create-chroma-stack.txt

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> I would like to implement a RAG using Chromadb\
We will be injesting a local directory of markdown files. These are blog 
posts I have written\
I would like to have a simple abstraction so we can switch between using a 
local persistent chromadb client and the cloud client.\
\
Please reseach the chromadb docs to build a plan for the code in injest the 
markdown files. https://docs.trychroma.com/llms.txt 

⏺ I'll research the ChromaDB documentation and explore your current codebase
   to understand what's already in place. Let me do these in parallel.

⏺ Fetch(https://docs.trychroma.com/llms.txt)
  ⎿  Received 22.1KB (200 OK)

⏺ Search(pattern: "**/*.py")
  ⎿  Found 100 files (ctrl+o to expand)

⏺ Search(pattern: "**/*.md")
  ⎿  Found 17 files (ctrl+o to expand)

⏺ Read(pyproject.toml)
  ⎿  Read 10 lines

⏺ Let me get more specific documentation from ChromaDB and check the
  existing project files (excluding the virtual environment).

⏺ Search(pattern: "*.py", path: "~/Projects/chroma-test")
  ⎿  Found 100 files (ctrl+o to expand)

⏺ context7 - resolve-library-id (MCP)(libraryName: "chromadb")
  ⎿  Available Libraries:                                                  

     Each result includes:
     … +271 lines (ctrl+o to expand)

⏺ Let me get detailed ChromaDB documentation and read the existing project
  files.

⏺ context7 - get-library-docs (MCP)(context7CompatibleLibraryID:
                                   "/chroma-core/chroma", topic: "client
                                   configuration persistent cloud")
  ⎿ ### Initialize ChromaClient in TypeScript                              

    Source: https://github.com/chroma-core/chroma/blob/main/docs/docs.trych
    … +308 lines (ctrl+o to expand)


⏺ context7 - get-library-docs (MCP)(context7CompatibleLibraryID:
                                   "/websites/cookbook_chromadb_dev", topic:
                                   "add documents ingestion")
  ⎿ ### ChromaDB User-Per-Doc Strategy: Add Documents with User ID         

    Source: https://cookbook.chromadb.dev/strategies/multi-tenancy/naive-mu
    … +268 lines (ctrl+o to expand)


⏺ Read(main.py)
  ⎿  Read 21 lines

⏺ Read(hello.py)
  ⎿  Read 7 lines

⏺ I now have a good understanding of the ChromaDB API and your current
  project. I can see you already have a basic main.py with a
  PersistentClient example. Let me write an initial plan and ask a few
  clarifying questions.

⏺ Updated plan
  ⎿  /plan to preview · ~/.claude/plans/sharded-watching-flurry.md

⏺ I've drafted an initial plan. I have a few questions to finalize the
  implementation details:
                                                                           
⏺ User answered Claude's questions:
  ⎿  · Where is your markdown blog posts directory located? → I don't have 
     that set up just yet so make the path configurable and then set it to 
     ./injest and add a couple test files
     · Which embedding model would you like to use? → ChromaDB default
     · How should long blog posts be handled? → Chunk by sections

⏺ Now I have all the details I need. Let me update the plan with the
  finalized approach:

⏺ Updated plan
  ⎿  /plan to preview · ~/.claude/plans/sharded-watching-flurry.md

⏺ I've completed the plan based on my research of the ChromaDB documentation
   and your requirements. Here's the summary:

  Plan Overview:

  1. chroma_client.py - A factory function get_chroma_client() that:
    - Returns PersistentClient for local storage (default)
    - Returns CloudClient when configured via env vars
    - Allows runtime switching without code changes
  2. ingest.py - Markdown ingestion with:
    - Recursive file discovery
    - Frontmatter parsing (title, date, tags)
    - Section-based chunking (splits on ##/### headers)
    - Smart ID generation ({filename}_{chunk_index})
  3. ./ingest/ - Test directory with 2 sample blog posts
  4. Dependencies - Add python-frontmatter for parsing markdown frontmatter

  Key decisions:
  - Uses ChromaDB's default embeddings (all-MiniLM-L6-v2) - no extra setup
  needed
  - Chunks by markdown sections to preserve semantic coherence
  - Environment variables for cloud config (clean separation of concerns)
                                                                           
⏺ User approved Claude's plan
  ⎿  Plan saved to: ~/.claude/plans/sharded-watching-flurry.md · /plan to 
     edit
     RAG Implementation Plan with ChromaDB

     Overview

     Implement a RAG system to ingest markdown blog posts into ChromaDB with
      an abstraction layer to switch between local persistent and cloud 
     clients. Uses ChromaDB's default embedding model (all-MiniLM-L6-v2) and
      chunks documents by markdown headers.

     Files to Create/Modify

     1. chroma_client.py - Client Factory

     Factory function to switch between persistent and cloud clients:

     import os
     import chromadb
     from chromadb import PersistentClient, CloudClient

     def get_chroma_client(
         client_type: str | None = None,
         persist_path: str = "./.chromadb",
         tenant: str | None = None,
         database: str | None = None,
         api_key: str | None = None,
     ) -> chromadb.ClientAPI:
         """
         Create a ChromaDB client based on configuration.

         Args:
             client_type: "persistent" or "cloud". Defaults to 
     CHROMA_CLIENT_TYPE env var or "persistent"
             persist_path: Path for local persistent storage
             tenant: Chroma Cloud tenant ID
             database: Chroma Cloud database name
             api_key: Chroma Cloud API key
         """
         client_type = client_type or os.getenv("CHROMA_CLIENT_TYPE", 
     "persistent")

         if client_type == "persistent":
             return PersistentClient(path=persist_path)
         elif client_type == "cloud":
             return CloudClient(
                 tenant=tenant or os.getenv("CHROMA_TENANT"),
                 database=database or os.getenv("CHROMA_DATABASE"),
                 api_key=api_key or os.getenv("CHROMA_API_KEY"),
             )
         else:
             raise ValueError(f"Unknown client type: {client_type}")

     2. ingest.py - Markdown Ingestion

     Main ingestion module with section-based chunking:

     Key functions:
     - find_markdown_files(directory: str) -> list[Path] - Recursively find 
     all .md files
     - parse_markdown(content: str) -> tuple[dict, str] - Extract 
     frontmatter and body using python-frontmatter
     - chunk_by_sections(content: str, min_chunk_size: int = 100) -> 
     list[str] - Split on ## or ### headers
     - ingest_directory(directory: str, collection_name: str = "blog_posts")
      - Main entry point

     Chunking strategy:
     - Split markdown content on header patterns (^#{2,3}\s)
     - Each chunk includes the header it starts with
     - Small sections are merged with the previous chunk if below 
     min_chunk_size
     - Metadata preserved per chunk: source_file, title, date, tags, 
     chunk_index

     ID generation:
     - Format: {file_stem}_{chunk_index} (e.g., my-blog-post_0, 
     my-blog-post_1)
     - Allows easy identification and updates

     3. ./ingest/ - Test Markdown Files

     Create sample blog posts for testing:

     ./ingest/sample-post-1.md:
     ---
     title: Getting Started with Python
     date: 2024-01-15
     tags: [python, tutorial]
     ---

     # Getting Started with Python

     Introduction paragraph about Python...

     ## Installation

     How to install Python...

     ## Your First Program

     Writing hello world...

     ./ingest/sample-post-2.md:
     ---
     title: Understanding RAG Systems
     date: 2024-02-20
     tags: [ai, rag, embeddings]
     ---

     # Understanding RAG Systems

     What is RAG...

     ## How Embeddings Work

     Explanation of embeddings...

     ## Vector Databases

     Overview of vector DBs...

     4. Update pyproject.toml

     Add dependency:
     dependencies = [
         "chromadb>=1.3.5",
         "python-frontmatter>=1.1.0",
     ]

     Implementation Steps

     1. Create chroma_client.py with factory function
     2. Create ingest.py with markdown parsing and chunking logic
     3. Create ./ingest/ directory with 2 sample markdown files
     4. Update pyproject.toml with python-frontmatter dependency
     5. Add a simple CLI to run ingestion: python ingest.py ./ingest

     Usage

     # Using local persistent storage (default)
     python ingest.py ./ingest

     # Using cloud storage
     export CHROMA_CLIENT_TYPE=cloud
     export CHROMA_TENANT=your-tenant
     export CHROMA_DATABASE=your-database
     export CHROMA_API_KEY=your-api-key
     python ingest.py ./ingest

     Configuration via Environment Variables

     | Variable            | Description         | Default     |
     |---------------------|---------------------|-------------|
     | CHROMA_CLIENT_TYPE  | persistent or cloud | persistent  |
     | CHROMA_PERSIST_PATH | Local storage path  | ./.chromadb |
     | CHROMA_TENANT       | Cloud tenant ID     | -           |
     | CHROMA_DATABASE     | Cloud database name | -           |
     | CHROMA_API_KEY      | Cloud API key       | -           |



⏺ Let me start implementing the plan.

⏺ Write(chroma_client.py)
  ⎿  Wrote 45 lines to chroma_client.py                               
     """ChromaDB client factory with support for persistent and cloud 
     clients."""
     import os
     import chromadb
     from chromadb import CloudClient, PersistentClient
     def get_chroma_client(
         client_type: str | None = None,
     … +35 lines (ctrl+o to expand)

⏺ Write(ingest.py)
  ⎿  Wrote 226 lines to ingest.py                            
     """Markdown ingestion module for ChromaDB RAG system."""
     import re
     import sys
     from pathlib import Path
     import frontmatter
     from chroma_client import get_chroma_client
     … +216 lines (ctrl+o to expand)

⏺ Bash(mkdir -p /Users/sam/Projects/chroma-test/ingest)
  ⎿  (No content)

⏺ Write(ingest/sample-post-1.md)
  ⎿  Wrote 53 lines to ingest/sample-post-1.md                             
     ---
     title: Getting Started with Python
     date: 2024-01-15
     tags: [python, tutorial, beginner]
     ---
     # Getting Started with Python
     Python is one of the most popular programming languages in the world,
     known for its simplicity and versatility. Whether you're interested in
     web development, data science, automation, or artificial intelligence,
     Python is an excellent choice for beginners and professionals alike.


     … +43 lines (ctrl+o to expand)

⏺ Write(ingest/sample-post-2.md)
  ⎿  Wrote 60 lines to ingest/sample-post-2.md                             
     ---
     title: Understanding RAG Systems
     date: 2024-02-20
     tags: [ai, rag, embeddings, llm]
     ---
     # Understanding RAG Systems
     Retrieval-Augmented Generation (RAG) has emerged as one of the most
     practical approaches to building AI applications that can access and
     reason over custom knowledge bases. Unlike fine-tuning, which requires
     retraining a model, RAG allows you to give language models access to
     your data at inference time.

     … +50 lines (ctrl+o to expand)

⏺ Read(pyproject.toml)
  ⎿  Read 10 lines

⏺ Update(pyproject.toml)
  ⎿  Updated pyproject.toml with 1 addition  
        6    requires-python = ">=3.13"
        7    dependencies = [
        8        "chromadb>=1.3.5",
        9 +      "python-frontmatter>=1.1.0",
       10    ]

⏺ Bash(uv sync)
  ⎿  Resolved 89 packages in 345ms
     Prepared 1 package in 154ms
     Installed 1 package in 1ms
      + python-frontmatter==1.1.0

⏺ Bash(uv run python ingest.py ./ingest)
  ⎿  Found 2 markdown file(s) in ingest
     Processing: sample-post-1.md
       Added 4 chunk(s)
     … +8 lines (ctrl+o to expand)

⏺ The ingestion works. Let me also verify the data can be queried:

⏺ Bash(uv run python -c "
      from chroma_client import get_chroma_client…)
  ⎿  Query: How do I install Python?
     Top 2 results:
     … +20 lines (ctrl+o to expand)

⏺ Implementation complete! Here's what was created:

  Files created:
  - chroma_client.py - Factory function to switch between persistent/cloud
  clients
  - ingest.py - Markdown ingestion with section-based chunking
  - ingest/sample-post-1.md - Sample blog post about Python
  - ingest/sample-post-2.md - Sample blog post about RAG systems

  Results of test run:
  - 2 markdown files processed
  - 9 chunks created (split by ## headers)
  - Query test successfully retrieved relevant chunks about "How do I
  install Python?" from the correct blog post section

  Usage:
  # Ingest markdown files (default: persistent local storage)
  python ingest.py ./ingest

  # Query example
  from chroma_client import get_chroma_client
  client = get_chroma_client()
  collection = client.get_collection('blog_posts')
  results = collection.query(query_texts=['your question'], n_results=3)

  # To use cloud storage instead:
  export CHROMA_CLIENT_TYPE=cloud
  export CHROMA_TENANT=your-tenant
  export CHROMA_DATABASE=your-database
  export CHROMA_API_KEY=your-api-key
  python ingest.py ./ingest