mirror of
https://github.com/hwchase17/langchain
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48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import os
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from langchain_community.chat_models import BedrockChat
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import ConfigurableField
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# For a description of each inference parameter, see
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# https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html
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_model_kwargs = {
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"temperature": float(os.getenv("BEDROCK_JCVD_TEMPERATURE", "0.1")),
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"top_p": float(os.getenv("BEDROCK_JCVD_TOP_P", "1")),
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"top_k": int(os.getenv("BEDROCK_JCVD_TOP_K", "250")),
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"max_tokens_to_sample": int(os.getenv("BEDROCK_JCVD_MAX_TOKENS_TO_SAMPLE", "300")),
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}
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# Full list of base model IDs is available at
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# https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html
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_model_alts = {
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"claude_2_1": BedrockChat(
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model_id="anthropic.claude-v2:1", model_kwargs=_model_kwargs
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),
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"claude_1": BedrockChat(model_id="anthropic.claude-v1", model_kwargs=_model_kwargs),
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"claude_instant_1": BedrockChat(
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model_id="anthropic.claude-instant-v1", model_kwargs=_model_kwargs
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),
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}
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# For some tips on how to construct effective prompts for Claude,
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# check out Anthropic's Claude Prompt Engineering deck (Bedrock edition)
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# https://docs.google.com/presentation/d/1tjvAebcEyR8la3EmVwvjC7PHR8gfSrcsGKfTPAaManw
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_prompt = ChatPromptTemplate.from_messages(
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[
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("human", "You are JCVD. {input}"),
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]
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)
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_model = BedrockChat(
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model_id="anthropic.claude-v2", model_kwargs=_model_kwargs
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).configurable_alternatives(
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which=ConfigurableField(
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id="model", name="Model", description="The model that will be used"
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),
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default_key="claude_2",
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**_model_alts,
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)
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chain = _prompt | _model
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