Build RAG Application Using a LLM Running on Local Computer with Ollama Llama2 and LlamaIndex

Privacy-preserving LLM without GPU

(λx.x)eranga
Effectz.AI

--

Background

In my previous post, I explored how to develop a Retrieval-Augmented Generation (RAG) application by leveraging a locally-run Large Language Model (LLM) through Ollama and Langchain. This time, I will demonstrate building the same RAG application using a different tool, LlamaIndex. All source codes related to this post have been published on GitLab. Please clone the repository to follow along with the post.

LlamaIndex

LlamaIndex is a comprehensive framework designed for constructing production-level Retrieval-Augmented Generation (RAG) applications. It provides a user-friendly and flexible interface that allows developers to connect LLMs with external data sources, such as private databases, documents in formats like PDFs and PowerPoints, and applications including Notion and Slack, as well as databases like PostgreSQL and MongoDB. It includes connectors for various data sources and formats, along with methods for structuring data to ensure seamless compatibility with LLMs, effectively transforming enterprise data into production-ready LLM applications. The framework offers a suite of tools for efficient data ingestion, indexing, and querying, making it an all-encompassing solution for RAG applications. So Llamaindex turn your enterprise data into production-ready RAG applications.

In comparison to LangChain, another framework for building RAG applications, LlamaIndex distinguishes itself with a focused dedication to search and retrieval applications within RAG systems. While LangChain serves as a generic platform for leveraging LLMs in a broad range of projects, LlamaIndex specializes in building search and retrieval applications RAG systems. It provides a simple interface for querying LLMs and retrieving relevant documents. LlamaIndex is also more efficient than Langchain, making it a better choice for applications that need to process large amounts of data.

Based on my personal experience, RAG applications built using LlamaIndex tend to generate more accurate responses compared to those developed with LangChain.

Ollama

Ollama is a lightweight and flexible framework designed for the local deployment of LLM on personal computers. It simplifies the development, execution, and management of LLMs with an intuitive API and provides a collection of pre-configured models ready for immediate use across a variety of applications. Central to its design is the bundling of model weights, configurations, and data into a unified package, encapsulated within a Modelfile.

The framework features a curated assortment of pre-quantized, optimized models, such as Llama 2, Mistral, and Gemma, which are ready for deployment. These models are specifically engineered to operate on standard consumer hardware, spanning CPUs and GPUs, and are compatible with multiple operating systems, including macOS, Linux, and Windows. This approach negates the necessity for users to undertake intricate model optimization tasks themselves.

Given that LLMs typically demand robust GPUs for their operation due to their considerable size, the models supported by Ollama employ neural network quantization. This technique dramatically reduces the hardware requirements, allowing LLMs to function efficiently on common computing devices without an internet connection. Ollama thus makes it more accessible to LLM technologies, enabling both individuals and organizations to leverage these advanced models on consumer-grade hardware.

RAG Application

This RAG application utilizes a custom-crafted dataset, dynamically harvested from a collection of documents. It enables user interaction with the dataset (e.g., querying or searching data) via an API, such as a REST API. For illustrative purposes, a cybersecurity scenario focusing on a red-team-related document set has been chosen. The data extracted from these documents are segmented and stored in the Chroma vector database as vector embeddings, allowing users to interact smoothly with the red-team documents through the API.

For the LLM component of this RAG application, I have selected the Llama2 7B model, executed via Ollama. Llama2 on Ollama, a quantized version of Meta’s Llama-2 LLM, is designed for efficient operation on consumer-grade hardware, including CPUs. In this setup, the Llama2 LLM, integrated with Ollama, offers responses to user inquiries based on the content found within the red-team-specific documents. This seamless fusion of the RAG application with the LLM is achieved through LlamaIndex, enhancing the application’s ability to provide relevant and accurate information.

The following are the main functionalities of the RAG application. A comprehensive functional architecture, encompassing these various components, is detailed in the figure below.

1. Scrape Document Data

LlamaIndex provide different types of document loaders to load data from different source as documents. SimpleDirectoryReader is one such document loader that can be used to load the data from a directory. This step employs LlamaIndex’s SimpleDirectoryReader to scrape documents from the given directory docs. This data used to create vector embedding and answer questions of the user.

2. Split Documents

When handling lengthy pieces of text, it’s essential to divide the text into smaller segments. Although this task seems straightforward, it can encompass considerable complexity. The goal is to ensure that semantically related segments of text remain together. The LlamaIndex node parsers accomplishes this task effectively. Essentially, it divides the text into small, semantically meaningful units (often sentences). These smaller segments are then combined to form larger chunks until they reach a certain size, determined by a specific function. Upon reaching this size, the chunk is designated as an individual piece of text, and the process begins anew with some overlap. LlamaIndex behind the scene uses TokenTextSplitter to split the scraped documents into manageable chunks.

3. Create Vector Embedding

Once the data is collected and split, the next step involves converting this textual information into vector embeddings. These embeddings are then created from the split data. Text embeddings are crucial to the functioning of LLM operations. While it’s technically feasible to work with language models using natural language, storing and retrieving such data is highly inefficient. To enhance efficiency, it’s necessary to transform text data into vector form. There are dedicated machine learning models specifically designed for creating embeddings from text. In this case, I have utilized open-souce HuggingFaceEmbedding model BAAI/bge-small-en-v1.5 to generate vector embeddings. The text is thereby converted into multidimensional vectors, which are essentially high-dimensional numerical representations capturing semantic meanings and contextual nuances. Once embedded, these data can be grouped, sorted, searched, and more. We can calculate the distance between two sentences to determine their degree of relatedness. Importantly, these operations transcend traditional database searches that rely on keywords, capturing instead the semantic closeness between sentences.

4. Store Vector Embedding in Chroma

The generated vector embeddings are then stored in the Chroma vector database. Chroma(commonly referred to as ChromaDB) is an open-source embedding database that makes it easy to build LLM apps by storing and retrieving embeddings and their metadata, as well as documents and queries. Chroma efficiently handles these embeddings, allowing for quick retrieval and comparison of text-based data. Traditional databases work well for exact queries but fall short when it comes to understanding the nuances of human language. Enter Vector Databases, a game-changer in handling semantic search. Unlike traditional text matching, which relies on exact words or phrases, vector databases like Postgres with pgvector process information semantically. This database is a cornerstone of the system’s ability to match user queries with the most relevant information from the scraped content, enabling fast and accurate responses.

5. User Ask Question

The system provides an API through which users can submit their questions. In this use case, users can ask any question related to the red-team cybersecurity use case. This API serves as the primary interface for interactions between the user and the chatbot. The API takes a parameter, user_id, which is used to identify different user sessions. This user_id is used for demonstration purposes. In real-world scenarios, it could be managed with an Authorization header (e.g., JWT Bearer token) in the HTTP request. The API is designed to be intuitive and accessible, enabling users to easily input their queries and receive responses.

6. Create Vector Embedding of Question

When a user submits a question through the API, the system converts this question into a vector embedding. The generation of the embedding is automatically handled by the BaseQueryEngin. This facilitates the semantic search of documents related to the question within the vector database.

7. Semantic Search Vector Database

Once the vector embedding for the question is created, the system employs semantic search to scan through the vector database, identifying content most relevant to the user’s query. By comparing the vector embedding of the question with those of the stored data, the system can accurately pinpoint information that is contextually similar or related to the query. In this scenario, LlamaIndex'sBaseQueryEngin automatically handles semantic searches based on the input query. The results of the semantic search are then identified as context for the LLM.

8. Generate Prompt

Next, the BaseQueryEngin generates a custom prompt with the user’s question and the semantic search result (context). A prompt for a language model is a set of instructions or input provided by the user to guide the model’s response. This helps the model understand the context and generate relevant and coherent language-based outputs, such as answering questions, completing sentences, or engaging in a conversation.

9. Post Prompt to LLM

After generating the prompt, it is posted to the LLM (in our case, the Llama2 7B) through LlamaIndex libraries Ollama(LlamaIndex officially supports the Ollama with in llama_index.llms). The LLM then finds the answer to the question based on the provided context. The BaseQueryEngin handles this function of posting the query to the LLM (behind the scenes, it uses Ollama REST APIs to submit the question).

10. LLM Generate Answer

The LLM, utilizing the advanced capabilities of Meta’s Llama-2, processes the question within the context of the provided content. It then generates a response and sends it back.

11. Send Answer Back to User

Finally, the answer received from the LLM is forwarded to the user via the HTTP API. Users can continue to ask different questions in subsequent requests by providing the same user_id. The system then recognizes the user’s chat history and includes it in the information sent to the LLM, along with the new semantic search results. This process ensures a seamless and contextually aware conversation, enriching the user experience with each interaction.

Implementation

The complete implementation of this ChatBot is detailed below. The full source code of the ChatBot agent is available for access and review on GitLab.

1. Configurations

In the config.py file, I have defined various configurations used in the ChatBot. These configurations are read through environment variables in adherence to the principles of 12-factor apps.

import os

# define init index
INIT_INDEX = os.getenv('INIT_INDEX', 'false').lower() == 'true'

# vector index persist directory
INDEX_PERSIST_DIRECTORY = os.getenv('INDEX_PERSIST_DIRECTORY', "./data/chromadb")

# http api port
HTTP_PORT = os.getenv('HTTP_PORT', 7654)

# mongodb config host, username, password
MONGO_HOST = os.getenv('MONGO_HOST', 'localhost')
MONGO_PORT = os.getenv('MONGO_PORT', 27017)
MONGO_USER = os.getenv('MONGO_USER', 'testuser')
MONGO_PASS = os.getenv('MONGO_PASS', 'testpass')

2. HTTP API

The HTTP API implementation is carried out in api.py. This API includes an HTTP POST endpoint api/question, which accepts a JSON object containing a question and user_id. The user_id is utilized for demonstration purposes. In a real-world application, this could be managed with an Authorization header (e.g., JWT Bearer token) in the HTTP request. When a question request is received from the user, it is forwarded to the chat function in the ChatBot model.

from flask import Flask
from flask import jsonify
from flask import request
from flask_cors import CORS
import logging
import sys
from model import *
from config import *

app = Flask(__name__)
CORS(app)

logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

@app.route('/api/question', methods=['POST'])
def post_question():
json = request.get_json(silent=True)
question = json['question']
user_id = json['user_id']
logging.info("post question `%s` for user `%s`", question, user_id)

resp = chat(question, user_id)
data = {'answer':resp}

return jsonify(data), 200

if __name__ == '__main__':
init_llm()
index = init_index(Settings.embed_model)
init_query_engine(index)

app.run(host='0.0.0.0', port=HTTP_PORT, debug=True)

3. Model

Below is the implementation of the Model. It includes a function, init_index, which scrapes data from a given web URL and creates the vector store index. The init_llm function initialize LlamaIndex Settings with Ollama’s Llama2 LLM and HuggingFaces' BAAI/bge-small-en-v1.5 embedding. The init_query_engine function initializes the LlamIndex query engine with the previously created vector store index and custom prompt. The chat function is responsible for posting questions to the LLM.

import chromadb
import logging
import sys

from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import (Settings, VectorStoreIndex, SimpleDirectoryReader, PromptTemplate)
from llama_index.core import StorageContext
from llama_index.vector_stores.chroma import ChromaVectorStore

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')


global query_engine
query_engine = None

def init_llm():
llm = Ollama(model="llama2", request_timeout=300.0)
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

Settings.llm = llm
Settings.embed_model = embed_model


def init_index(embed_model):
reader = SimpleDirectoryReader(input_dir="./docs", recursive=True)
documents = reader.load_data()

logging.info("index creating with `%d` documents", len(documents))

chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("iollama")

vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)

return index


def init_query_engine(index):
global query_engine

# custome prompt template
template = (
"Imagine you are an advanced AI expert in cyber security laws, with access to all current and relevant legal documents, "
"case studies, and expert analyses. Your goal is to provide insightful, accurate, and concise answers to questions in this domain.\n\n"
"Here is some context related to the query:\n"
"-----------------------------------------\n"
"{context_str}\n"
"-----------------------------------------\n"
"Considering the above information, please respond to the following inquiry with detailed references to applicable laws, "
"precedents, or principles where appropriate:\n\n"
"Question: {query_str}\n\n"
"Answer succinctly, starting with the phrase 'According to cyber security law,' and ensure your response is understandable to someone without a legal background."
)
qa_template = PromptTemplate(template)

# build query engine with custom template
# text_qa_template specifies custom template
# similarity_top_k configure the retriever to return the top 3 most similar documents,
# the default value of similarity_top_k is 2
query_engine = index.as_query_engine(text_qa_template=qa_template, similarity_top_k=3)

return query_engine


def chat(input_question, user):
global query_engine

response = query_engine.query(input_question)
logging.info("got response from llm - %s", response)

return response.response

Run Application

Below are the main steps to operate the ChatBot application and interact with it. Questions can be submitted using the HTTP API, and responses will be received accordingly.

1. Install Dependencies

In this application, I have utilized a number of Python packages that need to be installed using Python’s pip package manager before running the application. The requirements.txt file lists all the necessary packages.

huggingface-hub
sentence-transformers

Flask==2.0.1
Werkzeug==2.2.2
flask-cors

langchain==0.0.352
tiktoken
unstructured
unstructured[local-pdf]
unstructured[local-inference]

llama-index
llama-index-llms-ollama
llama-index-embeddings-huggingface
torch

# manually install below module to get rid of dependency install issues with `requirements.txt`
# pip install llama-index-vector-stores-chroma

I have used python virtual environment to setup these dependencies. These packages can be easily installed by executing the command pip install -r requirements.txt.

# create virtual environment in `ollama` source directory
❯❯ cd iollama
❯❯ python -m venv .venv

# enable virtual environment
❯❯ source .venv/bin/activate

# install dependencies
❯❯ pip install -r requirements.txt

# manually install below module to get rid of dependency install issues with `requirements.txt`
❯❯ pip install llama-index-vector-stores-chroma

2. Run Ollama Llama2

Ollama offers versatile deployment options, enabling it to run as a standalone binary on macOS, Linux, or Windows, as well as within a Docker container. This flexibility ensures that users can easily set up and interact with LLMs on their preferred platform. Ollama supports both command-line and REST API interactions, allowing for seamless integration into a variety of workflows and applications. An example of its utility is running the Llama2 model through Ollama, demonstrating its capability to host and manage LLMs efficiently. Below is an illustrated method for deploying Ollama on MacOS, highlighting my experience running the Llama2 model on this platform.

# download ollama for macos from here and insatll it
# once ins
https://github.com/ollama/ollama?tab=readme-ov-file

# once installed, the ollama binary will be available in `/usr/local/bin`
❯❯ ls /usr/local/bin/
ollama

# the ollama images, manifests data placed in the ~/.ollama directory
❯❯ ls ~/.ollama
history id_ed25519 id_ed25519.pub logs models

# run llama2 llm
# this will download the llm image and run it
# the downloaded image manifest will be stored in ~/.ollama/models
# if llm image already exists it will start the llm image
root@150bc5106246:/# ollama run llama2
pulling manifest
pulling 8934d96d3f08... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 3.8 GB
pulling 8c17c2ebb0ea... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 7.0 KB
pulling 7c23fb36d801... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 4.8 KB
pulling 2e0493f67d0c... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 59 B
pulling fa304d675061... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 91 B
pulling 42ba7f8a01dd... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ 557 B
verifying sha256 digest
writing manifest
removing any unused layers
success
>>>

# exit from llm console
>>> /bye
root@c96f4fc1be6f:/#

# list running llms
root@150bc5106246:/# ollama list
NAME ID SIZE MODIFIED
llama2:latest 78e26419b446 3.8 GB 10 hours ago

# reconnect to the llm console
root@c96f4fc1be6f:/# ollama run llama2
>>>

# ask question via llm console
root@c96f4fc1be6f:/# ollama run llama2
>>> what is docker
Docker is an open-source platform that enables you to create, deploy, and run applications in containers. Containers are lightweight and portable, allowing you to move your application between different
environments without worrying about compatibility issues. Docker provides a consistent and reliable way to deploy applications, making it easier to manage and scale your infrastructure.



---



# ollama exposes REST API(`api/generate`) to the llm which runs on port `11434`
# we can ask question via the REST API(e.g using `curl`)
# ask question and get answer as streams
# `"stream": true` will streams the output of llm(e.g send word by word as stream)
❯❯ curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt": "what is docker?",
"stream": true
}'
{"model":"llama2","created_at":"2024-03-17T10:41:53.358162047Z","response":"\n","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:53.494021698Z","response":"D","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:53.630381369Z","response":"ocker","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:53.766590368Z","response":" is","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:53.902649027Z","response":" an","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.039338585Z","response":" open","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.175494123Z","response":"-","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.311130558Z","response":"source","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.447809241Z","response":" platform","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.585971524Z","response":" that","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.723769251Z","response":" enables","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.862244297Z","response":" you","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:54.999796889Z","response":" to","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:55.136406278Z","response":" create","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:55.273430683Z","response":",","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:55.411326998Z","response":" deploy","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:55.54792922Z","response":",","done":false}
{"model":"llama2","created_at":"2024-03-17T10:41:55.68550623Z","response":" and","done":false}

# ask question and get answer without stream
# that will wail till getting full response from llm and output
❯❯ curl http://localhost:11434/api/generate -d '{
"model": "phi",
"prompt": "Why is docker?",
"stream": false
}'
{"model":"phi","created_at":"2024-03-16T23:42:34.140800795Z","response":" Docker is a containerization platform that allows you to package your application code, dependencies, and runtime environment into a single executable container. This makes it easy to run your applications on any machine with Docker installed, as long as the container has the necessary dependencies and configuration settings. Containers are also more isolated from each other than traditional installations of applications, which can help improve performance and security. Additionally, Docker provides tools for automating common tasks such as building and deploying containers, making it a popular choice among developers and IT professionals.\n","done":true,"context":[11964,25,317,8537,1022,257,11040,2836,290,281,11666,4430,8796,13,383,8796,3607,7613,7429,284,262,2836,338,2683,13,198,12982,25,4162,318,36253,30,198,48902,25,25716,318,257,9290,1634,3859,326,3578,345,284,5301,534,3586,2438,11,20086,11,290,19124,2858,656,257,2060,28883,9290,13,770,1838,340,2562,284,1057,534,5479,319,597,4572,351,25716,6589,11,355,890,355,262,9290,468,262,3306,20086,290,8398,6460,13,2345,50221,389,635,517,11557,422,1123,584,621,4569,26162,286,5479,11,543,460,1037,2987,2854,290,2324,13,12032,11,25716,3769,4899,329,3557,803,2219,8861,884,355,2615,290,29682,16472,11,1642,340,257,2968,3572,1871,6505,290,7283,11153,13,198],"total_duration":6343449574,"load_duration":21148773,"prompt_eval_duration":65335000,"eval_count":107,"eval_duration":6256397000}%

3. Run RAG Application

The RAG application can be initiated through api.py as outlined below. Prior to running it, it's necessary to set a few configurations via environment variables. Once api.py is executed, it will start the HTTP API, enabling users to post their questions.

# enable virtual environment in `ollama` source directory 
❯❯ cd iollama
❯❯ source .venv/bin/activate

# run aplication
2024-03-24 18:04:45,917 - INFO - index creating with `46` documents
2024-03-24 18:04:45,922 - INFO - Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.
* Serving Flask app 'api' (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: on
2024-03-24 18:04:47,151 - INFO - WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:7654
* Running on http://192.168.0.110:7654
2024-03-24 18:04:47,151 - INFO - Press CTRL+C to quit
2024-03-24 18:04:47,151 - INFO - * Restarting with stat
2024-03-24 18:04:52,083 - INFO - index creating with `46` documents
2024-03-24 18:04:52,088 - INFO - Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.
2024-03-24 18:04:53,338 - WARNING - * Debugger is active!
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
2024-03-24 18:04:53,353 - INFO - * Debugger PIN: 102-298-684

4. Post Question

Once the RAG application is running, I can submit questions related to the red-team use case via the HTTP API.

# post question
❯❯ curl -i -XPOST "http://localhost:7654/api/question" \
--header "Content-Type: application/json" \
--data '
{
"question": "what is red team",
"user_id": "kakka"
}
'

# response from llm
HTTP/1.1 200 OK
{
"answer": "According to cyber security law, a red team is an attack technique used in cyber security testing to simulate real-world attacks on an organization's security defenses. The goal of red teaming is to identify gaps in the organization's security response and improve its overall cyber resilience.\n\nIn the UK, there are various laws and regulations that govern cyber security, including the General Data Protection Regulation (GDPR) and the Cybersecurity Act 2019. These laws require organizations to implement appropriate security measures to protect their systems and data from cyber threats.\n\nThe GDPR, for instance, imposes obligations on organizations to implement appropriate technical and organizational measures to ensure the security of personal data. Organizations must also conduct regular risk assessments to identify potential vulnerabilities and take appropriate actions to mitigate them.\n\nSimilarly, the Cybersecurity Act 2019 sets out a framework for improving cyber security in the UK. The act requires organizations to take appropriate measures to protect their systems and data from cyber threats, including implementing incident response plans and conducting regular security testing and evaluation.\n\nIn addition to legal requirements, there are also industry-specific regulations and guidelines that govern cyber security in certain sectors, such as finance, healthcare, and energy. For example, the Payment Card Industry Data Security Standard (PCI DSS) provides specific requirements for organizations that handle credit card transactions, while the Health Insurance Portability and Accountability Act (HIPAA) sets out security standards for protecting sensitive healthcare information.\n\nIn summary, red teaming is an important cyber security testing technique that can help organizations identify gaps in their security defenses and improve their overall cyber resilience. The laws and regulations governing cyber security in the UK provide a framework for implementing appropriate security measures to protect systems and data from cyber threats."
}



---



❯❯ curl -i -XPOST "http://localhost:7654/api/question" \
--header "Content-Type: application/json" \
--data '
{
"question": "red team vs blue team",
"user_id": "kakka"
}
'
HTTP/1.1 200 OK

{
"answer": "According to cyber security law, the red team and blue team are essential components of an organization's cybersecurity strategy. The red team specializes in simulating real threats and attacks to identify vulnerabilities in defense systems, while the blue team focuses on analyzing these attacks and developing methodologies for their mitigation and prevention. The purple team aims to facilitate effective interaction between offensive and defensive elements, analyze the results, and suggest measures for optimizing mutual strategies and tactics.\n\nUnderstanding the roles of these teams is crucial in today's digital landscape, where cybersecurity threats are becoming more sophisticated and frequent. The red team's simulated attacks help organizations identify weaknesses in their defenses, allowing them to take proactive measures to address these vulnerabilities. Similarly, the blue team's analysis of these attacks helps organizations develop effective countermeasures to prevent future attacks.\n\nThe purple team's coordination of these efforts ensures that both teams work together effectively, sharing knowledge and expertise to enhance overall cybersecurity strategy. By collaborating closely, organizations can transform their cybersecurity approaches from static measures into a dynamic, continuously updated system. This allows them to deflect and anticipate threats more effectively, safeguarding their assets and ensuring the resilience of their work processes.\n\nRelevant laws and precedents include:\n\n1. The Cybersecurity Information Sharing Act (CISA) of 2015: This law encourages the sharing of cyber threat information between government agencies, private companies, and other stakeholders to enhance cybersecurity defenses.\n2. The European Union's General Data Protection Regulation (GDPR): This regulation sets forth comprehensive data protection rules for organizations operating in the EU, including provisions related to data privacy and security.\n3. The National Institute of Standards and Technology (NIST) Cybersecurity Framework: This framework provides a set of guidelines for managing cybersecurity risks in critical infrastructure sectors, including energy, transportation, and healthcare.\n4. Case law: Numerous court cases have established the legal basis for red teaming and blue teaming exercises. For instance, the 2018 case of United States v. John Doe et al. (D.D.C. No. 17-cr-0014) highlighted the importance of conducting regular security assessments to prevent cyber attacks and protect sensitive information.\n5. Industry standards: Organizations can refer to industry-specific standards, such as the Payment Card Industry Data Security Standard (PCI DSS) for payment card processing firms or the Health Insurance Portability and Accountability Act (HIPAA) for healthcare providers, to ensure compliance with regulatory requirements.\n\nIn summary, red team vs blue team is a crucial aspect of cyber security law, as it highlights the importance of proactive defense measures and effective collaboration between offensive and defensive elements to protect digital assets and maintain continuous protection."
}

What’s Next

In an upcoming post, I plan to delve into the process of fine-tuning or training a Large Language Model (LLM) using a custom dataset, with a particular focus on Meta’s Llama-2 LLM. Stay tuned :)

Reference

  1. https://medium.com/rahasak/build-rag-application-using-a-llm-running-on-local-computer-with-ollama-and-langchain-e6513853fda0
  2. https://medium.com/rahasak/build-rag-application-using-a-llm-running-on-local-computer-with-gpt4all-and-langchain-13b4b8851db8
  3. https://medium.com/@zilliz_learn/persistent-vector-storage-for-llamaindex-ef96133fc128
  4. https://stephencollins.tech/posts/web-content-indexing-with-llamaindex
  5. https://medium.com/llamaindex-blog/how-to-train-a-custom-gpt-on-your-data-with-embedai-llamaindex-8a701d141070
  6. https://pub.towardsai.net/a-complete-guide-to-rag-and-llamaindex-2e1776655bfa
  7. https://betterprogramming.pub/getting-started-with-llamaindex-part-2-a66618df3cd
  8. https://nanonets.com/blog/llamaindex/
  9. https://docs.llamaindex.ai/en/stable/examples/vector_stores/ChromaIndexDemo/

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