We want to use our Arango graph to be incorporated with LLM.
As input, the new model will take a graph/subgraph, process it, save it in the vector db and create a RAG.
CONFIG
For that, we define all the attributes in config file (YAML or python dictionary) with the following variables (preliminary):
model_name_path: "llama-v2-b-chat-hf"
generation_params:
temperature: 0.1
....
graph_db:
username:
password:
url:....
vector_db:
type: chroma_db
username:
password:
url:....
RAG_params: <params related to integrating vector db to llm>
CagRag Model
The CAG RAG model takes the config file and the subgraph (using AQL output, or a postprocessed format) and outputs the RAG model.
Questions to answer
- subgraphs to vectordb: how can we define the format and what do we need from the user?
- Shall we use only ChromaDB or shall we support more DBs?
We want to use our Arango graph to be incorporated with LLM.
As input, the new model will take a graph/subgraph, process it, save it in the vector db and create a RAG.
CONFIG
For that, we define all the attributes in config file (YAML or python dictionary) with the following variables (preliminary):
CagRag Model
The CAG RAG model takes the config file and the subgraph (using AQL output, or a postprocessed format) and outputs the RAG model.
Questions to answer