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Feb 05, 20254 min read

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AI Engineering Assistant Technology Recommendations

Gemini

Prompt

Based on the following notes, can you recommend some technologies I should consider starting with?

Response

Based on your notes, here are some technologies you could consider starting with for your AI Engineering Assistant project:

1. Local LLM Infrastructure:

  • Ollama: You’ve already started with this, which is a great foundation. Explore its capabilities further. Experiment with different model sizes and configurations to understand its strengths and limitations.
  • vLLM: Known for its high performance and efficiency, especially on GPUs. Consider it if performance is a critical factor.
  • Explore other local LLM options: Research alternatives like aprhodite or TGI to see if they better suit your specific needs.

2. Programming Languages and Frameworks:

  • Python: A popular choice for AI/ML development with a rich ecosystem of libraries.
  • Libraries:
    • LangChain: A powerful framework for building LLM applications, including those that interact with external tools and APIs.
    • Transformers: A library for working with state-of-the-art transformer models (like those used in LLMs).
    • Libraries for interacting with your tools:
      • If you use Git, explore libraries like pygit2.
      • For IDE integration, look into their respective APIs or SDKs.

3. Data Management and Storage:

  • Vector databases: Tools like Faiss, Milvus, or Pinecone can efficiently store and retrieve embeddings generated by your LLM. This is crucial for tasks like semantic search and personalized recommendations.
  • Database for storing and retrieving code, documents, and other relevant data: Consider a relational database (like PostgreSQL) or a document database (like MongoDB) depending on your data structure and access patterns.

4. Agent-Based Systems:

  • Research existing agent frameworks: Explore frameworks that can help you define and manage the behavior of your AI assistant.
  • Consider a simplified approach initially: Start with a basic agent that can execute simple commands and gradually increase its complexity.

5. User Interface (UI):

  • Choose a UI framework that suits your preferences: Options include web frameworks (like Flask, Django, or Streamlit), desktop applications (using libraries like PyQt or Tkinter), or even a simple command-line interface.

Next Steps:

  1. Proof of Concept: Start with a small, well-defined project to test your chosen technologies. For example, create a simple agent that can:
    • Generate basic code snippets (e.g., boilerplate code for a specific framework).
    • Summarize a technical article.
    • Answer simple questions about your codebase based on comments and function names.
  2. Iterate and Refine: Continuously evaluate and improve your system. Gather feedback, experiment with different approaches, and learn from your successes and failures.
  3. Prioritize Privacy and Security: Implement security measures to protect your data. Consider using encryption and secure storage mechanisms.

Remember to break down the project into smaller, manageable tasks. This will make it easier to track progress and stay motivated. Good luck with your AI Engineering Assistant project!

llama3.2 (Obsidian Local GPT Plugin → ollama)

Prompt

Based on the above notes, can you recommend some technologies I should consider starting with?

1. Natural Language Processing (NLP) Libraries

  • Consider using popular NLP libraries such as:
    • NLTK (Natural Language Toolkit)
    • spaCy
    • Stanford CoreNLP
  • These libraries can help with tasks like text processing, entity recognition, and sentiment analysis.

2. Machine Learning Frameworks

  • Popular machine learning frameworks include:
    • TensorFlow
    • PyTorch
    • Scikit-learn
  • These frameworks can be used to build and train AI models that power your assistant’s capabilities.

3. AI Agent Platforms

  • Consider using platforms like:
    • Ollama (as you’ve already set up)
    • Rasa
    • Dialogflow
  • These platforms provide pre-built components and tools for building conversational AI agents.

4. Integration with Engineering Tools

  • Look into integrating your assistant with popular engineering tools such as:
    • GitHub API
    • GitLab API
    • Visual Studio Code API
    • Jupyter Notebook API
  • This will enable your assistant to interact with these tools and provide features like code completion, suggestion, and review.

5. Data Storage and Management

  • Consider using data storage solutions like:
    • SQLite
    • MongoDB
    • PostgreSQL
  • These databases can help manage and store user data, configuration settings, and other relevant information.

6. Web APIs and Frontend Frameworks

  • Use web frameworks like:
    • Flask (Python)
    • Django (Python)
    • Express.js (JavaScript)
  • These frameworks will enable you to build a RESTful API for your assistant’s frontend components.

7. Voice Assistant Libraries

  • Consider using libraries like:
    • Google Cloud Speech-to-Text
    • Mozilla DeepSpeech
    • PyAudio
  • These libraries can help with voice recognition and processing tasks.

Next Steps

  1. Research each technology thoroughly to determine which ones best fit your project’s requirements.
  2. Start by building a minimal viable product (MVP) using the recommended technologies.
  3. Iterate on your MVP, adding features and improving performance as you go.
  4. Consider collaborating with other developers or joining online communities for support and guidance.

By following these steps, you can build a powerful AI engineering assistant that leverages the latest technologies to improve your productivity and efficiency.

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