rag-kmk 0.0.55

A simple RAG implementation for educational purposes implemented by Murat Karakaya Akademi

rag-kmk

A compact helper library for small Retrieval-Augmented Generation (RAG) workflows.

• Free software: MIT License
• Docs: see docs/ for examples and developer notes

Quick install

pip:

pip install rag-kmk

From source:

git clone https://github.com/kmkarakaya/rag-kmk.git
cd rag-kmk
pip install -e .

Quick start — unified rag_client interface

from rag_kmk import rag_client

rag = rag_client()  # Optionally: rag_client(config_path="path/to/config.yaml")

# List collections
print(rag.list_collections())

# Create a collection
print(rag.create_collection("my_collection"))

# Add documents to a collection
print(rag.add_doc("my_collection", doc_path="tests/sample_documents"))

# Summarize a collection
print(rag.summarize_collection("my_collection"))

# Chat with the collection
print(rag.chat("my_collection", prompt="What is this document about?"))

# Delete a collection
print(rag.delete_collection("my_collection"))

# Clean up
rag.close()

Vector DB API (ChromaDB) — Consistent Client-based Usage

All vector DB operations now require an explicit ChromaDB client parameter for clarity and efficiency.
You must first create a client, then pass it to all DB functions.

from rag_kmk.vector_db.database import (
    create_chromadb_client,
    create_collection,
    load_collection,
    list_collection_names,
    summarize_collection,
    delete_collection,
    ChromaDBStatus,
)

# 1. Create/load persistent ChromaDB client
client_result = create_chromadb_client()
if client_result['client'] is None:
    raise RuntimeError(client_result['error'])
client = client_result['client']

# 2. List all collections
collections_result = list_collection_names(client)
print(collections_result)

# 3. Create a new collection
create_result, created_collection = create_collection(client, "my_collection")
print(create_result)

# 4. Load a collection
load_result, loaded_collection = load_collection(client, "my_collection")
print(load_result)

# 5. Summarize a collection
if loaded_collection:
    summary = summarize_collection(loaded_collection)
    print(summary)

# 6. Delete a collection
delete_result = delete_collection(client, "my_collection")
print(delete_result)

Example: Minimal run.py

from rag_kmk import CONFIG
from rag_kmk.vector_db.database import (
    create_chromadb_client,
    create_collection,
    load_collection,
    list_collection_names,
    summarize_collection,
    delete_collection,
    ChromaDBStatus,
)
import json

# Update config if needed
CONFIG['llm']['model'] = 'gemini-2.5-flash'

# Create/load client
client_result = create_chromadb_client()
if client_result['client'] is None:
    print(client_result['error'])
    exit(1)
client = client_result['client']

# List collections
collections_result = list_collection_names(client)
print(json.dumps(collections_result, indent=2))

# Create collection
collection_name = "my_new_collection"
create_result, created_collection = create_collection(client, collection_name)
print(json.dumps(create_result, indent=2))

# Load collection
load_result, loaded_collection = load_collection(client, collection_name)
print(json.dumps(load_result, indent=2))

# Summarize collection
if loaded_collection:
    summary_result = summarize_collection(loaded_collection)
    print(json.dumps(summary_result, indent=2))

# Delete collection
delete_result = delete_collection(client, collection_name)
print(json.dumps(delete_result, indent=2))

Configuration

Important config keys (see rag_kmk/config/config.yaml):

• llm:
  • api_key — direct API key (not recommended in source)
  • api_key_env_var — name of environment variable that holds the API key
  • model — model identifier used by the configured LLM backend
  • system_prompt — optional system instruction
• vector_db:
  • chromaDB_path — filesystem path for persistent ChromaDB; set to a directory path for persistent storage

Notes:

• Legacy key chroma_db is accepted and normalized to chromaDB_path by load_config().
• Use rag_kmk.config.config.mask_config(cfg) when printing or logging config to avoid leaking secrets.
• Prefer calling initialize_rag() or load_config() explicitly in long-running programs instead of relying on the import-time CONFIG population.

API reference (short)

Primary helpers and their key parameters (one-line):

• rag_kmk.initialize_rag(custom_config_path=None) -> dict
  • Loads config using load_config() and returns the config dict.
• rag_kmk.config.config.load_config(config_path=None) -> dict
  • Loads and normalizes repository config (populates module CONFIG).
• rag_kmk.config.config.mask_config(config, keys=('api_key','api_key_env_var')) -> dict
  • Returns a shallow copy with sensitive values masked for safe logging.
• rag_kmk.knowledge_base.document_loader.build_knowledge_base(collection_name: str, document_directory_path: Optional[str]=None, add_documents: bool=False, chromaDB_path: Optional[str]=None, cfg: Optional[dict]=None, overwrite: bool=False) -> (collection, ChromaDBStatus)
  • Create (or open) a collection and optionally ingest documents.
• rag_kmk.knowledge_base.document_loader.load_knowledge_base(collection_name: str, cfg: Optional[dict]=None) -> (collection or None, ChromaDBStatus)
  • Open-only helper (does not create directories).
• rag_kmk.vector_db.database.create_chromadb_client(chromaDB_path=None) -> {'status': str, 'client': client or None, 'error': str or None}
• rag_kmk.vector_db.database.create_collection(client, collection_name) -> (result_dict, collection or None)
• rag_kmk.vector_db.database.load_collection(client, collection_name) -> (result_dict, collection or None)
• rag_kmk.vector_db.database.list_collection_names(client) -> {'status': str, 'collections': list, 'error': str or None}
• rag_kmk.vector_db.database.summarize_collection(chroma_collection) -> {'status': str, 'summary': dict, 'error': str or None}
• rag_kmk.vector_db.database.delete_collection(client, collection_name) -> {'status': str, 'success': bool, 'error': str or None}
• rag_kmk.vector_db.database.ChromaDBStatus
  • Enum-like statuses (CLIENT_READY, COLLECTION_CREATED, COLLECTION_LOADED, COLLECTION_LISTED, SUMMARY_READY, etc.)
• rag_kmk.chat_flow.llm_interface.build_chatBot(config: Optional[dict]=None) -> ChatClient
  • Lazily builds an LLM-backed ChatClient or returns a no-op client when SDK/creds missing.
• rag_kmk.chat_flow.llm_interface.generate_LLM_answer(client, prompt: str, timeout_seconds: int=30, **opts) -> str
  • Runs client generation with a timeout and returns text output.
• rag_kmk.chat_flow.llm_interface.run_rag_pipeline(client, kb_collection, non_interactive: bool=False)
  • Small interactive loop (prints to stdout); supply non_interactive=True in scripts/CI.
• rag_kmk.utils.compute_fingerprint(path: str) -> str
  • SHA256 hex digest for a file; raises FileNotFoundError if missing.
• rag_kmk.utils.now_isoutc() -> str
  • Current UTC timestamp as ISO8601 string ending with 'Z'.

If you need exact parameter details, consult the module source in rag_kmk/ (this README aims to be a concise reference).

Persistence & semantics

Path resolution precedence used by build_knowledge_base():

1. explicit chromaDB_path argument
2. cfg.get('vector_db', {}).get('chromaDB_path') returned by load_config()
3. default: ./chromaDB created under the current working directory

• Notes on persistence behavior (persistent-only):
• The library requires a filesystem path for persistent ChromaDB. Pass a directory to chromaDB_path or configure vector_db.chromaDB_path in the config.
• Supplying a filesystem path forces persistent storage; build_knowledge_base will create the path if needed.

Development & testing

• Run tests:

pytest -q tests

• Coverage helper (repository includes a helper script):

scripts\run_coverage.bat

• An environment spec exists at env-rag-backup.yml.

Contributing & CI

• See docs/contributing.md for contribution guidelines.
• CI workflows are under .github/workflows/.

Troubleshooting & notes

• If the LLM SDK or credentials are missing the library returns a no-op ChatClient so non-LLM parts of the pipeline continue to work.
• generate_LLM_answer() enforces a timeout (default 30s) and raises a RuntimeError on timeout.
• When debugging auth or model issues, print rag_kmk.config.config.mask_config(config) rather than the raw config to avoid leaking secrets.

Logging

The library uses Python's standard logging module. By default the package is non-invasive (it will not configure the global logging handlers so host applications remain in control).

• To enable file+console logging for development, set the environment variable RAG_KMK_AUTOLOG=1 before running your application. The library will read CONFIG['logging'] (see config.yaml) and create a rotating file at the configured path (default logs/rag_kmk.log) as well as stream logs to the console.
• You can also programmatically initialize logging from your application using the helper rag_kmk.logging_setup.init_logging_from_config(config, force=False).

PowerShell example to run the sample runner with logging enabled:

$env:RAG_KMK_AUTOLOG = "1"
python run.py

Or programmatically (no env var):

python - <<'PY'
import rag_kmk.logging_setup as ls
ls.init_logging_from_config(None, force=True)
import run
PY

Log file location and rotation are configurable via CONFIG['logging'] keys: file, level, max_bytes, and backup_count.

What's new (changelog fragment)

• All vector DB operations now require an explicit client parameter for clarity and efficiency.
• README and run.py updated to reflect the new API.
• Clarified persistence resolution (explicit arg > config > default) and removed references to a non-existent force_persistence parameter.

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For more examples and developer notes see docs/ and run.py (canonical usage example).