Unduh amazon personalize langchain extensions - unduh kode sumber amazon personalize langchain extensions

Ekstensi Langchain Personalisasi Amazon

Repo ini menyediakan serangkaian kelas utilitas untuk bekerja dengan Langchain. Saat ini ia memiliki kelas utilitas AmazonPersonalize untuk bekerja dengan kampanye/pemberi rekomendasi Amazon Personalize dan pembuatan rantai kustom AmazonPersonalizeChain untuk mengambil rekomendasi dari Amazon Personalize dan menjalankan perintah default (yang dapat diganti oleh pengguna).

Menginstal

Kloning repositori

git clone https://github.com/aws-samples/amazon-personalize-langchain-extensions.git

Pindah ke direktori repo

 cd amazon-personalize-langchain-extensions

Instal kelasnya

pip install .

Penggunaan

[Kasus Penggunaan-1] Siapkan Amazon Personalize Client dan aktifkan Personalize Chain untuk meringkas hasil

 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" )

[Use-case-2] Siapkan Amazon Personalize Client dan aktifkan Personalize Chain untuk meringkas hasil

 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 )

[Use-Case-3] Panggil Amazon Personalize Chain menggunakan perintah Anda sendiri

 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' })

[Kasus Penggunaan-4] Panggil Amazon Personalize dalam Rantai Berurutan

 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' })

[Use-case-5] Panggil Amazon Personalize menggunakan metadata dari respons

 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 )