graph-vect-rag 1.0.0

A Python package for hybrid Graph + Vector RAG using completely Open Source tools

Graph_Vect_RAG

graph_vect_rag is a powerful Python package designed to manage and query knowledge bases using a combination of knowledge graph and vector stores. This package supports integration with GROQ language models, HuggingFace embeddings, and Neo4j graph stores to build and query hybrid retrieval-augmented generation (RAG) systems.

Features

• Language Model (LLM) Integration: Supports multiple language models from GROQ for natural language processing.
• Graph and Vector Store Management: Handles knowledge bases as a combination of graph and vector indexes.
• Custom Retriever: Combines graph and vector store retrievers for more effective query results.
• Embeddings with HuggingFace: Uses HuggingFace embeddings for vector indexing.
• Neo4j Graph Store Integration: Manages graph stores using the Neo4j database.
• Flexible Querying: Allows natural language queries to the knowledge base.

Installation

To install Graph_Vect_RAG, use pip:

pip install graph_vect_rag

Basic Setup

1. Initialize the Engine:

from graph_rag_engine import Graph_RAG_Engine

engine = Graph_RAG_Engine()

2. Configure the Language Model (LLM):

You can configure the language model using any model from the supported list provided at the end of the document:

• Directly providing the API key:

  engine.configure_llm(
      model_id="model_name_from_supported_list",
      api_key="your_groq_api_key"
  )
  

• Using an environment variable: Alternatively, you can set the API key in your environment by creating a .env file with the following content:

  GROQ_API_KEY=your_groq_api_key
  

  The package will automatically load the API key from the environment variable GROQ_API_KEY when the api_key argument is not provided.

Replace "model_name_from_supported_list" with any of the supported models listed at the end of the document, such as "llama-3.1-70b-versatile" or "llama-3.1-8b-instant".

3. Configure the Embedding Model:

You can configure the embedding model using any HuggingFace embedding model:

engine.configure_embedding_model(model_id="huggingface_model_name")

Replace "huggingface_model_name" with the name of any HuggingFace model you wish to use for embeddings (e.g., "sentence-transformers/all-MiniLM-L6-v2").

4. Configure the Graph Store:

You can configure the graph store with default values for username and url. The default values are username="neo4j" and url="bolt://localhost:7687":

engine.configure_graph_store(
    password="neo4j_password",
    username="neo4j",  # Default value
    url="bolt://localhost:7687"  # Default value
)

If you need to use different values, simply provide them as arguments to configure_graph_store.

Note: You need to have Neo4j installed and running. Additionally, make sure that APOC procedures are enabled in your Neo4j instance for full functionality with this package.

Create a Knowledge Base

To create a knowledge base from a document:

engine.create_knowledge_base(
    file_path="path/to/your/document.txt",
    knowledge_base_name="my_knowledge_base"
)

Load a Knowledge Base

To load an existing knowledge base:

engine.load_knowledge_base(knowledge_base_name="my_knowledge_base")

Query the Knowledge Base

To query the loaded knowledge base using natural language:

response = engine.query_knowledge_base("What is the capital of France?")
print(response)

Exception Handling

Graph_Vect_RAG provides custom exceptions to handle various error scenarios:

• InvlaidModelIdException: Raised when an invalid model ID is provided.
• StorageContextNotFoundException: Raised when the storage context is not configured.
• LLMNotFoundException: Raised when the LLM is not configured.
• EmbeddingModelNotFoundException: Raised when the embedding model is not configured.
• KnowledgeBaseAlreadyExists: Raised when trying to create a knowledge base with an existing name.
• KnowledgeBaseNotFound: Raised when attempting to load a non-existent knowledge base.
• KnowledgeBaseNotConfigured: Raised when querying without selecting a knowledge base.

Supported GROQ Models

The following models are supported by the package:

• llama-3.1-70b-versatile
• llama-3.1-8b-instant
• llama3-groq-70b-8192-tool-use-preview
• llama3-groq-8b-8192-tool-use-preview
• llama-guard-3-8b
• llama3-70b-8192
• llama3-8b-8192
• mixtral-8x7b-32768
• gemma-7b-it
• gemma2-9b-it