pyragcore 0.1.10

A reusable RAG core library built on FAISS and Ollama

🧠 pyragcore

A reusable, modular RAG (Retrieval-Augmented Generation) core library built on FAISS and Ollama. Use it as the foundation for any AI project that needs document ingestion, semantic search, and LLM-powered responses.

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Features

• 🗂️ FAISS vector store with persistence, deduplication, and metadata filtering
• 🔢 SentenceTransformer embeddings with GPU support
• 🔍 Semantic retrieval with MMR search and metadata filtering
• 🤖 Ollama LLM integration for local, private inference
• 🎙️ Voice input/output support
• 🧱 Abstract base classes for building custom pipelines
• 📦 Modular optional dependencies — install only what you need

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Installation

pip install pyragcore          # core only (FAISS + tqdm + langchain-text-splitters)
pip install pyragcore[embeddings]  # + SentenceTransformers
pip install pyragcore[ollama]      # + Ollama LLM
pip install pyragcore[voice]       # + speech input/output
pip install pyragcore[all]         # everything

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Quick Start

from pyragcore.pipeline.base_pipeline import BasePipeline
from pyragcore.embeddings.embedder import Embedder
from pyragcore.retrieval.vector_store import VectorStore
from pyragcore.retrieval.retriver import Retriever
from pyragcore.llm.responder import Responder

# Extend BasePipeline for your use case
class MyPipeline(BasePipeline):
    def ingest(self, source: str) -> str:
        # implement your ingestion logic
        ...

pipeline = MyPipeline(persist_dir="./memory", output_folder="./output")
source_id = pipeline.ingest("./my_document.pdf")
answer = pipeline.ask("What is this document about?", source_id=source_id)
print(answer)

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Architecture

pyragcore/
├── embeddings/
│   └── embedder.py          # SentenceTransformer wrapper with GPU support
├── retrieval/
│   ├── vector_store.py      # FAISS vector store with persistence
│   └── retriver.py          # Semantic search with metadata filtering
├── ingestion/
│   ├── base_loader.py       # Abstract base loader
│   ├── base_chunker.py      # Abstract base chunker
│   └── chunker.py           # Token-based chunker (tiktoken)
├── llm/
│   ├── responder.py         # Ollama LLM wrapper
│   └── prompt.py            # Prompt builder with chat history
├── pipeline/
│   └── base_pipeline.py     # Abstract base pipeline
├── utils_io/
│   ├── voice.py             # Speech input/output
│   ├── choose_model.py      # Ollama model picker
│   └── logger.py            # Logging utility
└── exceptions.py            # Custom exceptions

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Building a Custom Pipeline

Extend BasePipeline and implement ingest():

from pyragcore.pipeline.base_pipeline import BasePipeline
from pyragcore.ingestion.base_loader import BaseLoader
from pyragcore.ingestion.chunker import Chunker
from tqdm import tqdm

class MyLoader(BaseLoader):
    def read(self, path) -> dict:
        # read your source and return
        return {
            "text": "...",
            "metadatas": {
                "file_id": "unique_id",
                "file_name": "my_file.txt",
                "source": path,
            }
        }

class MyPipeline(BasePipeline):
    def __init__(self, persist_dir: str, output_folder: str, model_name: str = "llama3.2"):
        super().__init__(persist_dir, output_folder, model_name)
        self.chunker = Chunker()

    def ingest(self, source: str) -> str:
        loader = MyLoader()
        content = loader.read(source)
        text = content.get("text", "")
        metadata = content.get("metadatas", {})
        source_id = metadata.get("file_id", "")

        if self._is_ingested(source_id):
            print("Already ingested, skipping...")
            return source_id

        chunks = self.chunker.chunk(text, metadata)
        documents, metadatas, ids = [], [], []

        for i, item in enumerate(chunks):
            documents.append(item["chunk"])
            metadatas.append(item["metadatas"])
            ids.append(f"{source_id}_chunk_{i}")

        BATCH_SIZE = 64
        all_embeddings = []
        for start in tqdm(range(0, len(documents), BATCH_SIZE), desc="Embedding"):
            batch = documents[start:start + BATCH_SIZE]
            all_embeddings.extend(self.embedder.embed(batch))

        self.vector_store.add(
            embeddings=all_embeddings,
            documents=documents,
            metadata=metadatas,
            ids=ids
        )
        return source_id

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VectorStore

from pyragcore.retrieval.vector_store import VectorStore

store = VectorStore(dim=768, persist_path="./memory", autosave=True)

# add documents
store.add(embeddings=[[...]], documents=["text"], metadata=[{"file_id": "abc"}], ids=["id_0"])

# search
results = store.search(query_embedding=[...], k=5)

# search with filter
results = store.search_with_filter(query_embedding=[...], k=5, where={"file_id": "abc"})

# MMR search for diversity
results = store.mmr_search(query_embedding=[...], k=5, lamda_param=0.5)

# list ingested files
files = store.list_files()

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Embedder

from pyragcore.embeddings.embedder import Embedder

embedder = Embedder(model_name="all-mpnet-base-v2")

# embed multiple texts
embeddings = embedder.embed(["text one", "text two"])

# embed a single query
embedding = embedder.embed_one("what is a database?")

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Requirements

• Python 3.13+
• Ollama installed and running (for LLM features)
• NVIDIA GPU with CUDA 12.8+ (optional, falls back to CPU)

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PyTorch with CUDA

pyragcore does not pin a specific PyTorch version to stay flexible. Install the version that matches your system:

# CUDA 12.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

# CPU only
pip install torch torchvision

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Exceptions

from pyragcore.exceptions import (
    BotRagException,        # base exception
    EmbeddingException,     # embedding failed
    RetrievalException,     # retrieval failed
    VectorStoreException,   # vector store error
    ModelNotFoundException, # ollama model not found
)

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Projects Built with pyragcore

• StudyBot — Chat with your documents and YouTube videos
• Coder-Assistant — AI assistant for your codebase (WIP) (Soon)

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Contributing

1. Fork the repo
2. Create a feature branch (git checkout -b feature-name)
3. Commit your changes (git commit -m "Add feature")
4. Push to the branch (git push origin feature-name)
5. Open a Pull Request

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License

MIT