From 8fa2863cc898647ea2c5fce49c5a49574c4fa4ae Mon Sep 17 00:00:00 2001 From: EC2 Default User Date: Fri, 12 Jan 2024 17:19:52 +0000 Subject: [PATCH] AWS Bedrock Tutorial and example scripts --- docs/images/bedrock_agents_1.png | Bin 0 -> 230664 bytes docs/images/bedrock_agents_2.png | Bin 0 -> 228219 bytes docs/images/bedrock_agents_3.png | Bin 0 -> 260164 bytes docs/images/bedrock_agents_4.png | Bin 0 -> 268788 bytes docs/images/bedrock_agents_5.png | Bin 0 -> 255442 bytes docs/images/bedrock_agents_6.png | Bin 0 -> 258052 bytes docs/images/bedrock_agents_7.png | Bin 0 -> 384271 bytes docs/images/bedrock_chat_playground_1.png | Bin 0 -> 208741 bytes docs/images/bedrock_chat_playground_2.png | Bin 0 -> 141302 bytes docs/images/bedrock_chat_playground_3.png | Bin 0 -> 444773 bytes docs/images/bedrock_chat_playground_4.png | Bin 0 -> 193564 bytes docs/images/bedrock_knowledgebase_1.png | Bin 0 -> 359690 bytes docs/images/bedrock_knowledgebase_10.png | Bin 0 -> 487952 bytes 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zimoq!L?slpSxV$4?b**ehB-8}h)Ukh1Wscz4+4j9A485M(NpB6f0tZ-X~8O>WxT+byK`nuX@Qm_@9`0^Omr;emWV z7CP_LM7%3H?F*wqSm)@128WfTT}RMXeTkXz@KnDC6E+Zfxf9gN6|4@IpA7Tv;1fwQ zjFHLH8L&0HwHr8=)Z&;4JT$Islj$SMCn_YZM(5s(vFqMNzD%itYanfu2WN_4^BR?; zyrQ0`?zLXEp-#|1c)$ZuQbm>iueC?4fB5#Hmr~}H?&6jz@tKD&1o&2%pCH&Z6Ar(s z%g~og_FUbzO>BY`w!5|fC bytes: + input_str = json.dumps({"inputs": + [[ + #{"role": "system", "content": ""}, + {"role": "user", "content": prompt}, + ]], + **model_kwargs + }) + #print(input_str) + + 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]['generation']['content'] + + content_handler = ContentHandler() + + llm=SagemakerEndpoint( + endpoint_name=endpoint_name, + region_name=region, + model_kwargs={"parameters": {"max_new_tokens": 1000, "top_p": 0.9,"temperature":0.6}}, + endpoint_kwargs={"CustomAttributes":"accept_eula=true"}, + content_handler=content_handler, + ) + + retriever = AmazonKendraRetriever(index_id=kendra_index_id,region_name=region) + + prompt_template = """ + Ignore everything before. + + Instruction: + I want you to act as a research paper summarizer. I will provide you with a research paper on a specific topic in English, and you will create a summary. The summary should be concise and should accurately and objectively communicate the takeaway of the paper. You should not include any personal opinions or interpretations in your summary, but rather focus on objectively presenting the information from the paper. Your summary should be written in your own words and ensure that your summary is clear, concise, and accurately reflects the content of the original paper. + + First, provide a concise summary. Then provides the sources. + + {question} Answer "don't know" if not present in the document. + {context} + Solution:""" + PROMPT = PromptTemplate( + template=prompt_template, input_variables=["context", "question"], + ) + + condense_qa_template = """ + Chat History: + {chat_history} + Here is a new question for you: {question} + Standalone question:""" + standalone_question_prompt = PromptTemplate.from_template(condense_qa_template) + + qa = ConversationalRetrievalChain.from_llm( + llm=llm, + retriever=retriever, + condense_question_prompt=standalone_question_prompt, + return_source_documents=True, + combine_docs_chain_kwargs={"prompt":PROMPT}, + ) + return qa + +def run_chain(chain, prompt: str, history=[]): + print(prompt) + return chain({"question": prompt, "chat_history": history}) + +if __name__ == "__main__": + chat_history = [] + qa = build_chain() + print(bcolors.OKBLUE + "Hello! How can I help you?" + bcolors.ENDC) + print(bcolors.OKCYAN + "Ask a question, start a New search: or CTRL-D to exit." + bcolors.ENDC) + print(">", end=" ", flush=True) + for query in sys.stdin: + if (query.strip().lower().startswith("new search:")): + query = query.strip().lower().replace("new search:","") + chat_history = [] + elif (len(chat_history) == MAX_HISTORY_LENGTH): + chat_history.pop(0) + result = run_chain(qa, query, chat_history) + chat_history.append((query, result["answer"])) + print(bcolors.OKGREEN + result['answer'] + bcolors.ENDC) + if 'source_documents' in result: + print(bcolors.OKGREEN + 'Sources:') + for d in result['source_documents']: + print(d.metadata['source']) + print(bcolors.ENDC) + print(bcolors.OKCYAN + "Ask a question, start a New search: or CTRL-D to exit." + bcolors.ENDC) + print(">", end=" ", flush=True) + print(bcolors.OKBLUE + "Bye" + bcolors.ENDC) + + + diff --git a/tutorials/notebooks/GenAI/example_scripts/langchain_chat_llama_2_zeroshot.py b/tutorials/notebooks/GenAI/example_scripts/langchain_chat_llama_2_zeroshot.py new file mode 100644 index 0000000..35725d6 --- /dev/null +++ b/tutorials/notebooks/GenAI/example_scripts/langchain_chat_llama_2_zeroshot.py @@ -0,0 +1,117 @@ +from langchain.retrievers import PubMedRetriever +from langchain.chains import ConversationalRetrievalChain +from langchain.prompts import PromptTemplate +#from langchain import SagemakerEndpoint +from langchain.llms.sagemaker_endpoint import LLMContentHandler +import sys +import json +import os +from langchain.llms import SagemakerEndpoint + + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKCYAN = '\033[96m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + +MAX_HISTORY_LENGTH = 1 + +def build_chain(): + region = os.environ["AWS_REGION"] + #kendra_index_id = os.environ["KENDRA_INDEX_ID"] + endpoint_name = os.environ["LLAMA_2_ENDPOINT"] + + 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": + [[ + #{"role": "system", "content": ""}, + {"role": "user", "content": prompt}, + ]], + **model_kwargs + }) + #print(input_str) + + 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]['generation']['content'] + + content_handler = ContentHandler() + + llm=SagemakerEndpoint( + endpoint_name=endpoint_name, + region_name=region, + model_kwargs={"parameters": {"max_new_tokens": 1000, "top_p": 0.9,"temperature":0.6}}, + endpoint_kwargs={"CustomAttributes":"accept_eula=true"}, + content_handler=content_handler, + ) + + #retriever = AmazonKendraRetriever(index_id=kendra_index_id,region_name=region) + retriever= PubMedRetriever() + + prompt_template = """ + Ignore everything before. + + Instruction: + I want you to act as a research paper summarizer. I will provide you with a research paper on a specific topic in English, and you will create a summary. The summary should be concise and should accurately and objectively communicate the takeaway of the paper. You should not include any personal opinions or interpretations in your summary, but rather focus on objectively presenting the information from the paper. Your summary should be written in your own words and ensure that your summary is clear, concise, and accurately reflects the content of the original paper. + + First, provide a concise summary then provide the sources. + + {question} Answer "don't know" if not present in the document. + {context} + Solution:""" + PROMPT = PromptTemplate( + template=prompt_template, input_variables=["context", "question"], + ) + + condense_qa_template = """ + Chat History: + {chat_history} + Here is a new question for you: {question} + Standalone question:""" + standalone_question_prompt = PromptTemplate.from_template(condense_qa_template) + + qa = ConversationalRetrievalChain.from_llm( + llm=llm, + retriever=retriever, + condense_question_prompt=standalone_question_prompt, + return_source_documents=True, + combine_docs_chain_kwargs={"prompt":PROMPT}, + ) + return qa + +def run_chain(chain, prompt: str, history=[]): + print(prompt) + return chain({"question": prompt, "chat_history": history}) + +if __name__ == "__main__": + chat_history = [] + qa = build_chain() + print(bcolors.OKBLUE + "Hello! How can I help you?" + bcolors.ENDC) + print(bcolors.OKCYAN + "Ask a question, start a New search: or CTRL-D to exit." + bcolors.ENDC) + print(">", end=" ", flush=True) + for query in sys.stdin: + if (query.strip().lower().startswith("new search:")): + query = query.strip().lower().replace("new search:","") + chat_history = [] + elif (len(chat_history) == MAX_HISTORY_LENGTH): + chat_history.pop(0) + result = run_chain(qa, query, chat_history) + chat_history.append((query, result["answer"])) + print(bcolors.OKGREEN + result['answer'] + bcolors.ENDC) + print(bcolors.ENDC) + print(bcolors.OKCYAN + "Ask a question, start a New search: or CTRL-D to exit." + bcolors.ENDC) + print(">", end=" ", flush=True) + print(bcolors.OKBLUE + "Bye" + bcolors.ENDC)

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