Detailed explanation of how to configure the Large Language Model (LLM) using environment variables, including setting up Ollama, Neo4j, and other dependencies.
What is LLM Configuration?
The LLM Configuration in the genai-stack project refers to the process of setting up and configuring the large language model (LLM) environment using environment variables. This includes installing and configuring dependencies such as Ollama, Neo4j, and other necessary components.
Why is LLM Configuration important?
Properly configuring the LLM environment is crucial for the successful deployment and operation of the genai-stack project. It ensures that the LLM can access the necessary dependencies and resources to function effectively.
Configuration Options
1. Setting up Ollama
To set up Ollama, you need to provide the following environment variables:
OLLAMA_API_KEY: Your Ollama API key.OLLAMA_API_SECRET: Your Ollama API secret.
Example:
export OLLAMA_API_KEY=your_api_key
export OLLAMA_API_SECRET=your_api_secret
For more information, refer to the Ollama documentation.
2. Setting up Neo4j
To set up Neo4j, you need to provide the following environment variables:
NEO4J_BOLT_URL: The URL of your Neo4j instance.NEO4J_USER: Your Neo4j username.NEO4J_PASSWORD: Your Neo4j password.
Example:
export NEO4J_BOLT_URL=bolt://localhost:7687
export NEO4J_USER=neo4j
export NEO4J_PASSWORD=password
For more information, refer to the Neo4j documentation.
3. Setting up other dependencies
Other dependencies, such as TensorFlow and PyTorch, can be installed using package managers like pip or conda. You can set up the necessary environment variables to configure these dependencies.
Example for TensorFlow:
export TF_FORCE_GPU_ALLOW_GROWTH=true
export CUDA_VISIBLE_DEVICES=0
For more information, refer to the TensorFlow documentation.
References
For more information about the genai-stack project and its dependencies, refer to the following resources:
