From 5579c4e0cd0038c41509f864d9569f8bb6965619 Mon Sep 17 00:00:00 2001 From: Dayuan Date: Fri, 7 Jun 2024 17:06:28 +0800 Subject: [PATCH] feat: add ai-agent-demo --- .../ai-agent-demo/dev/example_workspace.yaml | 13 +- example/ai-agent-demo/dev/kcl.mod | 4 +- example/ai-agent-demo/dev/main.k | 5 +- example/ai-agent-demo/src/demo.py | 196 ++++++++++++++++++ example/ai-agent-demo/src/nvidia-10q.pdf | Bin 0 -> 896741 bytes 5 files changed, 213 insertions(+), 5 deletions(-) create mode 100644 example/ai-agent-demo/src/demo.py create mode 100644 example/ai-agent-demo/src/nvidia-10q.pdf diff --git a/example/ai-agent-demo/dev/example_workspace.yaml b/example/ai-agent-demo/dev/example_workspace.yaml index b1578d4e..02e9fbc0 100644 --- a/example/ai-agent-demo/dev/example_workspace.yaml +++ b/example/ai-agent-demo/dev/example_workspace.yaml @@ -1,4 +1,10 @@ modules: + service: + path: oci://ghcr.io/kusionstack/service + version: 0.1.0 + configs: + default: + replicas: 1 opensearch: path: oci://ghcr.io/kusionstack/opensearch version: 0.1.0 @@ -17,4 +23,9 @@ modules: identifiers: - "*" action: - - es:* \ No newline at end of file + - es:* +# AWS SecretManager config +# secretStore: +# provider: +# aws: +# region: us-east-1 diff --git a/example/ai-agent-demo/dev/kcl.mod b/example/ai-agent-demo/dev/kcl.mod index 4135ece5..998daf28 100644 --- a/example/ai-agent-demo/dev/kcl.mod +++ b/example/ai-agent-demo/dev/kcl.mod @@ -2,8 +2,8 @@ name = "example" [dependencies] -kam = { git = "https://github.com/KusionStack/kam.git", tag = "0.2.0-beta" } -service = { oci = "oci://ghcr.io/kusionstack/service", tag = "0.1.0-beta" } +kam = { git = "https://github.com/KusionStack/kam.git", tag = "0.2.0" } +service = { oci = "oci://ghcr.io/kusionstack/service", tag = "0.1.0" } opensearch = { oci = "oci://ghcr.io/kusionstack/opensearch", tag = "0.1.0" } [profile] diff --git a/example/ai-agent-demo/dev/main.k b/example/ai-agent-demo/dev/main.k index e543880b..90a849a5 100644 --- a/example/ai-agent-demo/dev/main.k +++ b/example/ai-agent-demo/dev/main.k @@ -12,7 +12,7 @@ agent: ac.AppConfiguration { opensearch: c.Container { image: "kusionstack/ai-agent-demo:v0.1.0" env: { - "OPENAI_API_KEY": "secret://ai-agent/OPENAI_API_KEY" + # "OPENAI_API_KEY": "secret://ai-agent/OPENAI_API_KEY" # "RAG_ENABLED": "true" # "OPEN_SEARCH_AK": "secret://ai-agent/OPEN_SEARCH_AK" # "OPEN_SEARCH_SK": "secret://ai-agent/OPEN_SEARCH_SK" @@ -24,7 +24,8 @@ agent: ac.AppConfiguration { "ai-agent": sec.Secret { type: "external" data: { - "OPENAI_API_KEY": "ref://ai-agent/OPENAI_API_KEY" + # replace all references with your own tokens + # "OPENAI_API_KEY": "ref://ai-agent/OPENAI_API_KEY" # "OPEN_SEARCH_AK": "ref://ai-agent/OPEN_SEARCH_AK" # "OPEN_SEARCH_SK": "ref://ai-agent/OPEN_SEARCH_SK" } diff --git a/example/ai-agent-demo/src/demo.py b/example/ai-agent-demo/src/demo.py new file mode 100644 index 00000000..85f4926d --- /dev/null +++ b/example/ai-agent-demo/src/demo.py @@ -0,0 +1,196 @@ +# Kusion AI agent RAG demo + +# Import Libraries +# ---------------- +import os + +import boto3 +from flask import Flask, request, jsonify +from langchain.chains import ConversationalRetrievalChain +from langchain.chains.conversation.memory import ConversationBufferWindowMemory +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.chat_models import ChatOpenAI +from langchain_community.document_loaders import PyPDFLoader +from langchain_community.vectorstores import OpenSearchVectorSearch +from langchain_core.output_parsers import StrOutputParser +from opensearchpy import OpenSearch, RequestsHttpConnection +from requests_aws4auth import AWS4Auth + +# Constants and API Keys +# ---------------------- +OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') +RAG_ENABLED = os.getenv('RAG_ENABLED') +OPEN_SEARCH_ENDPOINT = os.getenv('OPEN_SEARCH_ENDPOINT') +OPEN_SEARCH_AK = os.getenv('OPEN_SEARCH_AK') +OPEN_SEARCH_SK = os.getenv('OPEN_SEARCH_SK') +OPEN_SEARCH_REGION = os.getenv('OPEN_SEARCH_REGION') +NVIDIA_PDF_PATH = "./nvidia-10q.pdf" +VECTOR_DB_DIRECTORY = "/tmp/vectordb" +GPT_MODEL_NAME = 'gpt-3.5-turbo' +CHUNK_SIZE = 700 +CHUNK_OVERLAP = 50 + + +# Function Definitions +# -------------------- +def load_and_split_document(pdf_path): + """Loads and splits the document into pages.""" + loader = PyPDFLoader(pdf_path) + return loader.load_and_split() + + +def split_text_into_chunks(pages, chunk_size, chunk_overlap): + """Splits text into smaller chunks for processing.""" + text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) + return text_splitter.split_documents(pages) + + +def create_embeddings(api_key): + """Creates embeddings from text.""" + return OpenAIEmbeddings(openai_api_key=api_key) + + +def setup_awsauth(): + service = "es" # must set the service as 'es' + region = OPEN_SEARCH_REGION + ak = OPEN_SEARCH_AK + sk = OPEN_SEARCH_SK + credentials = boto3.Session( + aws_access_key_id=ak, aws_secret_access_key=sk + ).get_credentials() + awsauth = AWS4Auth(ak, sk, region, service, session_token=credentials.token) + return awsauth + + +def setup_client(awsauth, host_url): + client = OpenSearch( + hosts=[{'host': host_url, 'port': 443}], + http_auth=awsauth, + use_ssl=True, + verify_certs=True, + connection_class=RequestsHttpConnection, + timeout=300 + ) + return client + + +def check_index_exists(client, index_name): + if client.indices.exists(index=index_name): + print("index exists") + client.indices.delete(index=index_name) + return True + else: + print("index does not exist") + return False + + +def setup_vector_database(awsauth, documents, embeddings, host_url): + """Sets up a vector database for storing embeddings.""" + service = "es" # must set the service as 'es' + region = OPEN_SEARCH_REGION + ak = OPEN_SEARCH_AK + sk = OPEN_SEARCH_SK + credentials = boto3.Session( + aws_access_key_id=ak, aws_secret_access_key=sk + ).get_credentials() + awsauth = AWS4Auth(ak, sk, region, service, session_token=credentials.token) + return OpenSearchVectorSearch.from_documents( + documents, + embeddings, + opensearch_url=host_url, + http_auth=awsauth, + timeout=300, + use_ssl=True, + verify_certs=True, + connection_class=RequestsHttpConnection, + index_name="test-index", + ) + + +def initialize_chat_model(api_key, model_name): + """Initializes the chat model with specified AI model.""" + return ChatOpenAI(openai_api_key=api_key, model_name=model_name, temperature=0.0) + + +def create_retrieval_qa_chain(chat_model, vector_database): + """Creates a retrieval QA chain combining model and database.""" + memory = ConversationBufferWindowMemory(memory_key='chat_history', k=5, return_messages=True) + return ConversationalRetrievalChain.from_llm( + chat_model, + retriever=vector_database.as_retriever(), + verbose=True, + # return_generated_question=True, + # return_source_documents=True, + memory=memory) + + +def create_regular_qa_chain(chat_model): + """Creates a regular QA chain combining model and database.""" + output_parser = StrOutputParser() + regular_chain = chat_model | output_parser + return regular_chain + + +def ask_question_and_get_answer(qa_chain, question): + """Asks a question and retrieves the answer.""" + return qa_chain({"question": question})['answer'] + + +# Main Execution Flow +# ------------------- + +app = Flask(__name__) + + +@app.route('/ask', methods=['POST']) +def ask(): + try: + question = request.json.get('question') + if not question: + return jsonify({"error": "No question provided"}), 400 + if RAG_ENABLED == "true": + response = ask_question_and_get_answer(qa_chain, question) + else: + response = regular_chain.invoke(question) + return jsonify({"answer": response}), 200 + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +if __name__ == '__main__': + """Main function to execute the RAG workflow.""" + chat_model = initialize_chat_model(OPENAI_API_KEY, GPT_MODEL_NAME) + regular_chain = create_regular_qa_chain(chat_model) + + if RAG_ENABLED == "true": + print("RAG is enabled") + pages = load_and_split_document(NVIDIA_PDF_PATH) + documents = split_text_into_chunks(pages, CHUNK_SIZE, CHUNK_OVERLAP) + embeddings = create_embeddings(OPENAI_API_KEY) + host = OPEN_SEARCH_ENDPOINT + host_url = "https://" + host + indexName = "test-index" + + awsauth = setup_awsauth() + client = setup_client(awsauth, host) + index_exists = check_index_exists(client, indexName) + if not index_exists: + vector_database = setup_vector_database(awsauth, documents, embeddings, host_url) + print("index created") + else: + print("index exists, returning vector database object") + vector_database = OpenSearchVectorSearch( + host_url, + indexName, + embeddings, + http_auth=awsauth, + timeout=300, + use_ssl=True, + verify_certs=True, + connection_class=RequestsHttpConnection, + ) + qa_chain = create_retrieval_qa_chain(chat_model, 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