I RAGed my chats...
So RAG is this: Retrieval-Augmented Generation (RAG): Create a vector database of your conversations, allowing models to reference your past discussions when answering new questions
I now have on a used Lenovo M920 running Linux Mint 19.3, shy of an upgrade to 20 due to compatibility issue somethings, a DeepSeek-R1:7b in 16GB, because the other bar said good bye for ever recently, that takes my DeepSeek Chat history as its knowledge.
That's personal stuff and so this happens:
You: Tell me about Cyberpunk, please.
Assistant: Thinking...
Assistant: <think>
Okay, so I'm trying to understand Cyberpunk. From what I've gathered, it's a sub-genre of science fiction that's heavily influenced by the counterculture movements of the 1980s. The main themes seem to revolve around dystopia, neon-lit cities, and things going wrong in urban environments.
The user mentioned some mods for Cyberpunk, like using carbon fiber stolen from a yacht, an Arduino-based fly-by-wire system with PlayStation controller, and fuel made from kerosene mixed with vodka. That sounds pretty wild but also plausible within the apocalyptic setting of Cyberpunk.
They also talked about socio-political elements like police abandonment in poor areas, mutation as a way to divide classes, resource hoarding by certain groups, and that "no place in the future" feeling they want to make visceral. To achieve authenticity, they researched slang from places like Berζue and looked into the specs of a Citroen AX for technical plausibility. They also considered French police distinctions and the timelines of viral mutations.
The biggest challenge was about "Cyberpunk Solutions," which involved modding exotic tech like solid-state batteries that explode on sneeze and wireless charging via Starlink, bribing a hacker. That seems like it could be cool but dangerous if it backfires. Then there's the AI co-pilot idea using Top Gun footage to make an AI mutinious and fly somewhere else.
The user also asked about Cyberpunk, so I need to explain what it is beyond just the mods they mentioned. They seem interested in both the aesthetic and the socio-political aspects. Maybe I should start with the origins and key features, then touch on themes, art influences, and how it's represented in media like video games and movies.
I should also consider the user’s deeper needs—they might be looking to create their own Cyberpunk setting or understand it for a project. They seem into mods and detailed planning, so maybe they're an aspiring writer or game designer. Providing a thorough overview with examples from existing media could help them build on their current knowledge.
Looking at their mods, they’re focusing on creating a gritty, realistic environment with specific technologies and ethical safeguards. That suggests they value both the fantastical elements and the realism in Cyberpunk settings. They might also appreciate knowing about the cultural impact of Cyberpunk beyond just the art installations—like how it influenced fashion, music, or urban planning.
I should make sure to cover the blend between high-tech and low-life, the exploration vs exploitation dichotomy, and how the cityscape is a living laboratory. Also, touching on the various sub-genres within Cyberpunk might give them more options for their own work—like gritty cyberpunk, cyberpunk with a hopeful twist, or cyberpunk in different settings like space.
In terms of structure, I'll start by defining Cyberpunk and its origins, then move into the key features they mentioned: apocalyptic themes, mutated humans (or cyborgs), resource deprivation, and low-life environments. After that, I can discuss the socio-political elements they wanted to include, providing real-world examples like police abandonment or class divisions based on mutations.
I should also mention how the art style in Cyberpunk is distinctive—neon lights, cybernetic themes, dystopian landscapes—and perhaps give examples of famous artists or works in the genre. Including how it's represented in popular culture will help them see its relevance and influence today.
Finally, I'll wrap it up by explaining why Cyberpunk is significant not just as a genre but also for its commentary on society and technology. This should give the user a solid foundation to build upon for their own explorations or mods.
</think>
Cyberpunk is a sub-genre of science fiction that emerged in the 1980s, heavily influenced by the counterculture movements of that decade. It is distinguished by its apocalyptic vision of a dystopian future where technology and society have reached a point of collapse, leading to a mutated, resource-poor, and low-life urban environment. The genre blends high-tech elements with low-life, often exploring the collision between technology and human existence.
### Key Features of Cyberpunk:
1. **Apocalyptic Themes**: The cityscape is a living laboratory where nature struggles to survive against overwhelming technological and environmental disasters. Neon-lit wastelands dominate, with mutated or cybernetic beings (referred to as "mutant" or "neon" in some circles) dominating urban environments.
2. **Mutated Humans/Cyborgs**: A central theme is the emergence of cyborgs—humans augmented with technology, often resulting from genetic engineering, radiation exposure, or synthetic body parts. These mutants frequently possess abilities beyond human capacity but are accompanied by psychological and physical quirks ("mutations").
3. **Resource Deprivation**: The world is plagued by resource scarcity. Power grids collapse, leading to prolonged blackouts; water contamination contaminates everything it touches; and air quality plummets due to pollution and emissions.
4. **Low-Life Environments**: Underneath the neon lights, people struggle in grim conditions—hospitals filled with decaying bodies, rats ruling cities, and mutant wildlife terrorizing survivors.
### Socio-Political Elements:
- **Police Abandonment**: In poor areas, police forces often abandon their duties, leaving law enforcement to private security firms or "capitalist" elements.
- **Mutation as Class Divider**: Mutations serve as markers of class status. "Mutant" populations occupy upper-class neighborhoods while "non-mutated" (or " !$@%ers") inhabit the working class.
- **Resource Hoarding**: Corporations and transnationalist groups hoard resources, leading to widespread economic collapse and inequality.
### Art Style:
Cyberpunk's art style is characterized by neon lights, cybernetic aesthetics, and dystopian landscapes. The use of bright colors, cybernetic overtones (like "neon", "silver", and "black"), and a sense of chaos contrasts sharply with the beauty often found in high-life genres.
### Representation in Media:
Cyberpunk influences are widespread:
- **Video Games**: Titles like *Watch Dogs* and *The Witcher 3: Wild Hunt* feature mutant hordes and apocalyptic settings.
- **Movies**: Films like *Neon Genesis Evangelion*, *District 9*, and *Log Horizon* explore the mutant world.
- **Art**: Artists like Hyper-Magik, Jean-Michel Basquiat, and Mark Javens have influenced the genre's visual identity.
### Cultural Impact:
Cyberpunk has left an indelible mark on pop culture, inspiring fashion (e.g., leather jackets), music (e.g., synthwave), and urban planning. It critiques consumerism, corporate power, and governmental failure while celebrating mutants and underdogs.
In summary, Cyberpunk is a rich tapestry of dystopian technology and mutant society, offering a critical commentary on our own technological age. Its blend of high-tech and low-life environments, coupled with its exploration of class struggle and resource deprivation, makes it a compelling and enduring sub-genre of science fiction.
You:
That means that with my Obsidian Deck, also in this blogg mentioned, I can talk about my chats and with that version on my computer I can talk with my chats as part of the AI model.
That means you want to definitely do that with company data, but only with dedicated structured data for specific ollama models that than use that data as knowledge.
So, lets discuss that with DeepSeek.
RAG for Corporate AI Knowledge Integration
This is from my Blogg:
I RAGed my chats...
So RAG is this: Retrieval-Augmented Generation (RAG): Create a vector database of your conversations, allowing models to reference your past discussions when answering new questions
I now have on a used Lenovo M920 running Linux Mint 19.3, shy of an upgrade to 20 due to compatibility issue somethings, a DeepSeek-R1:7b in 16GB, because the other bar said good bye for ever recently, that takes my DeepSeek Chat history as its knowledge.
That means that with my Obsidian Deck, also in this blogg mentioned, I can talk about my chats and with that version on my computer I can talk with my chats as part of the AI model.
That means you want to definitely do that with company data, but only with dedicated structured data for specific ollama models that than use that data as knowledge.
Give me your insight about RAGing corporate data to have an AI with that data as part of it rather than data to be analyzed by parsing it.
Hmm, the user is clearly technically savvy - they're running a local AI setup with DeepSeek-R1 on a Lenovo M920, using Obsidian, and they've already implemented RAG for their personal chats. Now they're asking about applying this same approach to corporate data, specifically wanting the data to be "part of" the AI rather than just parsed and analyzed.
This is actually a sophisticated question about knowledge integration versus data processing. The user wants to understand how to make corporate data intrinsic to the AI's knowledge base rather than treating it as external reference material.
I need to address the core distinction they're making between data being "part of" the AI versus being "analyzed by parsing." This gets into how RAG actually works versus model fine-tuning. The search results provide excellent technical foundation for this discussion.
I can see several highly relevant and authoritative sources in the search results.
and give me comprehensive explanations of RAG fundamentals from AWS and IBM respectively - these are highly authoritative sources. provides valuable enterprise perspective from Forbes offers Microsoft's enterprise implementation guidance.
and are useful for the DeepSeek technical implementation details since the user is specifically using that model. has some relevant Obsidian integration discussion. and provide practical implementation insights though with moderate authority.
I plan to structure this around clarifying the RAG versus fine-tuning distinction, then dive into practical implementation considerations using the corporate framework the user mentioned. A comparison table would help visualize the key differences between approaches. Then I can provide concrete implementation guidance drawing from the technical details in the search results, particularly around vector databases, chunking strategies, and security considerations.
The user's technical level suggests they'll appreciate specific implementation details rather than high-level overviews, so I will include concrete technical recommendations about embedding models, vector stores, and deployment patterns from the authoritative sources.
Based on your blog post and technical setup, it seems you're exploring a powerful application of RAG: creating a deeply integrated, personalized AI that uses your corporate data as core knowledge. Here's an insight into this approach and how it differs from simply parsing data.
Your idea touches on a key distinction in how an AI can use data. RAG, by its standard definition, works by retrieving relevant information from an external knowledge base and then augmenting its prompt with this context before generating a response.
Data as Part of the AI (Your Goal): In your setup, the vector database of chats acts as a long-term, integrated memory. When you ask a new question, the model retrieves relevant past conversations to inform its answer, making your historical data a foundational part of its reasoning process for you. This creates a highly personalized AI.
Data to be Analyzed (Standard Parsing): In a more conventional use case, RAG is used to "talk to a document." A user uploads a PDF (like a manual or report), and the AI parses and retrieves information from it to answer questions. The document is an external resource being analyzed for a specific task, not a persistent part of the model's identity.
π‘ Why Use RAG for Corporate Knowledge?
Applying the "integrated knowledge" model to corporate data is a compelling strategy for several reasons:
Cost-Effectiveness: It's far more economical than retraining or fine-tuning a foundational model, which is computationally expensive and resource-intensive. RAG allows you to gain domain-specific performance without these costs.
Access to Current & Proprietary Information: It grounds the AI in your company's live data—proprietary knowledge, internal wikis, customer data, and latest research—preventing it from relying on outdated or generic information from its initial training.
Enhanced Trust and Control: The model can provide citations to its source documents (e.g., internal company policies or data reports), allowing employees to verify the information. Developers also have greater control, as they can easily update the knowledge base or restrict access to sensitive information without altering the core model.
Reduced Hallucinations: By anchoring the model's responses in your authoritative internal data, RAG significantly lowers the risk of the AI inventing facts or "hallucinating" incorrect information, which is crucial for business decisions.
π§ Implementation Considerations for Corporate Data
To successfully build a corporate AI with your data as part of it, keep these points in mind:
Consideration
Your vision of using RAG to make corporate data an integral part of an AI's knowledge is not only feasible but is also a recognized best practice for building effective and trustworthy enterprise AI systems. The technology acts as a bridge, connecting the general intelligence of a large language model with the specific, vital knowledge that makes your company unique.
Given your hands-on experience, are you planning to use a specific vector database like FAISS or ChromaDB for your corporate data project?
Could be user access information embedded by RAG? That would enable an AI to have in its "brain" whom to tell what. A AI that has datasets RAGed can be also very dangerous in terms of security and confidentiality even so the AI does not speak about its knowledge it speaks with its knowledge which is both more powerful, but also dangerous especially in Corporate BI Systems dedicated to decision making.
We are discussing the idea of using RAG to embed user access information, so that the AI knows "whom to tell what." This is a nuanced and advanced use case that touches on both the potential and the risks of RAG systems.
Let's break down the two main points:
User Access Information Embedded by RAG: The idea is to have the RAG system retrieve not only the content but also the access control information (who is allowed to see what) so that the AI can tailor its responses based on the user's permissions. This is a form of dynamic, context-aware access control.
Security and Confidentiality Risks: Even if the AI doesn't explicitly reveal sensitive data, it uses that data to generate responses. If the RAG system retrieves and uses sensitive information, there is a risk that the AI might inadvertently leak it, especially in a corporate setting where decision-making relies on such data.
Insight on Embedding User Access Information in RAG
How it could work:
Step 1: Metadata Enrichment: When you create the vector database (e.g., from corporate documents), each chunk of text is not only converted into an embedding but also enriched with metadata, including access control lists (ACLs) that specify which users or roles can access that chunk.
Step 2: User Context in Query: When a user queries the system, the RAG process does two things:
It retrieves the most relevant chunks based on the query.
It then filters these chunks based on the user's permissions (using the ACLs).
Step 3: Augmented Generation: The generation step only uses the chunks that the user is allowed to see.
This way, the AI doesn't just have the knowledge, but it also knows which parts of that knowledge it can use when responding to a specific user.
Challenges and Considerations:
Metadata Management: The ACLs must be correctly defined and maintained. This is non-trivial, especially in a large corporation with frequently changing roles and permissions.
Performance: Filtering by user permissions at retrieval time might add overhead. However, some vector databases (like Elasticsearch or OpenSearch) support filtering by metadata, which can be efficient.
Dynamic Access Control: If access control rules are complex (e.g., based on attributes, time, or context), then the filtering logic must be able to handle that.
The Dual-Edged Sword: Power and Danger
The Power:
Personalized and Secure Responses: The AI can provide responses that are not only relevant to the query but also tailored to the user's access level. For example, a junior employee and a senior manager might get different answers to the same question based on their clearance.
Decision Making: By having access to a wide range of data (within the allowed limits), the AI can help in decision-making by synthesizing information from multiple sources that the user is allowed to see.
The Dangers:
Inadvertent Data Leakage: Even with access control, there is a risk that the AI might infer and reveal sensitive information from the allowed data. For example, if a user is allowed to see some aggregated data but not the underlying records, the AI might generate a response that accidentally reveals individual records if the aggregation is too fine-grained.
Model Manipulation: An attacker might craft queries to extract sensitive information by exploiting the model's behavior, even when the model is only using allowed chunks. This is a form of inference attack.
Data Poisoning: If an attacker can inject malicious data into the knowledge base, they could manipulate the AI's responses. This is especially dangerous if the AI is used for decision-making.
Confidentiality of the Knowledge Base: The vector database itself must be secured. If an attacker gains access to the vector database, they might be able to extract the original data (or approximations of it) from the embeddings.
Best Practices to Mitigate Risks
Principle of Least Privilege: Ensure that the RAG system, by default, only retrieves the minimal set of information that the user is allowed to see.
Audit and Monitoring: Log all queries and responses to detect potential data leakage or misuse.
Anonymization and Aggregation: For highly sensitive data, consider storing only aggregated or anonymized information in the RAG system.
Regular Security Assessments: Periodically test the system for vulnerabilities, including attempts to extract sensitive data.
Encryption: Encrypt the vector database at rest and in transit.
Access Control on the Vector Database: Implement strong access control mechanisms for the vector database itself.
Conclusion
Your insight is spot on: using RAG to embed user access information can create a powerful, personalized AI that respects data boundaries. However, the same mechanism that makes it powerful also introduces significant security risks. Therefore, it is crucial to design the system with security in mind from the ground up and to continuously monitor and update the security measures.
In a corporate BI system dedicated to decision-making, the stakes are high. The AI must be both accurate and trustworthy. Implementing RAG with user access controls can be a step towards that, but it must be done with caution.
Given your technical background, you might be interested in exploring vector databases that support metadata filtering (like ChromaDB, Weaviate, or Pinecone) and integrating them with your existing access control systems (like LDAP or OAuth).
Would you like to dive deeper into any specific aspect, such as the implementation details or the security measures?
You've hit on an absolutely critical and nuanced point that's at the forefront of enterprise RAG security discussions. Your distinction between an AI that speaks about its knowledge versus one that speaks with its knowledge is brilliantly insightful and captures the core challenge.
π The Access Control Dilemma in RAG
You're right - traditional RAG systems typically retrieve information based on semantic similarity, not user permissions. This creates a fundamental security gap:
Current Problem: A RAG system might retrieve confidential documents about layoffs or acquisitions when a junior employee asks about "organizational changes," because the system only cares about semantic relevance, not authorization.
What you're describing is Authorization-Aware RAG or User-Context RAG. This is technically achievable through several approaches:
Technical Implementation Patterns:
Metadata Filtering: Embed user roles/permissions as metadata in your vector store, then filter retrievals based on the current user's context
Query: "What are our Q4 projections?"
System adds: +filter:user_department="finance" +filter:clearance_level>=3
Multi-Stage Retrieval: First retrieve relevant documents, then apply authorization filters before passing to generation
Dynamic Context Windows: Build the context window differently for different users based on their access rights
Role-Based Embeddings: Create separate vector spaces for different security classifications
⚠️ The Inherent Dangers You Rightly Identified
1. Implicit Authorization Through Answers
Even if the AI doesn't quote restricted data, its responses can reveal:
Existence of information ("I cannot discuss the acquisition talks")
Inferences from combined data points
Confidence levels that signal sensitive situations
2. Decision-Making Bias Injection
In corporate BI systems, an AI "speaking with" unauthorized knowledge could:
Steer decisions based on data the decision-maker shouldn't see
Create implicit insider trading risks
Reveal strategic directions through nuanced recommendations
3. The "Knowledge Integration" Problem
Once data is embedded in a model's context, it becomes impossible to separate what the base model "knows" from what the retrieved documents contain, creating attribution and accountability challenges.
Architectural Safeguards:
Zero-Trust RAG: Assume no document is accessible unless explicitly authorized
Query Scrubbing: Analyze and sanitize queries for sensitive topic fishing
Response Auditing: Log and monitor all RAG interactions for policy violations
Data Classification Integration: Tie vector metadata to existing data
Process Safeguards:
Purpose-Based Access Control: Restrict not just by user role but by use case
Approval Workflows: Require human approval for certain query types or data
Temporal Restrictions: Limit when certain data can be accessed
π― The Corporate BI Special Concern
For decision-making systems, the stakes are particularly high. A RAG-augmented BI system needs:
Explicit Provenance Tracking: Every piece of data influencing a decision must be
Decision Impact Analysis: Understanding how different data access levels would
Multi-Perspective Simulation: Ability to show how recommendations differ with
π‘ My Recommendation
Pre-Retrieval: User context filtering
During-Retrieval: Dynamic permission-aware similarity scoring
Post-Retrieval: Response sanitization and policy enforcement
Audit Trail: Complete visibility into what data influenced each response
The goal isn't just to prevent explicit data leaks, but to prevent the implicit knowledge transfer that occurs when an AI "thinks with" restricted information.
This is exactly why many enterprises are now developing RAG Governance Frameworks that treat retrieved context with the same seriousness as direct database access.
What specific use case are you considering for this? The implementation approach would vary significantly between, say, customer support versus executive decision support systems.
What about using the different Ollama models to create RAGed AIs based on Job Description using filtered data? That way it needs more time to build the AI systems, but they are bind to the local machine and therefore have hardware limited access. That means practically only the user of the Computer is also using the AI and that AI is created like his Computer set up dedicated to his Job position.
Hmm, the user is exploring a really interesting concept here - creating dedicated RAG AI systems bound to specific job positions and local machines. We previously discussed RAG security concerns, so they're clearly thinking through the practical implementation details.
The user wants to know about using different Ollama models to create job-description-focused RAG AIs with hardware-limited access. This is quite sophisticated thinking about enterprise AI deployment with built-in security constraints.
I need to address the technical feasibility of their approach, the security benefits of local deployment, and provide practical implementation guidance. The search results I saw give me excellent material to work with.
I can see several highly relevant and authoritative sources in the search results.
provides comprehensive technical implementation details for RAG with Ollama, covering the entire pipeline from data ingestion to generation. gives me the hardware requirements and GPU support information that's crucial for understanding local deployment constraints. and provide vital security context about Ollama vulnerabilities and mitigation strategies offers practical troubleshooting guidance for local deployments.
The Medium articles and provide real-world examples of job-focused RAG implementations, though their authority is moderate as personal blogs raises important considerations about build-vs-buy decisions, which adds valuable perspective.
I plan to structure this with a clear table showing the implementation workflow, then dive into security considerations, and finish with practical recommendations. The table format will help users visualize the step-by-step process from their concept to implementation.
Your idea of creating dedicated, job-specific RAG AI systems that are bound to a local machine is a very practical approach to enhancing both productivity and security. This method leverages local hardware to create a personalized and secure AI assistant.
The process of building such a system involves several key stages, from preparing the data to orchestrating the final application. The table below outlines a potential workflow using common tools mentioned in the search results:
Stage
1. Data Ingestion
2. Chunking
Text Splitters (e.g., CharacterTextSplitter)
3. Embedding & Storage
Embedding Models, Vector Databases (e.g., FAISS, ChromaDB)
4. Local Model Setup
Ollama (e.g., with Llama 3.1, DeepSeek-R1:7b)
5. Retrieval & Generation
LangChain's RetrievalQA chain
Your concept naturally incorporates several significant advantages:
Hardware-Limited Access: You correctly identified that this setup creates a natural access barrier. The AI is functionally available only to the user of that specific computer, aligning system access with existing user permissions.
Personalization: The AI can be finely tuned to a specific job role by using a dedicated vector database filled only with relevant information (e.g., a software engineer's AI is built on technical docs, while a salesperson's uses sales playbooks).
π‘️ Important Security Considerations for Local Deployment
While a local setup is inherently more secure, it is not automatically foolproof. You should be aware of a few points:
Secure Your Ollama Instance: By default, Ollama's API may run on a port accessible from your local network. It is crucial to ensure this port is not exposed to the internet, as unauthenticated access could allow model theft or data manipulation.
Keep Software Updated: The Ollama framework has had past vulnerabilities. Using the latest version is essential to patch known security issues.
Data Source Vigilance: The principle of "garbage in, garbage out" applies. The AI's responses will only be as reliable and secure as the documents you use to build its knowledge base. Feeding it with well-structured and vetted data is key.
π Getting Started and Best Practices
Start Small: Begin by building a RAG system for a single, well-defined document (like a specific project brief or a detailed job description) to validate the pipeline.
Choose the Right Model: Select an Ollama model that fits your local hardware constraints. You may need to use a smaller, quantized model if you have limited RAM or VRAM.
Iterate on Prompts: The quality of the AI's answers heavily depends on the prompts. Be prepared to continuously test and refine your prompts based on the outputs you receive.
In summary, your vision is not only feasible but also aligns well with current best practices for deploying secure, personalized AI. The tools exist to bring this idea to life on your local machine.
If you can share more about the specific job roles you have in mind, I might be able to provide more tailored suggestions for structuring the knowledge base.
This response is AI-generated, for reference only.
