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CLI reference

The rag-python command is installed with the package:

pip install rag-python
rag-python --help

Commands

Command Description
rag-python ingest Load documents into the vector store
rag-python query Ask a question against ingested documents
rag-python docs Show built-in documentation in the terminal
rag-python --version Print package version

rag-python ingest

Load files or folders, chunk them, embed them, and store vectors in ChromaDB.

rag-python ingest PATH [PATH ...] [options]

Arguments

Argument Description
PATH One or more files or directories

Options

Option Description
--reindex Clear the vector store before ingesting
--llm-provider openai (default), azure_openai, anthropic, gemini, ollama
--embedding-provider openai (default), azure_openai, ollama, local
--llm-model Model or Azure deployment name
--embedding-model Embedding model name
--openai-api-key Override OPENAI_API_KEY
--ollama-base-url Ollama server URL

Supported file types

.txt .md .pdf .docx .csv .json .html

Examples

rag-python ingest ./data --reindex
rag-python ingest policy.pdf ./handbook --embedding-provider local

rag-python query

Run retrieval-augmented generation against ingested documents.

rag-python query QUESTION [options]

Arguments

Argument Description
QUESTION Natural-language question (multiple words are joined)

Options

Option Description
--retriever vector, multi_query (default), or hybrid
--no-multi-query Shortcut for --retriever vector
--metadata-filter JSON Chroma metadata filter
--stream Stream answer tokens to stdout
-v, --verbose Print evaluation scores and source paths
--llm-provider LLM backend (see ingest)
--embedding-provider Embedding backend (see ingest)

Examples

rag-python query "How many days of annual leave?"
rag-python query "PTO policy" --stream -v
rag-python query "benefits" --retriever hybrid
rag-python query "salary" --metadata-filter '{"filename": "hr-policy.pdf"}'
rag-python query "remote work" --llm-provider ollama --llm-model llama3.1

Metadata filter

Restrict retrieval to chunks matching Chroma metadata:

rag-python query "leave" --metadata-filter '{"filename": "policy.pdf"}'
rag-python query "docs" --metadata-filter '{"source": "/path/to/file.txt"}'

rag-python docs

Print user documentation in the terminal (no browser required).

rag-python docs [TOPIC]
rag-python docs --list

Topics

Topic Content
quickstart Install, ingest, query (default)
install pip extras and source install
cli Full CLI reference
config Environment variables and RAGConfig
providers LLM and embedding providers
features Pipeline overview

Examples

rag-python docs
rag-python docs cli
rag-python docs --list

Environment variables

Variable Used for
OPENAI_API_KEY Default OpenAI LLM + embeddings
ANTHROPIC_API_KEY Claude
GEMINI_API_KEY Gemini
AZURE_OPENAI_ENDPOINT Azure OpenAI
AZURE_OPENAI_API_KEY Azure OpenAI
OLLAMA_BASE_URL Local Ollama (default http://localhost:11434)
RAG_PYTHON_DATA_DIR Default document directory (./data)
RAG_PYTHON_CHROMA_DIR Vector store path (./chroma_db)

See Configuration for the full list.