fullstack-rag-ai 0.1.5

Lightweight RAG library for internal knowledge systems

Fullstack RAG AI

Lightweight Retrieval-Augmented Generation (RAG) library for building internal knowledge systems using local documents and LLMs.

This library allows you to:

• Load documents from PDFs or raw text
• Automatically chunk and embed documents
• Store embeddings in a FAISS vector database
• Ask questions against the knowledge base
• Use deterministic caching to avoid repeated LLM calls
• Automatically detect document updates, additions, and deletions

It is designed to use:

• Ollama LLMs
• HuggingFace embeddings
• FAISS vector database

---

Installation

pip install fullstack-rag-ai

Default Configuration

The library uses a default configuration, but all parameters can be overridden by the user.

DEFAULT_CONFIG = {
    "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
    "llm_model": "llama3",
    "chunk_size": 5000,
    "chunk_overlap": 500,
    "k": 15
}

Core Workflow

Typical usage flow:

• Load documents
• Build or update vector database
• Ask questions against the database

Documents → Chunking → Embeddings → FAISS → Retrieval → LLM → Answer

Loading Documents

Documents can be loaded either from PDF files or from a list of text strings.

Load From PDFs

from fullstack_rag_lib.ingestion import load_documents

docs = load_documents(path="documents/")

Load From Text List

from fullstack_rag_lib.ingestion import load_documents

texts = [
    "Python is a programming language",
    "FAISS is used for vector similarity search"
]

docs = load_documents(texts=texts)

Function

load_documents(path=None, texts=None)

Parameter	Type	Description
path	str	Folder containing PDF files
texts	List[str]	List of raw text strings

Returns: List[Document]

Updating the Vector Database

This function builds or updates the FAISS vector database.

It automatically handles:

• New documents
• Updated documents
• Deleted documents

Only the necessary parts of the database are rebuilt.

from fullstack_rag_lib.vector_db import sync_vector_db

sync_vector_db(
    documents_path="./data",
    index_path="./vector_db",
)

Function

sync_vector_db(documents_path, index_path, embedding_model=embedding_model, chunk_size=chunk_size, chunk_overlap=chunk_overlap)

Parameter	Type	Description
documents_path	str	Directory containing PDF files
index_path	str	Directory where FAISS index is stored
embedding_model	str	HuggingFace embedding model
chunk_size	int	Size of one chunk
chunk_overlap	int	Sentences Overlap

This function maintains the following internal files: index.faiss index.pkl documents.bin metadata.bin qa_cache.bin

Asking Questions

Once the vector database exists, you can query it using an LLM.

The system retrieves the most relevant chunks and sends them to the LLM as context.

from fullstack_rag_lib.qa import ask_question

answer = ask_question(
    question="What is FAISS used for?",
    index_path="./vector_db"
)

print(answer)

Function

ask_question(question, index_path, model=model, embedding_model=embedding_model, k=k, debug=False)

Parameter	Type	Description
question	str	User question
index_path	str	Path to vector database
model	str	Ollama LLM model
embedding_model	str	HuggingFace embedding model
k	int	Number of chunks retrieved
debug	bool	Print retrieved chunks

Returns: str

Deterministic QA Cache

The library includes a smart cache system that prevents unnecessary LLM calls.

Cache keys are generated using: question + retrieved context hash

If:

• the question is the same

• the retrieved documents have not changed

The answer is returned directly from cache.

Cache File

qa_cache.bin

Helper Functions

load_binary

Loads a binary pickle file.

load_binary(path)

Returns empty dictionary if file does not exist.

save_binary

Stores data as a binary pickle file.

save_binary(path, data)

compute_cache_key

Creates a deterministic key for caching LLM responses.

compute_cache_key(question, docs)

Uses:

• Question text
• Retrieved document content

Example Full Pipeline

from fullstack_rag_lib.vector_db import sync_vector_db
from fullstack_rag_lib.qa import ask_question

# Step 1: Build / Update Vector DB
sync_vector_db(
    documents_path="./data",
    index_path="./vector_db",
)

# Step 2: Ask Questions
answer = ask_question(
    question="Summarize the company onboarding process",
    index_path="./vector_db",
)

print(answer.content)

Changing the Embedding Model

Any HuggingFace embedding model supported by sentence-transformers can be used.

Example:

sync_vector_db(
    documents_path="./data",
    index_path="./vector_db",
    embedding_model="sentence-transformers/all-mpnet-base-v2"
)

The library will automatically download the model through HuggingFace if it is not already installed.

Changing the LLM Model

The LLM model can be changed to any model available in Ollama.

Example:

answer = ask_question(
    question="Explain the onboarding process",
    index_path="./vector_db",
    model="llama3:8b",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2"
)

Other examples:

• model="mistral"
• model="phi3"
• model="llama3:70b"

The only requirement is that the model must be available locally in Ollama.

To install a model:

ollama pull llama3
ollama pull llama3:8b
ollama pull llama3:70b
ollama pull phi3
ollama pull mistral

Important: Rebuilding the Vector Database

If the embedding model changes, the vector database must be rebuilt.

This is because embeddings from different models are not compatible.

Example workflow:

sync_vector_db(
    documents_path="./data",
    index_path="./vector_new_db",
    embedding_model="BAAI/bge-base-en"
)

Alternatively, delete the existing index and rebuild.

Dependencies

• langchain
• langchain-community
• langchain-huggingface
• langchain-ollama
• faiss-cpu
• sentence-transformers
• pypdf

These libraries allow dynamic usage of:

• Any HuggingFace embedding model
• Any Ollama LLM model

Therefore, users do not need to install additional dependencies when switching models.

When changing models:

Change	Required Action
LLM model	No rebuild needed
Embedding model	Rebuild vector database
Chunk size	Rebuild vector database

License

MIT License

Author

Fullstack Solutions