databricks-langchain

import os from langchain_community.chat_models import ChatDatabricks from langchain_core.messages import HumanMessage # Ensure these environment variables are set for authentication: # os.environ["DATABRICKS_HOST"] = "https://<your-workspace-url>" # os.environ["DATABRICKS_TOKEN"] = "dapixxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Retrieve credentials from environment variables host = os.environ.get("DATABRICKS_HOST", "") token = os.environ.get("DATABRICKS_TOKEN", "") if not host or not token: print("Error: DATABRICKS_HOST and DATABRICKS_TOKEN environment variables must be set.") # In a production environment, you would likely raise an exception or handle gracefully. exit(1) # Initialize the Databricks chat model llm = ChatDatabricks( databricks_host=host, databricks_token=token, temperature=0.1, model="databricks-mixtral-8x7b-instruct" # Use an appropriate model available in your workspace ) # Invoke the model with a string prompt response = llm.invoke("Explain the concept of RAG in LLMs in one sentence.") print(f"\nString prompt response: {response.content}") # Example with a list of messages (common for chat models) messages = [ HumanMessage(content="What is the capital of France?"), ] response_chat = llm.invoke(messages) print(f"\nChat response: {response_chat.content}")