amazon personalize langchain extensions
1.0.0
Dieses Repo bietet eine Reihe von Hilfsklassen für die Arbeit mit Langchain. Es verfügt derzeit über eine Dienstprogrammklasse AmazonPersonalize für die Arbeit mit einer Amazon Personalize-Kampagne/-Empfehlung und einen benutzerdefinierten Kettenaufbau AmazonPersonalizeChain um Empfehlungen von Amazon Personalize abzurufen und eine Standardaufforderung auszuführen (die vom Benutzer überschrieben werden kann).
Klonen Sie das Repository
git clone https://github.com/aws-samples/amazon-personalize-langchain-extensions.gitWechseln Sie in das Repo-Verzeichnis
cd amazon-personalize-langchain-extensionsInstallieren Sie die Klassen
pip install . from aws_langchain import AmazonPersonalize
recommender_arn = "<insert_arn>"
client = AmazonPersonalize ( credentials_profile_name = "default" , region_name = "us-west-2" , recommender_arn = recommender_arn )
client . get_recommendations ( user_id = "1" ) from aws_langchain import AmazonPersonalize
from aws_langchain import AmazonPersonalizeChain
from langchain . llms . bedrock import Bedrock
recommender_arn = "<insert_arn>"
bedrock_llm = Bedrock ( model_id = "anthropic.claude-v2" , region_name = "us-west-2" )
client = AmazonPersonalize ( credentials_profile_name = "default" , region_name = "us-west-2" , recommender_arn = recommender_arn )
# Create personalize chain
# Use return_direct=True if you do not want summary
chain = AmazonPersonalizeChain . from_llm (
llm = bedrock_llm ,
client = client ,
return_direct = False
)
response = chain ({ 'user_id' : '1' })
print ( response ) from langchain . prompts . prompt import PromptTemplate
from aws_langchain import AmazonPersonalize
from aws_langchain import AmazonPersonalizeChain
from langchain . llms . bedrock import Bedrock
RANDOM_PROMPT_QUERY = """
You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week,
given the movie and user information below. Your email will leverage the power of storytelling and persuasive language.
The movies to recommend and their information is contained in the <movie> tag.
All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them.
Put the email between <email> tags.
<movie>
{result}
</movie>
Assistant:
"""
RANDOM_PROMPT = PromptTemplate ( input_variables = [ "result" ], template = RANDOM_PROMPT_QUERY )
recommender_arn = "<insert_arn>"
bedrock_llm = Bedrock ( model_id = "anthropic.claude-v2" , region_name = "us-west-2" )
client = AmazonPersonalize ( credentials_profile_name = "default" , region_name = "us-west-2" , recommender_arn = recommender_arn )
chain = AmazonPersonalizeChain . from_llm ( llm = bedrock_llm , client = client , return_direct = False , prompt_template = RANDOM_PROMPT )
chain . run ({ 'user_id' : '1' , 'item_id' : '234' }) from langchain . chains import SequentialChain
from langchain . chains import LLMChain
from aws_langchain import AmazonPersonalize
from aws_langchain import AmazonPersonalizeChain
from langchain . llms . bedrock import Bedrock
from langchain . prompts . prompt import PromptTemplate
RANDOM_PROMPT_QUERY_2 = """
You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week,
given the movie and user information below. Your email will leverage the power of storytelling and persuasive language.
You want the email to impress the user, so make it appealing to them.
The movies to recommend and their information is contained in the <movie> tag.
All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them.
Put the email between <email> tags.
<movie>
{result}
</movie>
Assistant:
"""
recommender_arn = "<insert_arn>"
bedrock_llm = Bedrock ( model_id = "anthropic.claude-v2" , region_name = "us-west-2" )
client = AmazonPersonalize ( credentials_profile_name = "default" , region_name = "us-west-2" , recommender_arn = recommender_arn )
RANDOM_PROMPT_2 = PromptTemplate ( input_variables = [ "result" ], template = RANDOM_PROMPT_QUERY_2 )
personalize_chain_instance = AmazonPersonalizeChain . from_llm ( llm = bedrock_llm , client = client , return_direct = True )
random_chain_instance = LLMChain ( llm = bedrock_llm , prompt = RANDOM_PROMPT_2 )
overall_chain = SequentialChain ( chains = [ personalize_chain_instance , random_chain_instance ], input_variables = [ "user_id" ], verbose = True )
overall_chain . run ({ 'user_id' : '1' , 'item_id' : '234' }) from aws_langchain import AmazonPersonalize
from aws_langchain import AmazonPersonalizeChain
from langchain . llms . bedrock import Bedrock
recommender_arn = "<insert_arn>"
metadata_column_list = [ "METADATA_COL1" ]
metadataMap = { "ITEMS" : metadata_column_list }
bedrock_llm = Bedrock ( model_id = "anthropic.claude-v2" , region_name = "us-west-2" )
client = AmazonPersonalize ( credentials_profile_name = "default" , region_name = "us-west-2" , recommender_arn = recommender_arn )
# Create personalize chain
# Use return_direct=True if you do not want summary
chain = AmazonPersonalizeChain . from_llm (
llm = bedrock_llm ,
client = client ,
return_direct = False
)
response = chain ({ 'user_id' : '1' , 'metadata_columns' : metadataMap })
print ( response )pip uninstall aws-langchainErstellen Sie Ihren GitHub-Zweig und stellen Sie eine Pull-Anfrage. Weitere Informationen finden Sie unter BEITRAGEN.
Diese Bibliothek ist unter der MIT-0-Lizenz lizenziert. Siehe die LICENSE-Datei.
