bibirags 0.1.2

A simple, unified interface for RAG across multiple vector store backends (txtai, Chroma, Qdrant, LightRAG)

bibirags

A simple, unified interface for RAG (Retrieval-Augmented Generation) across multiple vector store backends.

bibirags wraps txtai, Chroma, Qdrant, and LightRAG behind a consistent three-function API so you can swap backends without rewriting your pipeline.

save_<backend>(chunks, rag_root, conf, ...)  → index documents
search_<backend>(query, rag_root, conf, ...) → retrieve chunks
query_<backend>(query, rag_root, conf, ...)  → retrieve + answer

All LLM and embedding calls go through LiteLLM via a single LitellmConfDict, meaning any model provider (OpenAI, Anthropic, Cohere, Ollama, etc.) works out of the box.

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Installation

Install the core package plus the backends you need:

# Qdrant only
pip install bibirags[qdrant]

# Chroma only
pip install bibirags[chroma]

# txtai only
pip install bibirags[txtai]

# LightRAG only
pip install bibirags[lightrag]

# All backends + document loading helpers
pip install bibirags[all]

Add [docs] to get PDF and TXT loading via LangChain:

pip install bibirags[qdrant,docs]

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Quick start

Build a LitellmConfDict

Every function takes a single conf dict instead of scattered llm_model / embed_model / api_key arguments:

from bibirags import LitellmConfDict

# OpenAI
conf: LitellmConfDict = {
    "embed_model": "text-embedding-3-small",
    "llm_model": "gpt-4o-mini",
    "api_key": "sk-...",          # falls back to OPENAI_API_KEY env var
}

# Ollama (local)
conf: LitellmConfDict = {
    "embed_model": "ollama/bge-m3:latest",
    "llm_model": "ollama/gemma3:8b",
    "api_base": "http://localhost:11434",
}

# Any LiteLLM-compatible proxy
conf: LitellmConfDict = {
    "embed_model": "openai/text-embedding-3-small",
    "llm_model": "openai/gpt-4o",
    "api_base": "https://my-proxy.example.com/v1",
    "api_key": "proxy-key",
}

Index raw text chunks

from bibirags import save_qdrant, search_qdrant, query_qdrant

chunks = [
    "The Eiffel Tower was completed in 1889.",
    "The Louvre is the world's largest art museum.",
    "Paris is the capital of France.",
]

conf = {"embed_model": "text-embedding-3-small", "llm_model": "gpt-4o-mini"}
rag_root = "./my_rag_index"

# 1. Index
save_qdrant(chunks, rag_root, conf)

# 2. Semantic search
results = search_qdrant("When was the Eiffel Tower built?", rag_root, conf)

# 3. RAG query → answer + source chunks
answer, sources = query_qdrant("When was the Eiffel Tower built?", rag_root, conf)
print(answer)

Load documents from disk

from bibirags import chunk_docs, save_chroma, query_chroma

conf = {"embed_model": "text-embedding-3-small", "llm_model": "gpt-4o"}
chunks = chunk_docs("./my_docs/", chunk_size=800, chunk_overlap=120)

save_chroma(chunks, "./chroma_index", conf)

answer, sources = query_chroma(
    "What does the contract say about termination?",
    rag_root="./chroma_index",
    conf=conf,
)

Using Ollama (local models)

from bibirags import save_txtai, query_txtai

conf = {
    "embed_model": "ollama/bge-m3:latest",
    "llm_model": "ollama/gemma3:8b",
    "api_base": "http://localhost:11434",
}

save_txtai(chunks, "./txtai_index", conf)
answer, sources = query_txtai("What happened in the news?", "./txtai_index", conf)

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Backends at a glance

Backend	Best for	Index format	Notes
Qdrant	Production workloads, filtering	Local files or server	Cosine similarity, rich payload filtering
Chroma	LangChain ecosystems	Local SQLite	Easy LangChain integration
txtai	All-in-one HuggingFace pipelines	SQLite + FAISS	Built-in pipeline support
LightRAG	Knowledge-graph RAG	Local JSON + vector	Graph-enhanced hybrid retrieval

---

API reference

LitellmConfDict

class LitellmConfDict(TypedDict, total=False):
    embed_model: str   # required for save/search/query
    llm_model:   str   # required for query
    api_base:    str   # optional – custom API endpoint
    api_key:     str   # optional – falls back to env vars

chunk_docs

chunk_docs(docs_path, chunk_size=800, chunk_overlap=120) → list[str]

Recursively loads .pdf and .txt files from docs_path and returns text chunks.

save_<backend>

save_qdrant(chunks, rag_root, conf)
save_chroma(chunks, rag_root, conf)
save_txtai(chunks, rag_root, conf)
save_lightrag(chunks, rag_root, conf)

search_<backend>

results: list[str] = search_qdrant(query, rag_root, conf, top_k=3)

Returns the top_k most relevant chunk texts.

query_<backend>

answer, sources = query_qdrant(query, rag_root, conf, top_k=3)

Returns (answer_string, list_of_source_chunks).

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Contributing

git clone https://github.com/yourname/bibirags
cd bibirags
pip install -e ".[dev]"
pre-commit install
pytest

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License

MIT – see LICENSE.