langchain-sambanova

import os from langchain_sambanova import ChatSambaNova from langchain_core.messages import HumanMessage # Ensure SAMBANOVA_API_KEY is set as an environment variable # For SambaStack, SAMBANOVA_API_BASE might also be required. os.environ['SAMBANOVA_API_KEY'] = os.environ.get('SAMBANOVA_API_KEY', 'your_sambanova_api_key_here') # Initialize the SambaNova Chat model llm = ChatSambaNova( model="Meta-Llama-3.3-70B-Instruct", # Replace with your desired model max_tokens=1024, temperature=0.7, top_p=0.01, ) # Invoke the model with a message messages = [ ("system", "You are a helpful assistant that translates English to French."), ("human", "I love programming."), ] response = llm.invoke(messages) print(response.content) # Example for embeddings from langchain_sambanova import SambaNovaEmbeddings embeddings = SambaNovaEmbeddings(model="E5-Mistral-7B-Instruct") # Replace with your desired embedding model query_embedding = embeddings.embed_query("What is the meaning of life?") print(f"Embedding snippet: {str(query_embedding)[:50]}...")