Morphik Core AI Integration

search this repo: https://github.com/morphik-org/morphik-core and then search this guide: https://danielkliewer.com/blog/2025-03-30-Building-a-Personalized-AI-Learning-System-with-Local-LLM Now integrate the two into one project

Integrating Morphik Core with the personalized AI learning system described by Daniel Kliewer can enhance the system's capabilities in handling unstructured data and providing advanced retrieval-augmented generation (RAG) features. Here's how you can achieve this integration: **1. Understand the Components:** - **Morphik Core:** An open-source database designed for AI applications, offering advanced RAG capabilities with multi-modal support, knowledge graphs, and intuitive APIs. It efficiently manages unstructured data and scales to handle millions of documents. citeturn0search0 - **Personalized AI Learning System:** As outlined in Daniel Kliewer's guide, this system dynamically generates lessons tailored to individual learners using a knowledge graph and a local LLM, ensuring content adapts based on user progress. citeturn0search1 **2. Integration Steps:** - **Set Up Morphik Core:** - Follow the [Docker Setup Guide](https://github.com/morphik-org/morphik-core/blob/main/DOCKER.md) to deploy Morphik Core. This setup includes the core API, PostgreSQL with pgvector, and Ollama for AI models. - **Integrate with the Backend:** - Modify the FastAPI backend of the learning system to interact with Morphik Core's API. This allows for efficient storage and retrieval of unstructured data, such as lesson content and user interactions. - **Utilize Knowledge Graphs:** - Leverage Morphik Core's knowledge graph integration to map and manage relationships between learning concepts. This enhances the system's ability to generate contextually relevant and personalized lessons. - **Implement Advanced RAG Features:** - Incorporate Morphik Core's multi-stage retrieval and reranking capabilities to improve the quality and relevance of AI-generated lesson content. - **Ensure Data Flow Consistency:** - Establish a seamless data flow where user interactions update the knowledge graph within Morphik Core, enabling the AI layer to generate lessons that reflect the learner's progress accurately. **3. Benefits of Integration:** - **Enhanced Data Management:** Morphik Core's support for unstructured data allows for efficient handling of diverse learning materials, including text, videos, and PDFs. - **Improved Personalization:** The integration enables more precise tracking of user progress and preferences, facilitating the generation of lessons that are increasingly tailored to individual learning paths. - **Scalability:** Morphik Core's architecture supports scaling, allowing the learning system to accommodate a growing number of users and learning materials without performance degradation. By integrating Morphik Core into the personalized AI learning system, you can create a robust, adaptive, and scalable platform that delivers highly personalized educational experiences.

now write a guide to do so step by step

Integrating Morphik Core into a personalized AI learning system enhances the system's ability to manage unstructured data and provides advanced retrieval-augmented generation (RAG) features. This step-by-step guide will help you integrate Morphik Core into the personalized AI learning system as described in Daniel Kliewer's guide. **Prerequisites:** - Basic knowledge of Python and web development. - Familiarity with FastAPI and Next.js frameworks. - Access to a system with Docker installed. **Step 1: Set Up Morphik Core** 1. **Clone the Morphik Core Repository:** ```bash git clone https://github.com/morphik-org/morphik-core.git cd morphik-core ```  2. **Deploy Using Docker:** Morphik Core provides a Docker setup that includes the core API, PostgreSQL with pgvector, and Ollama for AI models. ```bash docker-compose up -d ```  This command starts the necessary services in detached mode. citeturn0search0 **Step 2: Integrate Morphik Core with the Backend** 1. **Install the Morphik Python SDK:** In your backend project environment, install the Morphik SDK: ```bash pip install morphik ```  2. **Configure Connection to Morphik Core:** In your FastAPI backend, establish a connection to the Morphik Core server: ```python from morphik import Morphik morphik = Morphik("http://localhost:8000") ```  3. **Modify Ingestion Processes:** Update your content ingestion logic to utilize Morphik Core's capabilities. For example, to ingest a document: ```python doc = morphik.ingest_file("path/to/document.pdf", metadata={"category": "lesson"}) ```  This approach allows for efficient storage and retrieval of unstructured data, such as lesson content and user interactions. citeturn0search0 **Step 3: Leverage Knowledge Graphs for Personalized Learning** 1. **Create a Knowledge Graph:** Utilize Morphik Core's knowledge graph features to map relationships between learning concepts: ```python morphik.create_graph("learning_graph", filters={"category": "lesson"}) ```  2. **Integrate with Lesson Generation:** When generating lessons, query the knowledge graph to provide contextually relevant content: ```python response = morphik.query("Explain the concept of recursion", graph_name="learning_graph", hop_depth=2) ```  This integration ensures that the AI-generated lessons are tailored to the learner's progress and the relationships between concepts. citeturn0search0 **Step 4: Implement Advanced Retrieval-Augmented Generation (RAG) Features** 1. **Enhance Content Retrieval:** Use Morphik Core's multi-stage retrieval and reranking capabilities to fetch the most relevant content for lesson generation: ```python chunks = morphik.retrieve_chunks("What are the applications of machine learning?", filters={"category": "lesson"}, k=5) ```  2. **Generate Personalized Lessons:** Incorporate the retrieved chunks into your lesson generation pipeline to create personalized and context-rich content for learners. citeturn0search0 **Step 5: Update the Frontend to Reflect Changes** 1. **Modify API Endpoints:** Ensure that your Next.js frontend communicates with the updated FastAPI backend, reflecting the integration with Morphik Core. 2. **Display Knowledge Graph Visualizations:** Utilize libraries like React-Flow to visualize the knowledge graph, helping learners understand the relationships between concepts. citeturn0search1 **Step 6: Test the Integrated System** 1. **Run the Backend and Frontend:** Start both the FastAPI backend and Next.js frontend to test the system. 2. **Simulate User Interactions:** Engage with the system as a learner would, ensuring that lessons are generated dynamically and personalized based on progress. 3. **Verify Data Flow:** Confirm that user interactions update the knowledge graph and that subsequent lessons reflect these updates. By following these steps, you integrate Morphik Core into your personalized AI learning system, enhancing its ability to manage unstructured data, utilize knowledge graphs, and provide advanced RAG features. This integration results in a more adaptive and personalized learning experience for users.