B) - **Supporting** (Concept A expands on Concept B) - **Contrasting** (Concept A is different but related to Concept B) - **Sequential** (Concept A is typically learned before Concept B) - **Weights:** - Cosine similarity of embeddings. - Co-occurrence in `.md` files. - LLM-generated ranking scores. ### 3. **API Endpoints** - **Upload & Processing** - `/upload_md` (POST) → Extracts concepts, generates embeddings, updates the graph. - **Knowledge Graph API** - `/get_prerequisites/{concept_id}` - `/get_next_concept/{concept_id}` - `/get_learning_path/{concept_id}` - `/recommend_next_concept` - **LLM Services** - `/generate_lesson/{concept_id}` - `/summarize_concept/{concept_id}` - `/generate_quiz/{concept_id}` - **Chat Interface** - `/chat` (POST) → Query the knowledge graph. ### 4. **Database & Storage (SQLAlchemy + ChromaDB)** - **SQLAlchemy (PostgreSQL):** Stores structured graph data (concepts & relationships). - **ChromaDB:** Stores embeddings and metadata for fast similarity search. ### 5. **Frontend (Next.js + ReactFlow + Chat UI)** - **ReactFlow** for interactive visualization of the knowledge graph. - **Chat UI** for querying the graph & getting LLM-generated lessons. ### Next Steps: ✅ Define chunking strategy in code. ✅ Finalize metadata schema for nodes & edges. ✅ Implement API routes in FastAPI. ✅ Create database models (SQLAlchemy for graph relationships). ✅ Connect ChromaDB for embeddings. ✅ Prototype ReactFlow frontend. Let me know if you want to dive into any part of this in detail! 🚀

Your design outlines an ambitious, modular system that leverages a variety of tools to build an interactive knowledge graph from Markdown files. Here are some key considerations and suggestions to help refine your plan: ### 1. File Upload and Processing Flow - **Frontend:** - Use React Dropzone to allow drag-and-drop uploads of .md files. - Visualize the resulting knowledge graph with ReactFlow and integrate a chat interface. - **Backend:** - A FastAPI endpoint (e.g., `/upload_md`) receives the .md files. - Implement file validation and error handling. ### 2. Chunking and Concept Extraction - **Chunking Strategy:** - Adopt a sliding window approach to maintain continuity between chunks. - Ensure overlapping context so that no concept is lost at the boundaries. - **Concept Extraction:** - Parse the Markdown to detect logical boundaries (e.g., headings, bullet lists, or thematic breaks). - Consider using heuristics or an initial LLM pass to identify concepts if the structure is not explicit. ### 3. Embedding and Metadata Management - **Embedding Generation:** - Use SentenceTransformers to generate embeddings for each chunk or extracted concept. - **Metadata for Nodes:** - Store details such as ID, name, description, embedding, dependencies, examples, and related concepts. - Decide what additional metadata might be useful (e.g., source file reference, creation timestamp). - **ChromaDB Integration:** - Store the embeddings and metadata in ChromaDB for quick vector searches. ### 4. Knowledge Graph Construction with NetworkX - **Nodes:** - Each node represents a concept extracted from the .md files. - **Edges and Relationships:** - Define relationships such as prerequisite, supporting, contrasting, and sequential. - Consider multiple factors for weighing edges: - **Cosine Similarity:** Use the similarity of embeddings as a baseline for relatedness. - **Co-occurrence Frequency:** Count how often concepts appear together in chunks. - **LLM-Generated Scores:** Optionally refine edge weights with scores from LLM prompts. - **Graph Analysis:** - Utilize NetworkX functions to traverse the graph (e.g., for generating learning paths or prerequisites). ### 5. API Design and Endpoints - **Knowledge Graph Endpoints:** - `/get_prerequisites/{concept_id}`: Returns prerequisite concepts. - `/get_next_concept/{concept_id}`: Suggests subsequent topics based on the current concept. - `/get_learning_path/{concept_id}`: Generates a learning path through the graph. - `/recommend_next_concept/{concept_id}`: Provides recommendations based on graph metrics. - **LLM Service Endpoints:** - `/generate_lesson/{concept_id}`: Produces a detailed lesson. - `/summarize_concept/{concept_id}`: Offers a concise summary. - `/generate_quiz/{concept_id}`: Creates quiz questions for the concept. - **Chat Interface Endpoint:** - `/chat`: Accepts POST requests to interact with the graph and provide context-aware responses. ### 6. LLM Integration with Ollama/Mistral - **LLM Service Class:** - Encapsulate calls to the LLM in a dedicated class (e.g., `LLMService`) to abstract prompt management. - This allows for easy modifications of prompts and switching LLM providers if needed. - **Prompt Templates:** - Define clear, consistent prompt templates for each endpoint (lesson, summary, quiz). - Consider including context such as related nodes or edge weights to enrich responses. ### 7. Database and ORM Considerations - **SQLAlchemy Models:** - Define models for concepts (nodes) and relationships (edges). - Ensure that the models capture all necessary metadata and can support the queries needed for graph operations. - **Integration with ChromaDB:** - Maintain synchronization between the SQLAlchemy models and the vector store, ensuring that any updates to the knowledge graph are reflected in both. ### 8. Testing and Iteration - **Unit Tests:** - Test individual components (chunking logic, embedding generation, graph construction). - **Integration Tests:** - Simulate end-to-end flows from file upload to graph visualization and chat interactions. - **Iterative Refinement:** - Begin with a minimal viable product (MVP) that handles basic uploads and graph creation, then iterate on features like LLM interactions and advanced relationship weighting. ### Final Thoughts Your approach is well-structured and leverages modern libraries to build an interactive learning system. Balancing the granularity of the chunking process and fine-tuning the relationships within the graph will be key. As you proceed, consider prototyping small parts of the system (e.g., a single file upload and graph extraction) to validate the workflow before scaling up. Happy coding and good luck with your knowledge graph project!