CLI prompt system plan

help me brainstorm and plan how to create a list of prompts for CLIne to build the following : I want to write a program which takes a folder of .md files and creates a database from it which I can use local ollama inference to analyze and create a way to go through the contents of the folder of .md files

Absolutely, let’s break this down into clear steps and brainstorm how to create a CLI-driven prompt system (e.g. with [CLINE](https://cline.readthedocs.io/en/latest/) or a custom CLI orchestrator) to help guide and generate a **program that takes a folder of Markdown files, indexes them into a database**, and uses **local Ollama inference for semantic analysis**, with a **navigation or querying interface**. --- ## ✅ High-Level System Components 1. **Input Layer: Markdown Folder Reader** - Recursively reads `.md` files from a folder - Parses frontmatter & content - Generates metadata (date, title, tags, etc.) 2. **Processing Layer: Chunk + Embed** - Tokenize & chunk the Markdown (e.g., by paragraph, heading) - Embed each chunk using `Ollama + nomic-embed-text` (or another local embedding model) - Store embeddings and metadata in a database (e.g., ChromaDB) 3. **Query Layer: Natural Language Interface** - Accepts user queries - Embeds query and performs vector search - Optionally runs LLM (via Ollama) on top of search results for summarization, tagging, commenting, etc. 4. **UI Layer: CLI Navigation / TUI / Web** - CLI-based prompts (interactive or scriptable) - Navigate files, browse summaries - Optional: lightweight web UI or TUI (text user interface) --- ## 🧠 CLIne Prompt Design Brainstorm Each prompt should do one of the following: - Ask user for config/data - Chain into another prompt/task - Offer modularity (choose Markdown tokenizer, embedding model, etc.) - Save/recall previous inputs (CLIne supports that!) --- ### ⚙️ Prompts to Generate Core Workflow Let’s break it into phases. Here are candidate prompts and the file/code each might generate. --- #### 🗂 Phase 1: File Loader & Parser **Prompt 1: Folder Input** ```bash "Create a Python function that recursively finds all .md files in a folder and reads them into memory. Parse frontmatter and body separately." ``` **Prompt 2: Markdown Chunker** ```bash "Write a function that splits markdown files into chunks by heading or paragraph. Return each chunk with its parent file and metadata." ``` --- #### 💾 Phase 2: Embedding & DB Storage **Prompt 3: Local Embedding** ```bash "Create a wrapper around Langchain's OllamaEmbeddings using the model 'nomic-embed-text'. Use it to embed a list of Markdown chunks." ``` **Prompt 4: ChromaDB Integration** ```bash "Write a function that stores the embedded Markdown chunks in ChromaDB, using the parent filepath and heading as metadata." ``` **Prompt 5: Embedding Checkpointing** ```bash "Add logic to skip re-embedding files if they haven’t changed. Use file hash or modified time." ``` --- #### 🔍 Phase 3: Search & Query **Prompt 6: Vector Search** ```bash "Write a function that accepts a query, embeds it, and retrieves the top 5 matching markdown chunks from ChromaDB." ``` **Prompt 7: LLM Inference** ```bash "Wrap a local Ollama model (like mistral or llama3) to summarize or answer questions using the search results as context." ``` --- #### 💻 Phase 4: CLI Navigation Interface **Prompt 8: CLI with Options** ```bash "Create a CLI tool with options: 'index', 'search', 'summarize', 'list files', 'delete index'. Use argparse or click." ``` **Prompt 9: Search Result Renderer** ```bash "Write a terminal-friendly output formatter for displaying search results, showing snippet, filename, and context." ``` **Prompt 10: Interactive Search Loop** ```bash "Create an interactive loop that asks for queries and displays results until the user quits." ``` --- ## 🧩 Optional Advanced Prompts **Prompt: Persona/Commenting Layer** ```bash "Add a feature where search results are commented on by a local LLM persona, based on writing style or tone." ``` **Prompt: Metrics Extraction** ```bash "Write a function that extracts and tracks frequently used words, phrases, or named entities across all Markdown files." ``` **Prompt: TUI (Text UI)** ```bash "Create a text-based UI using Textual or Rich that allows navigating indexed markdown by topic, tags, or similarity." ``` --- ## 🧪 Example Cline Workflow File (Hypothetical) ```yaml steps: - id: read_md_files prompt: Create a Python function that recursively finds all .md files in a folder and reads them into memory... - id: chunk_md prompt: Write a function that splits markdown into paragraphs or sections... - id: embed_chunks prompt: Use Ollama embeddings to embed a list of Markdown chunks... - id: store_chroma prompt: Store the embedded chunks in ChromaDB using filename as metadata... - id: query_interface prompt: Create a CLI tool that allows searching and summarizing the markdown content... ``` --- ## 🛠️ Tools to Use | Component | Library/Tool | |------------------|----------------------------------| | Embeddings | `langchain_community.embeddings.OllamaEmbeddings` | | DB | `chromadb.PersistentClient` | | Markdown Parsing | `frontmatter`, `markdown`, or `mistune` | | CLI | `argparse`, `click`, or `prompt_toolkit` | | Tokenization | `nltk`, `tiktoken`, or `spacy` | | TUI | `rich`, `textual`, or `urwid` | --- Would you like a starter folder scaffold and a working example of the first 3 prompts? Or do you want to generate the CLIne `.yaml` file directly and run it through Cline?

Got it! You want to **create a list of prompts for CLIne** (an LLM prompt orchestration tool) that will help you **build a program** with these core features: - Input: a folder of `.md` files - Output: a database of the `.md` contents - Use local Ollama inference (local LLM) for analysis - Provide a way (CLI or interactive) to navigate, query, or analyze the markdown folder contents --- ## Let's brainstorm and plan a prompt list for this task: --- # Step 1: Understand the overall system architecture and goals **Prompt idea:** > "Explain the high-level architecture and key components needed for a Python program that: > - Accepts a folder path containing markdown `.md` files > - Parses and stores the content into a local vector database for semantic search > - Uses local Ollama LLM inference for content analysis and query answering > - Provides a CLI interface to search, browse, and analyze the markdown contents interactively" --- # Step 2: Reading and parsing markdown files **Prompt idea:** > "Write Python code that recursively reads all `.md` files in a specified directory, extracts the raw markdown content, and splits it into paragraphs or sections suitable for embedding." --- # Step 3: Generate embeddings for the markdown chunks **Prompt idea:** > "Provide a Python function that takes text chunks (from markdown files) and generates embeddings using a local Ollama model for semantic search." --- # Step 4: Create or connect to a local vector database to store embeddings **Prompt idea:** > "Explain how to initialize a local vector database (e.g., ChromaDB or FAISS) and store the embeddings with metadata including the source filename and position in the file." --- # Step 5: Building the query interface for the database **Prompt idea:** > "Write Python code that queries the local vector database with a text input, returning the most relevant markdown chunks with their source files." --- # Step 6: Use Ollama LLM for analyzing or summarizing query results **Prompt idea:** > "Create a Python function that sends the retrieved markdown chunks to a local Ollama LLM for generating an analysis, summary, or answer to a user's query." --- # Step 7: Putting it all together: CLI interface **Prompt idea:** > "Design a CLI app in Python that allows users to: > - Index a folder of `.md` files into the vector database > - Ask natural language questions or queries about the markdown content > - Receive analysis or summaries powered by local Ollama LLM" --- # Step 8: Bonus – advanced features - Incremental updating of the database with new files - Handling large markdown files with chunking strategies - Displaying highlighted snippets from matched markdown - Exporting query results or summaries to a file --- # Summary of Prompt List for CLIne | # | Prompt Summary | |-----|--------------------------------------------------------| | 1 | Explain high-level architecture and components | | 2 | Code to read and parse `.md` files into text chunks | | 3 | Generate embeddings from text chunks using Ollama LLM | | 4 | Initialize and store embeddings in a local vector DB | | 5 | Query vector DB for relevant chunks | | 6 | Analyze or summarize retrieved chunks via Ollama LLM | | 7 | Build CLI to index, query, and display analysis | | 8 | Bonus: incremental update, chunking, highlighting | --- Would you like me to help you write the **exact CLIne prompt templates** for each of these steps? Or start by generating code for a particular step?