From 4dd79ba807b3789c5a9bf7e7443b325e079a6fe7 Mon Sep 17 00:00:00 2001 From: Rene Honig <5851246+renehonig@users.noreply.github.com> Date: Tue, 19 Sep 2023 15:25:55 +0200 Subject: [PATCH] Full refactoring of the engine (#19) * fully refactored code * updated outstanding tasks in README * fix ingestion and make file paths fully configurable * update docker composew file to add rabbitmq server and persist vector database * Update .openai-template.env * add llm usage and cost info * updates after debugging session * make 'docker on Windows proof' and update docker compose file * refactor query chain so a chatLLM is used for the query and 'normal' llm for condensing the question * improve language capabilities * updated generate website script to run on windows - no dash in filename, absolute path for hugo; additional logging * fix additional path issue * moved from shell script to python for the generation * fixed additional path issues; reverted order in app.py * re-enable website generation * misc tidy up * reverted to loading hugo from the path + setting path properly * Refactoring * Made naming more consistent * revert some temperature changes some llm's need zero temperature. * Update ai_utils.py fix typo in prompt * finetuning and dependency updates --------- Co-authored-by: Valentin Yanakiev Co-authored-by: Neil Smyth --- .azure-template.env | 5 +- .gitignore | 3 +- .openai-template.env | 12 +- Dockerfile | 14 +- README.md | 66 ++- ai_utils.py | 121 +++- app.py | 85 ++- config.py | 19 + def_ingest.py | 168 ++++-- docker-compose.yaml | 34 ++ payloads.json | 20 + poetry.lock | 1310 ++++++++++++++++-------------------------- pyproject.toml | 12 +- 13 files changed, 932 insertions(+), 937 deletions(-) create mode 100644 config.py create mode 100644 docker-compose.yaml create mode 100644 payloads.json diff --git a/.azure-template.env b/.azure-template.env index 90d0dd5..6f6e65c 100644 --- a/.azure-template.env +++ b/.azure-template.env @@ -8,4 +8,7 @@ RABBITMQ_PASSWORD=super-secure-pass AI_MODEL_TEMPERATURE=0.3 AI_MODEL_NAME=gpt-35-turbo AI_DEPLOYMENT_NAME=deploy-gpt-35-turbo -AI_EMBEDDINGS_DEPLOYMENT_NAME=embedding \ No newline at end of file +AI_EMBEDDINGS_DEPLOYMENT_NAME=embedding +AI_SOURCE_WEBSITE=https://www.alkemio.org +AI_LOCAL_PATH=~/alkemio/data +AI_WEBSITE_REPO=https://github.com/alkem-io/website.git \ No newline at end of file diff --git a/.gitignore b/.gitignore index ecb0a27..de198b6 100644 --- a/.gitignore +++ b/.gitignore @@ -3,4 +3,5 @@ openai.env azure.env /__pycache__/* -/local_index/* +/vectordb/* +local.env diff --git a/.openai-template.env b/.openai-template.env index 7484358..3f34a36 100644 --- a/.openai-template.env +++ b/.openai-template.env @@ -1 +1,11 @@ -OPENAI_API_KEY=api-key \ No newline at end of file +OPENAI_API_KEY=api-key +AI_SOURCE_WEBSITE=https://www.alkemio.org +AI_LOCAL_PATH=~/alkemio/data +RABBITMQ_HOST=localhost +RABBITMQ_USER=admin +RABBITMQ_PASSWORD=super-secure-pass +AI_MODEL_TEMPERATURE=0.3 +AI_MODEL_NAME=gpt-35-turbo +AI_SOURCE_WEBSITE=https://www.alkemio.org +AI_LOCAL_PATH=~/alkemio/data +AI_WEBSITE_REPO=https://github.com/alkem-io/website.git diff --git a/Dockerfile b/Dockerfile index 107b74f..91c7e58 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,15 +4,23 @@ FROM python:3-slim-bookworm # Set the working directory in the container to /app WORKDIR /app +ARG GO_VERSION=1.21.1 +ARG HUGO_VERSIOM=0.118.2 +ARG ARCHITECTURE=amd64 + +# install git, go and hugo +RUN apt update && apt upgrade -y && apt install git wget -y +RUN wget https://go.dev/dl/go${GO_VERSION}.linux-${ARCHITECTURE}.tar.gz && tar -C /usr/local -xzf go${GO_VERSION}.linux-${ARCHITECTURE}.tar.gz +RUN export PATH=$PATH:/usr/local/go/bin:/usr/local && go version +RUN wget https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSIOM}/hugo_extended_${HUGO_VERSIOM}_linux-${ARCHITECTURE}.tar.gz && tar -C /usr/local -xzf hugo_extended_${HUGO_VERSIOM}_linux-${ARCHITECTURE}.tar.gz && ls -al /usr/local +RUN /usr/local/hugo version + # Install Poetry RUN pip install poetry # Copy the current directory contents into the container at /app COPY . /app -# install chromium-driver -RUN apt update && apt install chromium-driver -y - # Use Poetry to install dependencies RUN poetry config virtualenvs.create true && poetry install --no-interaction --no-ansi diff --git a/README.md b/README.md index 5c5bd3c..fb53150 100644 --- a/README.md +++ b/README.md @@ -24,23 +24,50 @@ Training a LLM is prohibitatively expensive for most organisations, but for most The projects has been implemented as a container based micro-service with a RabbitMQ RPC. There is one RabbitMQ queue: - `alkemio-chat-guidance` - queue for submitting requests to the microservice -The request payload consists of json with the following structure `{"operation" : "*operation type*", "param": "*addition request data*"} +The request payload consists of json with the following structure (example for a query): +``` +{ + "data": { + "userId": "userID", + "question": "What are the key Alkemio concepts?", + "language": "UK" + }, + "pattern": { + "cmd": "query" + } +} +``` The operation types are: -- `ingest`: data collection from the Alkemio foundation website and embedding using the [OpenAI Ada text model](https://openai.com/blog/new-and-improved-embedding-model), no *addition request data*. -- `reset`: reset the chat history for the ongoing chat, no *addition request data*. -- `query`: post the next question in a chat sequence, with user question as *addition request data* +- `ingest`: data collection from the Alkemio foundation website (through the Github source) and embedding using the [OpenAI Ada text model](https://openai.com/blog/new-and-improved-embedding-model), no *addition request data*. +- `reset`: reset the chat history for the ongoing chat, needs userId. +- `query`: post the next question in a chat sequence, see exmaple The response is published in an auto-generated, exclusive, unnamed queue. +There is a draft implementation for the interaction language of the model (this needs significant improvement). If no language code is specified, English will be assumed. Choices are: + 'EN': 'English', + 'US': 'English', + 'UK': 'English', + 'FR': 'French', + 'DE': 'German', + 'ES': 'Spanish', + 'NL': 'Dutch', + 'BG': 'Bulgarian', + 'UA': "Ukranian" + *note: there is an earlier (outdated) RESTful implementation available at https://github.com/alkem-io/guidance-engine/tree/http-api ### Docker -The following command can be used to build the container from the Docker CLI: -`docker build -t guidance-engine . ` +The following command can be used to build the container from the Docker CLI (default architecture is amd64, so `--build-arg ARCHITECTURE=arm64` for amd64 builds): +`docker build --build-arg ARCHITECTURE=arm64 --no-cache -t alkemio/guidance-engine:v0.2.0 .` +`docker build--no-cache -t alkemio/guidance-engine:v0.2.0 .` +The Dockerfile has some self-explanatory configuration arguments. The following command can be used to start the container from the Docker CLI: -`docker run --name guidance-engine -v /dev/shm:/dev/shm -v .env guidance-engine` +`docker run --name guidance-engine -v /dev/shm:/dev/shm --env-file .env guidance-engine` +where `.env` based on `.azure-template.env` +Alternatively use `docker-compose up -d`. with: - `OPENAI_API_KEY`: a valid OpenAI API key @@ -54,6 +81,9 @@ with: - `AI_MODEL_NAME`: the model name in Azure - `AI_DEPLOYMENT_NAME`: the AI gpt model deployment name in Azure - `AI_EMBEDDINGS_DEPLOYMENT_NAME`: the AI embeddings model deployment name in Azure +- `AI_SOURCE_WEBSITE`: the URL of the website that contains the source data (for references only) +- `AI_LOCAL_PATH`: local file path for storing data +- `AI_WEBSITE_REPO`: url of the Git repository containing the website source data, based on Hugo You can find sample values in `.azure-template.env` and `.openai-template.env`. Configure them and create `.env` file with the updated settings. @@ -62,9 +92,19 @@ The project requires Python & Poetry installed. The minimum version dependencies After installing Python & Poetry, you simply need to run `poetry run python app.py` ### Linux -The project required Python 3.10 as a minimum and the chromium driver is required for scraing of the Alkemio website: -install Chromium-driver: `sudo apt-get install chromium-driver` - -Note: make sure the version of the driver of chromium-driver is compatible with your chrome version. Otherwise, in order for this to work, you will need to: -- re-install chrome / chromium driver to match the versions -- uninstall chrome \ No newline at end of file +The project requires Python 3.11 as a minimum and needs Go and Hugo installed for creating a local version of the website. See Go and Hugo documentation for installation instructions (only when running outside container) + + +## Outstanding +The following tasks are still outstanding: +- clean up code and add more comments. +- improve interaction language. +- assess overall quality and performance of the model and make improvements as and when required. +- assess the need to summarize the chat history to avoid exceeding the prompt token limit. +- update the yaml manifest. +- add error handling. +- perform extensive testing, in particular in multi-user scenarios. +- look at improvements of the ingestion. As a minimum the service engine should not consume queries whilst the ingestion is ongoing, as thatwill lead to errors. +- look at the use of `temperature` for the `QARetrievalChain`. It is not so obvious how this is handled. +- look at the possibility to implement reinforcement learning. +- return the actual LLM costs and token usage for queries. diff --git a/ai_utils.py b/ai_utils.py index 54c47b6..7d93385 100644 --- a/ai_utils.py +++ b/ai_utils.py @@ -1,39 +1,118 @@ -from langchain.prompts.prompt import PromptTemplate from langchain.embeddings import OpenAIEmbeddings -from langchain.chains import ConversationalRetrievalChain from langchain.vectorstores import FAISS from langchain.llms import AzureOpenAI +from langchain.prompts import PromptTemplate +from langchain.chat_models import AzureChatOpenAI +from langchain.chains import ConversationalRetrievalChain, LLMChain +from langchain.chains.question_answering import load_qa_chain +from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT import os +# define internal configuration parameters +# token limit for retrieval chain +max_token_limit = 2000 +# verbose output for LLMs +verbose_models = True +# doews chain return the source documents? +return_source_document = True -# Set Context for response -TEMPLATE = """ -- Act as a product and innovation expert. -- Your task is to answer user questions. -- Return your response in markdown, and highlight important elements. -- If the answer cannot be found within the context, write 'I could not find an answer to your question'. -- Provide concise replies that are polite and professional. -- Use the following context to answer the query. +# Define a dictionary containing country codes as keys and related languages as values +language_mapping = { + 'EN': 'English', + 'US': 'English', + 'UK': 'English', + 'FR': 'French', + 'DE': 'German', + 'ES': 'Spanish', + 'NL': 'Dutch', + 'BG': 'Bulgarian', + 'UA': "Ukranian" +} + +# function to retrieve language from country +def get_language_by_code(language_code): + """Returns the language associated with the given code. If no match is found, it returns 'English'.""" + return language_mapping.get(language_code, 'English') + + +chat_template = """ +You are a conversational agent. Use the following step-by-step instructions to respond to user inputs. +1 - The text provided in the context delimited by triple pluses may contain questions. Remove those questions from the context. +2 - Provide a single paragragh answer that is polite and professional taking into account the context delimited by triple pluses. If the answer cannot be found within the context, write 'I could not find an answer to your question'. ++++ Context: {context} ++++ +Question: {question} +""" -Question: -{question} +custom_question_template = """" +Combine the chat history and follow up question into a standalone question. ++++ +Chat History: {chat_history} ++++ +Follow up question: {question} ++++ +Standalone question: """ -QA_PROMPT = PromptTemplate(template=TEMPLATE, input_variables=["question", "context"]) +translate_template = """" +Act as a professional translator. Use the following step-by-step instructions: +1: assess in what language input below delimited by triple pluses is written. +2. carry out one of tasks A or B below: +A: if the input language is different from {language} then translate the input below delimited by triple pluses to natural {language} language, maintaining tone of voice and length +B: if the input language is the same as {language} there is no need for translation, simply return the original input below delimited by triple pluses as the answer. +3. Only return the answer from step 2, do not show any code or additional information. ++++ +input: +{answer} ++++ +Translated input: +""" + +custom_question_prompt = PromptTemplate( + template=custom_question_template, input_variables=["chat_history", "question"] +) + +translation_prompt = PromptTemplate( + template=translate_template, input_variables=["language", "answer"] +) +# prompt to be used by retrieval chain, note this is the default prompt name, so nowhere assigned +QA_PROMPT = PromptTemplate( + template=chat_template, input_variables=["question", "context"] +) + +def translate_answer(answer, language): + translate_llm = AzureOpenAI(deployment_name=os.environ["AI_DEPLOYMENT_NAME"], model_name=os.environ["AI_MODEL_NAME"], + temperature=0, verbose=verbose_models) + prompt = translation_prompt.format(answer=answer, language=language) + return translate_llm(prompt) + + +def setup_chain(db_path): + generic_llm = AzureOpenAI(deployment_name=os.environ["AI_DEPLOYMENT_NAME"], model_name=os.environ["AI_MODEL_NAME"], + temperature=0, verbose=verbose_models) -def setup_chain(): - llm = AzureOpenAI(deployment_name=os.environ["AI_DEPLOYMENT_NAME"], model_name=os.environ["AI_MODEL_NAME"], temperature=os.environ["AI_MODEL_TEMPERATURE"]) embeddings = OpenAIEmbeddings(deployment=os.environ["AI_EMBEDDINGS_DEPLOYMENT_NAME"], chunk_size=1) - vectorstore = FAISS.load_local("local_index", embeddings) - chain = ConversationalRetrievalChain.from_llm( - llm, vectorstore.as_retriever(), return_source_documents=True - ) - print("\n\nchain:\n", chain) + vectorstore = FAISS.load_local(db_path, embeddings) + retriever = vectorstore.as_retriever() + + chat_llm = AzureChatOpenAI(deployment_name=os.environ["AI_DEPLOYMENT_NAME"], + model_name=os.environ["AI_MODEL_NAME"], temperature=os.environ["AI_MODEL_TEMPERATURE"], + max_tokens=max_token_limit) - return chain + conversation_chain = ConversationalRetrievalChain.from_llm( + llm=chat_llm, + retriever=retriever, + condense_question_prompt=custom_question_prompt, + chain_type="stuff", + verbose=verbose_models, + condense_question_llm=generic_llm, + return_source_documents=True, + combine_docs_chain_kwargs={"prompt": QA_PROMPT} + ) + return conversation_chain diff --git a/app.py b/app.py index 5d90db6..d35b2d5 100644 --- a/app.py +++ b/app.py @@ -1,18 +1,10 @@ +from langchain.callbacks import get_openai_callback import os import pika import json import ai_utils import def_ingest -from dotenv import load_dotenv - -load_dotenv() - -config = { - "rabbitmq_host": os.getenv('RABBITMQ_HOST'), - "rabbitmq_user": os.getenv('RABBITMQ_USER'), - "rabbitmq_password": os.getenv('RABBITMQ_PASSWORD'), - "rabbitmqrequestqueue": "alkemio-chat-guidance", -} +from config import config, website_source_path, website_generated_path, vectordb_path, generate_website user_data = {} @@ -20,29 +12,65 @@ config['rabbitmq_password']) parameters = pika.ConnectionParameters(host=config['rabbitmq_host'], credentials=credentials) +print(f"\About to connect to RabbitMQ with params {config['rabbitmq_user']}: {config['rabbitmq_host']}\n") connection = pika.BlockingConnection(parameters) channel = connection.channel() channel.queue_declare(queue=config['rabbitmqrequestqueue']) -def query(user_id, query): - print(f"Query from user {user_id}: {query}") +# Check if the vector database exists +if os.path.exists(vectordb_path+"/index.pkl"): + print(f"The file vector database is present") +else: + # ingest data + if generate_website: + def_ingest.clone_and_generate(config['website_repo'], website_generated_path, website_source_path) + def_ingest.mainapp(config['source_website']) + +qa_chain = ai_utils.setup_chain(vectordb_path) + +def query(user_id, query, language_code): + print(f"\nQuery from user {user_id}: {query}\n") if user_id not in user_data: reset(user_id) + user_data[user_id]['language'] = ai_utils.get_language_by_code(language_code) + + print(f"\nlanguage: {user_data[user_id]['language']}\n") chat_history = user_data[user_id]['chat_history'] - llm_result = ai_utils.setup_chain()( - {"question": query, "chat_history": chat_history} + # llm_result =ai_utils.qa_chain( + # query, + # chat_history, + # user_data[user_id]['language'] + # ) + with get_openai_callback() as cb: + llm_result = qa_chain({"question": query, "chat_history": chat_history}) + #translation = ai_utils.translate_answer(llm_result['answer'], user_data[user_id]['language']) + translation = llm_result['answer'] + + print(f"\nTotal Tokens: {cb.total_tokens}") + print(f"\nPrompt Tokens: {cb.prompt_tokens}") + print(f"\nCompletion Tokens: {cb.completion_tokens}") + print(f"\nTotal Cost (USD): ${cb.total_cost}") + + print(f"\n\nLLM result: {llm_result}\n\n") + print(f"\n\ntranslation result: {translation}\n\n") + + formatted_messages = ( + f"Human:'{llm_result['question']}'", + f"AI:'{llm_result['answer']}'" ) + user_data[user_id]['chat_history'].append(formatted_messages) + + # only keep the last 3 entires of that chat history to avoid exceeding the token limit. + user_data[user_id]['chat_history'] = user_data[user_id]['chat_history'][-3:] - chat_history.append((llm_result["question"], llm_result["answer"])) + print(f"new chat history {user_data[user_id]['chat_history']}") response = json.dumps({ - "question": str(llm_result["question"]), - "answer": str(llm_result["answer"]), - "sources": str(llm_result["source_documents"]) + "question": str(llm_result["question"]), "answer": str(translation), "sources": str(llm_result["source_documents"]), "prompt_tokens": cb.prompt_tokens, "completion_tokens": cb.completion_tokens, "total_tokens": cb.total_tokens, "total_cost": cb.total_cost } ) @@ -50,14 +78,15 @@ def query(user_id, query): def reset(user_id): user_data[user_id] = { - 'chat_history': [], - 'docs': [] + 'chat_history': [] } return "Reset function executed" -def ingest(): - def_ingest.mainapp() +def ingest(source_url, website_repo, destination_path, source_path): + def_ingest.clone_and_generate(website_repo, destination_path, source_path) + def_ingest.mainapp(source_url) + return "Ingest function executed" def on_request(ch, method, props, body): @@ -67,16 +96,16 @@ def on_request(ch, method, props, body): operation = message['pattern']['cmd'] if operation == 'ingest': - response = ingest() + response = ingest(config['source_website'], config['website_repo'], website_generated_path, website_source_path) else: if user_id is None: response = "userId not provided" else: if operation == 'query': - if 'question' in message['data']: - response = query(user_id, message['data']['question']) + if ('question' in message['data']) and ('language' in message['data']): + response = query(user_id, message['data']['question'], message['data']['language']) else: - response = "Query parameter not provided" + response = "Query parameter(s) not provided" elif operation == 'reset': response = reset(user_id) else: @@ -91,10 +120,10 @@ def on_request(ch, method, props, body): ch.basic_ack(delivery_tag=method.delivery_tag) print(f"Response sent for correlation_id: {props.correlation_id}") + print(f"Response sent to: {props.reply_to}") + print(f"response: {response}") -def_ingest.mainapp() - channel.basic_qos(prefetch_count=1) channel.basic_consume(queue=config['rabbitmqrequestqueue'], on_message_callback=on_request) diff --git a/config.py b/config.py new file mode 100644 index 0000000..36164db --- /dev/null +++ b/config.py @@ -0,0 +1,19 @@ +import os +from dotenv import load_dotenv +load_dotenv() + +config = { + "rabbitmq_host": os.getenv('RABBITMQ_HOST'), + "rabbitmq_user": os.getenv('RABBITMQ_USER'), + "rabbitmq_password": os.getenv('RABBITMQ_PASSWORD'), + "rabbitmqrequestqueue": "alkemio-chat-guidance", + "source_website": os.getenv('AI_SOURCE_WEBSITE'), + "website_repo": os.getenv('AI_WEBSITE_REPO'), + "local_path": os.getenv('AI_LOCAL_PATH') +} + +local_path = config['local_path'] +website_source_path = local_path + '/website/source' +website_generated_path = local_path + '/website/generated' +vectordb_path = local_path + "/vectordb" +generate_website = True diff --git a/def_ingest.py b/def_ingest.py index 69ed819..fe5f082 100644 --- a/def_ingest.py +++ b/def_ingest.py @@ -1,48 +1,57 @@ import os -import requests -from langchain.document_loaders import SeleniumURLLoader +import re from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import FAISS import xml.etree.ElementTree as ET -from selenium import webdriver -from selenium.webdriver.chrome.options import Options -from selenium.webdriver.chrome.service import Service -# Setup Chrome Driver, may need to change based on system -service = Service("/usr/bin/chromedriver") -options = Options() -options.add_argument("--headless") -options.add_argument("--no-sandbox") -driver = webdriver.Chrome(service=service, options=options) +from langchain.document_transformers import BeautifulSoupTransformer +from langchain.document_loaders import BSHTMLLoader +from bs4 import BeautifulSoup +import shutil +import subprocess +import xml.etree.ElementTree as ET +from config import local_path, website_generated_path, vectordb_path + + +def extract_urls_from_sitemap(base_directory): + """ + Purpose: + Read the sitemap.xml file and create a list of local html files to be read + Args: + base_directory: path to directory containing local html files + Returns: + list of files to be retrieved + """ -def extract_urls_from_sitemap(sitemap_url): - response = requests.get(sitemap_url) - if response.status_code != 200: - print(f"Failed to fetch sitemap: {response.status_code}") - return [] + sitemap_file = base_directory + os.sep + "sitemap.xml" + print(f"Extracting urls using {sitemap_file}") - sitemap_content = response.content - root = ET.fromstring(sitemap_content) + # Parse the XML directly from the file + tree = ET.parse(sitemap_file) + root = tree.getroot() # Extract the URLs from the sitemap - urls = [ - elem.text + to_be_retieved = [ + base_directory + elem.text + "index.html" for elem in root.iter("{http://www.sitemaps.org/schemas/sitemap/0.9}loc") ] - return urls + print(f"...sitemap as urls: {to_be_retieved[:5]}....") + return to_be_retieved -def load_html_text(sitemap_urls): - loader = SeleniumURLLoader(urls=sitemap_urls) +def load_html_text(target_files): + + loader = BSHTMLLoader(target_files) data = loader.load() - text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20) - texts = text_splitter.split_documents(data) + # Transform + bs_transformer = BeautifulSoupTransformer() + docs_transformed = bs_transformer.transform_documents(data, tags_to_extract=["body"]) - return texts + return docs_transformed def embed_text(texts, save_loc): @@ -51,30 +60,101 @@ def embed_text(texts, save_loc): docsearch.save_local(save_loc) +def read_and_parse_html(local_source_path, source_website_url): + """ + Purpose: read the target files from disk, transform html to readable text, remove sequnetial CR and space sequences, fix the document source address + and split into chunks. + Args: + local_source_path: path to directory containing local html files + source_website_url: base url of source website + Returns: list of parses and split doucments + """ + # Get all links from the sitemaps + print(f"generating html: {local_source_path}, {source_website_url}") + full_sitemap_list = extract_urls_from_sitemap(website_generated_path) + + data = [] + for file_name in full_sitemap_list: + loader = BSHTMLLoader(file_name) + document = loader.load() + + body_text = document[0] + + # first remove duplicate spaces, then remove duplicate '\n\n', then remove duplicate '\n \n ' + body_text.page_content = re.sub(r'(\n ){2,}', '\n', re.sub(r'\n+', '\n', re.sub(r' +', ' ', body_text.page_content))) + + # remove the local directory from the source object + body_text.metadata['source'] = body_text.metadata['source'].replace(local_source_path, source_website_url) + + data.append(body_text) + + text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=0) + texts = text_splitter.split_documents(data) + return texts -def mainapp() -> None: +def remove_and_recreate(dir_path): + try: + if os.path.exists(dir_path): + shutil.rmtree(dir_path) + print(f"Directory {dir_path} and its contents removed successfully.") + os.makedirs(dir_path) + print(f"...directory {dir_path} (re)created.") + except OSError as e: + print(f"Error: {e.strerror}") + +def clone_and_generate(website_repo, destination_path, source_path): + print(f"About to generate website") + remove_and_recreate(source_path) + remove_and_recreate(destination_path) + print(f"...cloning or updating repo") + + # Check if the repository already exists in the source_path + if os.path.exists(os.path.join(source_path, '.git')): + # Repository exists, perform a git pull to update it + git_pull_command = ['git', 'pull', 'origin', 'main'] # Modify branch name as needed + result_pull = subprocess.run(git_pull_command, cwd=source_path, capture_output=True, text=True) + print(f"git pull result: {result_pull.stdout}") + else: + # Repository doesn't exist, perform a git clone + clone_command = ['git', 'clone', website_repo, source_path] + result_clone = subprocess.run(clone_command, capture_output=True, text=True) + print(f"git clone result: {result_clone.stdout}") + + os.chdir(source_path) + print(f"...cloned/updated, moved to directory: {os.getcwd()}") + + env = os.environ.copy() + additional_path_go = '/usr/local/go/bin' + additional_path_usr = '/usr/local' + env["PATH"] = additional_path_go + os.pathsep + additional_path_usr + os.pathsep + env["PATH"] + hugo_command = ['/usr/local/hugo', '--gc', '-b', '/', '-d', destination_path] + result_hugo = subprocess.run(hugo_command, env=env, capture_output=True, text=True) + print(f"hugo result: {result_hugo.stdout}") + + +def mainapp(source_website_url) -> None: """ Purpose: - Ingest data into a a local db + ingest the trnaformed website contents into a vector database in presized chunks. Args: - N/A + source_website_url: full url of source website, used to return the proper link for the source documents. Returns: N/A """ - # Site maps for the Alkemio website - sitemap_url_list = [ - "https://www.alkemio.org/sitemap.xml", - ] + # open file to check output + f = open(local_path+"/ingestion_output.txt", "w") - # Get all links from the sitemaps - full_sitemap_list = [] - for sitemap in sitemap_url_list: - full_sitemap_list.extend(extract_urls_from_sitemap(sitemap)) - - print(full_sitemap_list) - # get the raw html text - texts = load_html_text(full_sitemap_list) - # print(texts) - # Save embeddings to local_index - embed_text(texts, "local_index") + # read and parse the files + texts = read_and_parse_html(website_generated_path, source_website_url) + + # Save embeddings to vectordb + embed_text(texts, vectordb_path) + + f.write(str(texts)) + f.close() + + +# only execute if this is the main program run (so not imported) +if __name__ == "__main__": + mainapp(os.getenv('AI_SOURCE_WEBSITE')) diff --git a/docker-compose.yaml b/docker-compose.yaml new file mode 100644 index 0000000..49e3018 --- /dev/null +++ b/docker-compose.yaml @@ -0,0 +1,34 @@ +version: '3' + +networks: + alkemio: + +services: + guidance-engine: + container_name: guidance-engine + volumes: + - '/dev/shm:/dev/shm' + - ~/alkemio/data:/home/alkemio/data + env_file: + - .env + image: alkemio/guidance-engine:v0.2.0 + depends_on: + rabbitmq: + condition: "service_healthy" + networks: + - alkemio + + rabbitmq: + image: rabbitmq:3-management-alpine + container_name: 'rabbitmq' + healthcheck: + test: rabbitmq-diagnostics check_port_connectivity + interval: 30s + timeout: 30s + retries: 10 + ports: + - 5672:5672 + - 8080:15672 + + networks: + - alkemio \ No newline at end of file diff --git a/payloads.json b/payloads.json new file mode 100644 index 0000000..6c5715d --- /dev/null +++ b/payloads.json @@ -0,0 +1,20 @@ +{ + "data": { + "userId": "user1", + "question": "What are the key Alkemio concepts?", + "language": "UK" + }, + "pattern": { + "cmd": "query" + } +} + + +{ + "data": { + "userId": "user1" + }, + "pattern": { + "cmd": "reset" + } +} diff --git a/poetry.lock b/poetry.lock index fd8f0d3..4633436 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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