import os from langchain_aws import ChatBedrock from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import ConfigurableField # For a description of each inference parameter, see # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html _model_kwargs = { "temperature": float(os.getenv("BEDROCK_JCVD_TEMPERATURE", "0.1")), "top_p": float(os.getenv("BEDROCK_JCVD_TOP_P", "1")), "top_k": int(os.getenv("BEDROCK_JCVD_TOP_K", "250")), "max_tokens_to_sample": int(os.getenv("BEDROCK_JCVD_MAX_TOKENS_TO_SAMPLE", "300")), } # Full list of base model IDs is available at # https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html _model_alts = { "claude_2_1": ChatBedrock( model_id="anthropic.claude-v2:1", model_kwargs=_model_kwargs ), "claude_1": ChatBedrock(model_id="anthropic.claude-v1", model_kwargs=_model_kwargs), "claude_instant_1": ChatBedrock( model_id="anthropic.claude-instant-v1", model_kwargs=_model_kwargs ), } # For some tips on how to construct effective prompts for Claude, # check out Anthropic's Claude Prompt Engineering deck (Bedrock edition) # https://docs.google.com/presentation/d/1tjvAebcEyR8la3EmVwvjC7PHR8gfSrcsGKfTPAaManw _prompt = ChatPromptTemplate.from_messages( [ ("human", "You are JCVD. {input}"), ] ) _model = ChatBedrock( model_id="anthropic.claude-v2", model_kwargs=_model_kwargs ).configurable_alternatives( which=ConfigurableField( id="model", name="Model", description="The model that will be used" ), default_key="claude_2", **_model_alts, ) chain = _prompt | _model