LangChain은 LM(Large Language)을 편리하게 사용할 수 있도록 도와주는 Framework입니다.
LangChain Basic에서는 LangChain의 각 구성별 Sample 코드를 설명합니다.
Falcon FM으로 만든 SageMaker Endpoint에 LangChain을 적용하는 방법에 대해 설명합니다. SageMaker JumpStart로 Falcon FM 설치하기에서 얻은 SageMaker Endpoint(예: jumpstart-dft-hf-llm-falcon-7b-instruct-bf16)를 이용합니다.
Falcon의 입력과 출력을 참조하여 아래와 같이 ContentHandler의 transform_input, transform_output을 등록합니다.
from langchain import PromptTemplate, SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:
input_str = json.dumps({'inputs': prompt, 'parameters': model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]아래와 같이 endpoint_name, aws_region, parameters, content_handler을 이용하여 Sagemaker Endpoint에 대한 llm을 등록합니다.
endpoint_name = 'jumpstart-dft-hf-llm-falcon-7b-instruct-bf16'
aws_region = boto3.Session().region_name
parameters = {
"max_new_tokens": 300
}
content_handler = ContentHandler()
llm = SagemakerEndpoint(
endpoint_name = endpoint_name,
region_name = aws_region,
model_kwargs = parameters,
content_handler = content_handler
)llm의 동작은 아래와 같이 확인할 수 있습니다.
llm("Tell me a joke")이때의 결과는 아래와 같습니다.
I once told a joke to a friend, but it didn't work. He just looked
Web loader - langchain을 이용하여 web page를 loading 할 수 있습니다.
from langchain.document_loaders import WebBaseLoader
from langchain.indexes import VectorstoreIndexCreator
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
index = VectorstoreIndexCreator().from_loaders([loader])아래와 같이 template를 정의후에 LLMChain을 정의후 run을 수행할 수 있습니다. 세부 내용은 langchain-sagemaker-endpoint-Q&A.ipynb을 참조합니다.
from langchain import PromptTemplate, LLMChain
template = "Tell me a {adjective} joke about {content}."
prompt = PromptTemplate.from_template(template)
llm_chain = LLMChain(prompt=prompt, llm=llm)
outputText = llm_chain.run(adjective="funny", content="chickens")
print(outputText)이때의 결과는 아래와 같습니다.
Why did the chicken cross the playground? To get to the other slide!
langchain.chains.question_answering을 이용하여 Document에 대한 Question/Answering을 수행합니다. 세부 내용은 langchain-sagemaker-endpoint-Q&A.ipynb을 참조합니다.
prompt의 template을 정의합니다.
template = """Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Answer:"""
prompt = PromptTemplate(
template=template, input_variables=["context", "question"]
)langchain.docstore.document을 이용하여 Document를 생성합니다.
from langchain.docstore.document import Document
example_doc_1 = """
Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.
Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.
Therefore, Peter stayed with her at the hospital for 3 days without leaving.
"""
docs = [
Document(
page_content=example_doc_1,
)
]이제 Question/Answering을 수행합니다.
from langchain.chains.question_answering import load_qa_chain
question = "How long was Elizabeth hospitalized?"
chain = load_qa_chain(prompt=prompt, llm=llm)
output = chain({"input_documents": docs, "question": question}, return_only_outputs=True)
print(output)이때의 결과는 아래와 같습니다.
{'output_text': ' 3 days'}
langchain-sagemaker-endpoint-pdf-summary.ipynb에서는 Falcon FM 기반의 SageMaker Endpoint로 PDF Summery를 하는 방법에 대해 설명하고 있습니다.
먼저 PyPDF2를 이용하여 S3에 저장되어 있는 PDF 파일을 읽어서 Text를 추출합니다.
import PyPDF2
from io import BytesIO
sess = sagemaker.Session()
s3_bucket = sess.default_bucket()
s3_prefix = 'docs'
s3_file_name = '2016-3series.pdf' # S3의 파일명
s3r = boto3.resource("s3")
doc = s3r.Object(s3_bucket, s3_prefix+'/'+s3_file_name)
contents = doc.get()['Body'].read()
reader = PyPDF2.PdfReader(BytesIO(contents))
raw_text = []
for page in reader.pages:
raw_text.append(page.extract_text())
contents = '\n'.join(raw_text)
new_contents = str(contents).replace("\n"," ")문서의 크기가 크므로 RecursiveCharacterTextSplitter를 이용해 chunk 단위로 분리하고 Document에 저장합니다. 이후 load_summarize_chain를 이용해 요약합니다.
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=0)
texts = text_splitter.split_text(new_contents)
from langchain.docstore.document import Document
docs = [
Document(
page_content=t
) for t in texts[:3]
]
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import PromptTemplate
prompt_template = """Write a concise summary of the following:
{text}
CONCISE SUMMARY """
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
summary = chain.run(docs)- stuff puts all the chunks into one prompt. Thus, this would hit the maximum limit of tokens.
- map_reduce summarizes each chunk, combines the summary, and summarizes the combined summary. If the combined summary is too large, it would raise error.
- refine summarizes the first chunk, and then summarizes the second chunk with the first summary. The same process repeats until all chunks are summarized.
from langchain import Bedrock
from langchain.embeddings import BedrockEmbeddings
llm = Bedrock()
print(llm("explain GenAI"))AWS Kendra Langchain Extensions
LangChain - Modules - Language models - LLMs - Integration - SageMakerEndpoint