Comprehensive Implementation Plan for Enterprise-Scale RAG

Overview: This plan describes a modular Retrieval-Augmented Generation (RAG) system architecture tailored for Enterprise ’s enterprise environment. It outlines how to ingest diverse data sources (PDFs, URLs, databases, code repositories), generate and store embeddings using Ollama and ChromaDB, and serve natural language queries to over 10,000 users/agents. We also detail hardware requirements for supporting large language models (up to ~440B parameters), deployment strategy (on-premises with hybrid cloud capabilities), security/compliance measures, and integration points with Enertprize ’s internal tools (dashboards, CRM, data lake). The architecture is broken into five key layers, ensuring scalability and maintainability:

High-level RAG system flow: documents are ingested and processed into the vector store, then used by the RAG engine (LLM) to answer user queries via the client interface. Each component maps to the layers described below.

1. Data Ingestion Layer (Multi-Source Batch & Real-Time)

Function: The Data Ingestion layer handles acquiring and preprocessing data from various sources in both batch (bulk indexing) and real-time (streaming updates) modes. Its goal is to transform raw data into clean text chunks ready for embedding.

• Supported Sources: Ingest content from unstructured files (PDFs, Word docs, etc.), web pages/URLs, structured databases, and source code repositories (e.g. GitHub). Each source may require a specialized handler: PDFs: Use robust parsers (e.g. PyMuPDF or PDFMiner) to extract text. Handle complex layouts (headers/footers, multi-columns, tables) and if text extraction fails (e.g. scanned documents), fall back to OCR for image-based text . This ensures even scanned or image-heavy PDFs are indexed.
• Web URLs: Crawl and fetch HTML content (respecting robots/privacy rules), then strip boilerplate (scripts, navigation) and extract the main article or page text. Libraries or APIs (e.g. readability or Mercury parser) can help isolate primary content.
• Databases: For structured data (e.g. SQL/NoSQL databases, data lake tables), define transformation pipelines to convert records into a text form. For example, a customer record might be serialized to a descriptive JSON or plain text summary. Ensure relationships (foreign keys, etc.) are preserved via metadata rather than trying to embed entire tables. Alternatively, for certain queries it may be preferable to query the database directly; see integration notes below.
• Source Code (GitHub Repos): Connect to internal GitHub Enterprise APIs or git repositories to retrieve code files. Focus on text-based files (e.g. .py, .java, .sql); skip binaries. Preprocess code by removing comments if irrelevant or separating comments from code if treating differently. Consider splitting code into logical units (functions, classes) or chunks of ~100-200 lines, ensuring each chunk is self-contained. Including file path and repository metadata with each code chunk is critical so that search results can point to the location in the repo.
• Preprocessing & Cleaning: Normalize text by removing non-informative content (boilerplate, HTML tags, duplicate navigation elements). Handle character encoding issues and perform light cleanup (extra whitespace, control characters). Maintain metadata for each document or chunk, such as source type, origin (URL or file path), author, timestamp, etc., as this can later enable filtered queries (e.g. limiting results to a certain data source or date range).
• Document Chunking: Apply intelligent text splitting to large documents to improve retrieval granularity. For example, a lengthy PDF or code file can be divided into chunks of a few hundred words or lines (or based on logical sections like paragraphs, headings, or code blocks). Chunk sizes should be tuned so that each fits the vector model’s input limits and captures a coherent piece of information (often ~500 tokens per chunk is a good starting point). Overlap content between chunks slightly (e.g. overlapping sentences) to preserve context and avoid losing information at boundaries. Each chunk will be treated as a separable unit in the vector store with its own embedding.
• Batch vs. Real-Time Ingestion: For initial rollout, perform a bulk ingestion of existing data (e.g. all relevant PDFs, knowledge base articles, historical code, etc.). Use parallel processing where possible (distributed workers) to handle the volume – for instance, batch process documents in groups to leverage vector DB bulk insert efficiencies. After the initial load, enable continuous ingestion for new or updated content:
• Schedule periodic jobs to scan for new documents or database updates (e.g. nightly sync) and update the index.
• For real-time updates, integrate with event streams or webhooks (e.g. a new file added to a SharePoint or a commit to GitHub triggers an ingestion pipeline run for that item). A message queue (Kafka or AWS SNS/SQS if cloud) can buffer ingestion tasks. Ensure idempotency (don’t duplicate data in the index).
• Provide an admin interface or API to manually trigger re-ingestion of a document (useful if a source is corrected or changed).
• Quality Control: As ingestion runs, implement logging and error handling. Flag documents that fail to parse so they can be reviewed (e.g. an unexpected format or parse failure). It’s important to preserve document structure where relevant – for example, note headings in a Word doc or section titles in a PDF. This can be stored as metadata or even as part of chunk text (“Title: ...”) to give the retrieval layer more context. The ingestion process should also skip or sanitize any sensitive fields as needed (see Security considerations) so that, for instance, PII is not inadvertently embedded unless permitted.

By the end of this layer, all source data is converted into text chunks with associated metadata. This processed corpus will feed into the embedding layer. (In summary, “ingestion is the process of parsing information from source documents so that it can be embedded into a search space for later retrieval,” even when documents have complex formats.)

2. Embedding Generation Layer (Ollama-based Vectorization)

Function: This layer transforms each text chunk into a high-dimensional numeric vector (embedding) that represents the semantic meaning of the text. We leverage Ollama – an open-source framework for running language models locally – to generate these embeddings. The output vectors will be stored in the vector database for similarity search.

• Ollama for Local Embeddings: Ollama provides a convenient way to host and run open-source models (like LLaMA variants) on-premises or in cloud, exposing them via simple APIs. We will deploy an Ollama service with a suitable embedding model. For example, Ollama supports models such as mxbai-embed-large (334M parameters) or all-MiniLM (23M) specifically tuned for embedding generation. These models convert input text into a vector embedding (commonly 384 to 768 dimensions for text). The embedding model choice will balance performance and accuracy:
• For a general text corpus (documents, articles, etc.), a model like mxbai-embed-large or nomic-embed-text can capture semantic meaning well. These are moderate in size and can run quickly on modern hardware.
• For source code data, consider using a model trained on code. Text-only embeddings may not fully capture code syntax and structure. Ideally, integrate a specialized code embedding model (e.g. Salesforce’s CodeT5 or SFR-Embedding-Code) for those inputs, as “text-based retrievers often fail to capture the nuances of code; code retrieval requires models trained specifically on code to understand programming syntax and context”. If maintaining two embedding models is too complex initially, the system can start with one general model for all data and later iterate to add a code-specific embedding pipeline for better accuracy on developer queries.
• Embedding Process: Each cleaned text chunk from the ingestion layer is fed into the Ollama embedding API. The model produces an embedding vector (a list of floating-point numbers). Ollama’s API supports batch processing (either natively or via custom code) which we will use to improve throughput – e.g. send 10–50 chunks at a time for embedding once batch support is available. This significantly speeds up indexing large corpora.
• Scalability: To handle enterprise scale (potentially millions of chunks and continuous updates), the embedding service should be scalable: Concurrency: Deploy multiple instances of the Ollama embedding service across servers or containers. A load balancer can distribute chunks to embed across these instances in parallel. This is important during initial bulk ingestion and will also aid real-time updates if many new pieces of content arrive concurrently. GPU Acceleration: While smaller embedding models can run on CPU, GPU acceleration is preferred for speed at scale. We will equip embedding servers with GPUs to significantly increase throughput. For example, a single 30B parameter model would be too slow on CPU, but smaller 100M–300M models can embed hundreds of texts per second on a GPU. Even with GPUs, multiple processes or threads can be utilized, as the embedding model is relatively lightweight compared to generative models. Resource Allocation: In production, consider dedicating GPU-equipped nodes to the embedding task separate from the main LLM that answers questions. This separation prevents indexing jobs from contending with user query processing resources. Each embedding node might host an Ollama service that loads the embedding model into GPU memory once and reuses it.
• Output Storage: As each batch of embeddings is generated, they are immediately sent to the vector database layer (ChromaDB) for insertion, along with the corresponding chunk IDs, text, and metadata. We precompute embeddings in this way rather than compute on the fly during queries (except for the query itself) – this approach is standard for RAG and ensures query-time latency stays low.
• Consistency: The embedding generation process must use the same model for documents and for queries. We will use the chosen Ollama embedding model both to embed incoming data and later to embed user queries. This consistency ensures that semantic similarity comparisons between query and document vectors are meaningful. (Any model change would require re-embedding the corpus for compatibility, so model updates will be managed carefully and infrequently in production.)

In summary, this layer provides a modular vectorization service. It converts all ingested information into a form that the system can efficiently search by meaning. By using Ollama locally, we avoid external API calls and keep data on-prem, which aligns with Enterprise ’s privacy needs, while still leveraging powerful pre-trained models for embeddings. The result of this layer is a populated vector database of embeddings representing Enterprise ’s knowledge.

3. Vector Database Layer (ChromaDB for Scalable Semantic Search)

Function: The vector database stores all document embeddings and enables fast similarity search over them. We use ChromaDB as the core vector store, allowing efficient retrieval of relevant documents given an input query embedding. This layer serves as the “knowledge base” of the RAG system, supporting semantic lookup across potentially millions of data points.

• Choice of ChromaDB: ChromaDB is an open-source vector database known for its ease of use, integrations, and ability to scale to large datasets. It is purpose-built for AI applications, excelling at storing and querying high-dimensional embeddings. Key reasons for choosing Chroma:
• Scalability: It can handle a large number of embeddings and provides fast approximate nearest neighbor (ANN) search. With proper indexing (HNSW index by default), search remains performant even as data grows, by using efficient graph-based algorithms instead of brute force.
• Persistence: ChromaDB can persist data to disk (using an embedded database like DuckDB or SQLite under the hood), so the vector index can be saved and reloaded on system restarts. This is crucial for enterprise – we won’t need to re-embed everything if the service restarts.
• Integration: It integrates well with Python and ML tooling (and can be called directly from Ollama’s Python API as shown in examples). It also supports metadata storage with each vector, which we will utilize to store document IDs, source info, etc.
• Data Organization: We will create one or more collections in ChromaDB to categorize embeddings: Likely a primary collection (e.g., Enterprise knowledge) containing all embeddings of text content. Optionally, separate collections per data type if we want to isolate certain domains (for example, a codesnippets collection vs. policy_docs collection). This can be useful to apply different search parameters or allow filtering by category. In early phases, a single collection is simpler, using metadata filters to distinguish types. Each entry in the collection includes: the embedding vector, the original document text (or a reference to it), and metadata (document ID, source type, etc.). For instance, Chroma allows storing the raw text alongside the vector, and we will use that to quickly retrieve the content snippet when a search hits.
• Indexing and Performance: As we add embeddings, Chroma will build an ANN index (using HNSW by default). We will tune index parameters for our use case: Use cosine similarity as the distance metric (appropriate for typical text embeddings where cosine similarity captures semantic closeness)
• Set HNSW index parameters (like M – number of neighbors, and ef – search beam width) to balance recall vs. query speed. For initial deployment, defaults can be used, but we will test and adjust based on dataset size and query patterns.
• Batch insertion: During bulk loading, use Chroma’s batch add methods to insert vectors in large groups. This minimizes indexing overhead and speeds up initial population.
• Monitor Chroma’s resource usage. It can be memory-intensive for very large indexes; we may configure it to use disk-backed indexes if needed. Given hardware planning (detailed later), we will provision ample RAM to hold indexes for fast search, while relying on disk persistence for durability.
• Retrieval Operation: When a user query comes in (described in the next section), the system will:

• Embed the query text into a vector (using the same Ollama embedding model).
• Query ChromaDB for the nearest neighbor vectors to that query vector. For example, we might request the top k=5 or k=10 most similar embeddings
• . Chroma returns the IDs, metadata, and similarity scores of the top matches.
• Retrieve the stored text for those top matching chunks (so we have the actual content to feed into the LLM). Because we stored either the full chunk text or a reference, we can get the content easily in this step
• Scalability & High Availability: For enterprise use, we must ensure the vector DB layer can handle high query volume and is robust: Throughput: ChromaDB’s search is very fast (sub-second) even for large corpora, but 10,000 users could generate many simultaneous queries. To scale reads, we can run multiple replicas of ChromaDB behind a read load balancer. Each replica would have a copy of the embedding data (since our data isn’t rapidly changing in ways that require strong consistency, read replicas would work for most queries). We might designate one node as primary for writes (ingestion updates) and replicate the index to read-secondaries periodically or in near-real-time if supported. Sharding: If the dataset grows to truly massive (e.g. billions of embeddings), we may consider sharding the vector index by some key (perhaps by document domain or time range). Each shard would handle a subset of data. However, initially we expect on the order of millions of embeddings at most, which Chroma can handle on a single node with proper hardware. Hardware: Run ChromaDB on a high-memory node with fast disk (NVMe SSD). This allows the index to reside largely in memory for speed, with SSD for persistence. Encryption at rest can be enabled on the underlying storage for security (more in Security section). Backup: Regularly back up the ChromaDB persistent store (e.g. daily dumps or using built-in snapshot if available) so we can recover quickly in case of corruption or human error. Because the source of truth for data is ultimately the original sources, we could re-generate the index if needed, but backups will save significant time.
• Semantic Search Advantage: This layer is what makes the system “smart.” Rather than keyword search, the vector similarity means the system can find relevant information even if the query phrasing doesn’t match the document’s keywords. For example, “automobile safety policy” will match a chunk about “vehicle security procedures” because the embeddings capture semantic similarity (knowing that “automobile” ≈ “vehicle”). This capability addresses the context and synonymy issues that traditional search struggles with.

In essence, ChromaDB is our “knowledge index”, turning Enterprise ’s documents and data into a searchable semantic space. It will store the numeric fingerprints of all content and quickly yield relevant pieces to satisfy user queries.

4. Retrieval & Query Layer (Orchestration of Query Understanding, Vector Search, and LLM Response)

Function: This layer is responsible for taking a user’s natural language query, finding relevant information via the vector database, and producing a final answer using a generative model. It orchestrates the RAG process: Query → Retrieve → Generate. It also integrates with user-facing applications or APIs as needed.

• Natural Language Query Handling: Users (or applications) will send questions or prompts in plain language to the RAG system (e.g. “Which vendors were top performers last quarter?” or “Explain the code deployment process in our CI pipeline.”). The query subsystem will: Embed the Query: Use the same embedding model as above (via the Ollama embedding service) to compute the query’s embedding vector. This is typically very fast (a single short query embedding).
• Vector Retrieval: Submit this embedding to ChromaDB to retrieve top-matching content chunks (as described). For instance, get the top 5 chunks that are most semantically similar to the query. These chunks might be from different documents or sources but all related to the query intent.
• (Optional) Reranked or Filter: We can apply business rules or a secondary scoring at this stage if needed. For example, if a query includes a date filter (“last quarter”), we could filter out documents not in that date range using metadata. Or use a lightweight re-ranking model to refine the ordering of retrieved chunks for relevance. Initially, a simple similarity sort is sufficient (Chroma’s cosine similarity scores). We will ensure that the retrieved contexts have a diversity of sources if applicable (to avoid, say, all 5 chunks coming from one large document if the user likely wants a broader answer).
• Contextual Answer Generation: With the relevant context chunks in hand, the system invokes a Generative LLM to produce the final answer. This is the “augmented generation” step:

• We will deploy a Large Language Model (such as LLaMA 2 or another model up to ~440B parameters as required) to serve as the answering engine. This model will be hosted on-prem (likely via Ollama or another serving stack that can handle large models).
• The query and retrieved texts are combined into a single prompt for the LLM. A proven prompt format is: “You are an expert assistant. Using the information provided, answer the question.\n\nContext:\n[Top retrieved chunk 1]\n[Chunk 2]...\n\nQuestion: [user query]\nAnswer:”. The model is thereby instructed to ground its answer on the given context. For example, an actual prompt might look like: “Using this data: <snippet about vendor performance> <snippet about financial results>. Respond to the prompt: Which vendors were top performers last quarter?”. This format was demonstrated with Llama-2 in an Ollama example, where the model produced a factually grounded answer using the provided data.
• The LLM generates an answer, ideally phrased in a helpful manner, and because we provided supporting data, it will quote or incorporate facts from that data rather than relying purely on its internal training (this greatly reduces hallucination and increases factual accuracy
• Model Selection: For generation, a model up to 440B parameters is anticipated for the highest quality. Initially, a 70B parameter LLaMA-2 or similar model fine-tuned for instruction following can be used, as it offers strong performance with feasible infrastructure. The plan, however, accounts for scaling to even larger models (e.g. a hypothetical 200B–440B model) as needed for improved answer quality or specific domain expertise. The serving infrastructure (detailed in Hardware section) will ensure such a model can run with acceptable latency. Using Ollama, we could load a local LLaMA or other model and even serve it via API calls similarly to the embedding model (Ollama allows serving LLMs as well). If needed for model parallelism, we might integrate HuggingFace Transformers with GPU distribution or use frameworks like DeepSpeed or NVIDIA Triton for optimized inference on multi-GPU setups.
• Answer Composition: The raw output from the LLM may be post-processed slightly:

• Ensure it does not contain the internal prompt or any artifacts (with careful prompt design this is usually fine).
• Optionally, append citations or source attributions to the answer. Since our system knows which documents were retrieved, we can map the content back to source titles/IDs. For example, the answer could include a reference like “[Source: Q3 Financial Report]” if we wish. This can greatly increase user trust. The implementation can simply identify which chunk contributed most to the answer and mention its document title. (The user interface can also handle showing sources separately, see Frontend section.)
• If the LLM output is too verbose or not directly addressing the question, we can refine the prompt or use few-shot examples to guide tone and conciseness. We will test with sample queries from Enertprize ’s domain to fine-tune the prompting for optimal responses.

• Multi-turn Conversations: Although the initial scope is single-question queries, the system can be extended to support multi-turn dialogues (where it remembers previous questions/answers). This would require maintaining conversation state and possibly appending the dialogue history as additional context for each new query. The architecture is flexible to allow this (the client interface can manage context tracking), but we will focus first on single-turn Q&A which covers many use cases (searching knowledge base, etc.).
• Agents vs. Users: The prompt mentions 10,000 users and agents. In practice, “agents” could refer to automated processes (robot users) or AI agents that also query the system. The retrieval and query layer will be exposed via an API endpoint (e.g. a RESTful or gRPC service) so that not only human users through the UI, but also other services or bots can programmatically send queries and get answers. For example, an “IT support chatbot” agent could hit this API to get answers for a customer query, or a scheduled job could query for information for reporting. The system will handle authentication/authorization for such uses (see Security).
• Scaling Query Throughput: To serve thousands of users, this layer will be deployed in a stateless, scalable manner:

• Multiple instances of the query service (the orchestration logic + connection to the LLM) will run behind a load balancer. Each instance can handle many requests but is limited by LLM inference speed. By running N instances, we can handle N times the load.
• The LLM inference is the bottleneck; even with a powerful model, generating an answer might take 1-5 seconds. We mitigate this by horizontal scaling (more model replicas) and possibly dynamic batching (grouping multiple small queries into one forward-pass if using an inference server that supports it). Caching is another optimization: if many users ask identical questions, we can cache the answer for a short period. A simple in-memory cache keyed by the exact query (or a normalized form) could return answers instantly for repeats. This has huge benefits for repeated queries in a large organization (the system “remembers if someone asked the same thing before”, reusing that result to respond faster

To support large models, we may utilize model parallelism (sharding the model across GPUs) which gives one powerful service instance, but for scaling, we will likely run multiple such model instances in parallel. For example, two 440B model servers might serve queries in round-robin. The infrastructure section will detail the hardware for this.

• Error Handling: If the system cannot find any relevant context (e.g. the vector search returns very low similarity scores or no results above a threshold), the generation step can be adjusted to avoid hallucination. We can program the logic such that if no good context is found, the LLM is either not invoked (return a “no information found” message), or it’s invoked with a prompt that explicitly says “If you don’t find relevant info, admit it.” This prevents confident-sounding but baseless answers. Maintaining user trust is key, so we prefer to respond with, “I’m sorry, I don’t have information on that,” rather than a guess, when our knowledge base lacks the answer.

Overall, this Retrieval & Query orchestration layer is the “brain” that manages the end-to-end question answering process – from understanding the question (via embeddings) to gathering knowledge (via ChromaDB) to producing a coherent, factual answer (via the LLM). It will be implemented as a microservice (e.g. a FastAPI or Node.js service) that the frontend or other apps can call. The logic will draw on frameworks like LangChain or LlamaIndex if helpful, since they offer abstractions for chaining retrieval and LLM calls; however, we have a clear custom design so we may implement it directly for transparency and control. By ensuring modularity here, we can swap in improved models or adjust retrieval techniques (for example, incorporate hybrid search with keywords + vectors later, as an advanced improvement to catch edge cases).

5. Frontend Interface (User Interaction and Integration Layer)

Function: The frontend provides a user-friendly interface for Enterprise  staff (and possibly systems) to interact with the RAG system. It’s essentially the client interface through which queries are input and answers (with context) are output. This layer focuses on usability, accessibility, and integration into Enterprise ’s existing tools.

• User Interface: We will create a web-based application that allows users to ask questions and view answers. Key considerations for the UI: Simplicity: The interface will have a clear input box for questions (supporting multi-sentence queries) and an area to display answers. The design should resemble a modern chatbot or search assistant, which users are familiar with. Answer Presentation: Answers will be shown in a readable format, ideally with the option to expand and see the source snippets. For example, after the answer text, we might list the titles of documents used (or hyperlink the answer text to the source). This transparency helps users trust and verify the information. (In internal testing, we can include citation numbers mapping to documents, but in production the UI can show full source details in a sidebar or on demand.) Context & Follow-up: If supporting follow-up questions, the UI would show the conversation history. Even for single-turn, it may be useful to show the user the “context” that was used to answer (some systems show the chunks). However, to avoid information overload, we might keep that optional (like a “show sources” button). Interactivity: Provide the ability to refine queries. If the answer isn’t what the user needed, they can modify and ask again. The UI can also allow users to give feedback (thumbs up/down on an answer). This feedback loop can be logged for continuous improvement of the system (e.g. noticing if certain queries consistently fail or get downvotes, indicating missing data or model issues).
• Integration with Existing Dashboards: Rather than a standalone tool, we plan to integrate the RAG interface into Enertprize ’s internal portal and tools: Dashboards: For example, within an internal KPI dashboard web app, a sidebar could host the RAG Q&A interface. This allows analysts to ask questions about the data they’re viewing in real-time. The integration can be done via an iframe or a component that calls the RAG API. Because the RAG system can interface via REST, embedding a mini-chatbot that sends queries to the RAG backend is feasible in any web context. CRM Systems: In Enertprize ’s CRM (customer relationship management) software used by support or sales teams, we can embed a “Assistant” panel. This panel would let the agent query information about products, policies, or even specific customer data. For instance, an agent could select a customer and ask, “What recent issues has this customer reported?” – the RAG system could retrieve relevant support tickets or knowledge base articles. Integration here might require context passing (e.g. the CRM could pass the customer ID or relevant info along with the query so that the RAG system can filter or incorporate it). Initially, we focus on general knowledge Q&A, but designing the interface to allow contextual queries (via metadata filters) is a future improvement. Data Lake/BI Tools: Data scientists or analysts might use RAG via a notebook or BI tool plugin to quickly get documentation answers. For example, a JupyterLab plugin could allow querying the RAG system for “what does field X in dataset Y mean?” and get the answer from data catalog documentation.
• API Access: In addition to human-facing UI, the frontend layer includes the API endpoints that other applications or scripts will call. We will likely implement a REST API with endpoints such as: POST /query – accepts a JSON with query text (and possibly user context) and returns the answer and sources. GET /health – health check for monitoring. Optionally, endpoints for ingestion triggers or admin actions (could be separate service).

These APIs will use authentication (e.g. API tokens or Kerberos/SSO integration) to ensure only authorized internal apps call them. The API makes the RAG functionality re-usable anywhere in Enertprize ’s ecosystem. For instance, an internal Slack bot could be built that takes a user’s question in Slack, calls the RAG API, and posts the answer back in the channel – extending the reach of the system to where employees already communicate.

• User Management: Leverage Enterprise ’s Single Sign-On (SSO) and identity management so that users logging into the RAG UI are authenticated against corporate credentials. This gives us the ability to personalize or restrict content if needed. For example, if certain data is confidential to a department, we could use the user’s group membership (via JWT claims or an API call) to filter results. Initially, we might not enforce many differential access rules, but the architecture will support it (we have metadata in vector DB for data classification and user roles from SSO, so we can intersect them as needed).
• UI Technology: Use a modern web framework (React or Angular) to build the interface, ensuring it is responsive and works in common browsers. The interface will call the backend API (likely via HTTPS) and handle streaming responses if we enable that (streaming token-by-token from the model for long answers, so the user sees partial answer coming in like ChatGPT – this can improve experience for very large answers).
• Logging and Analytics: The frontend can also send user interaction data (like queries asked, feedback given) to a logging service. This helps in monitoring usage and identifying areas to improve. We will implement privacy safeguards (e.g. hashing or not logging full queries if they might contain sensitive info – see Security considerations). Still, understanding what kinds of questions are frequent will guide future expansions of the knowledge base.

In summary, the Frontend Interface ensures the RAG system is accessible and useful to end-users. By embedding it into Enterprise ’s existing tools and providing a seamless Q&A experience, we drive adoption. A well-designed UI coupled with integration hooks (API, context passing, etc.) will allow the RAG system to become a natural part of daily workflows – whether it’s an employee quickly searching for a policy detail, or an automated agent using it to assist a customer. The focus is on an intuitive, fast, and secure interface that abstracts the complex backend into a simple “Ask and Answer” interaction.

Hardware and Infrastructure Requirements (440B-Model Support)

Deploying an enterprise RAG system with potential 440B parameter models demands careful planning of compute, memory, storage, and network resources. Below we outline the hardware requirements and recommendations for each layer and discuss how to scale from proof-of-concept to production. We assume on-premises deployment with enterprise-grade servers (while allowing for cloud augmentation if needed). The key hardware considerations are for two main workloads: embedding/vector search (which are moderate compute, high memory tasks) and LLM inference (which is extremely compute-intensive, especially for a 440B model).

1. Compute (CPU & GPU):

• CPU Requirements: The ingestion layer (parsing files, running OCR, etc.) and the vector DB primarily use CPU. We recommend high-core-count CPUs (e.g. 32-core Intel Xeon or AMD EPYC per server) to allow parallel processing of documents and concurrent query handling. Ingestion tasks like PDF parsing or HTML processing can be distributed across threads or nodes. For example, an ingestion node with 16–32 cores can handle multiple file parses in parallel. The vector database (ChromaDB) benefits from CPU for managing the index and computing distances. A machine with at least 16 cores and 256GB of RAM is suggested for the Chroma service in production, so it can handle many simultaneous similarity searches and background index maintenance. The query orchestrator and API servers are not heavy compute users beyond what the LLM and DB require; typical application servers (8–16 cores) are sufficient for those, unless they share resources with the LLM runtime. For a 440B model, if we ever attempted CPU-only inference (not really feasible for real-time), it would require an enormous number of CPU cores (and would be very slow). Thus, GPU is mandatory for LLM of that size. CPUs will complement by feeding data to GPUs and running non-ML logic.
• GPU Requirements: Embedding generation: Models like 100M–300M can run on a single GPU easily (even a 16GB GPU could suffice). To accelerate embedding of large volumes, we suggest NVIDIA A100 or H100 GPUs. A single A100 40GB can embed hundreds of texts per second for models like all-MiniLM. One GPU-backed server for embeddings could handle the load, but for redundancy and scaling, 2+ is better. These don’t need to be the latest GPUs; even an NVIDIA T4 or A10 could serve for embeddings if needed, but since we will have A100/H100 for the LLM, those same servers can double for embedding tasks when not fully occupied by generation. LLM Inference: Large models up to 440B parameters require significant GPU memory and compute: Memory requirements: A rough rule is about 2 GB of GPU VRAM per 1B model parameters for inference in half-precision. That implies ~880 GB of VRAM for a 440B model in fp16. No single GPU has this (current high-end is 80 GB), so multi-GPU model parallelism is required. For instance, splitting across 11 × 80GB GPUs would provide 880GB total, theoretically enough. We can also employ memory-optimized approaches: using 8-bit quantization roughly halves memory needs (~440 GB for 440B), and 4-bit quantization quarters it (~220 GB). With 4-bit, ~220 GB could fit on 3 × 80GB GPUs (240GB total) – though with little headroom. In practice, we might use 4–8 GPUs to host a 440B model in 4-bit mode.
• Compute and distribution: To run the model efficiently, the GPUs should be in a single server or connected with high-speed interconnect (NVLink/NVSwitch or InfiniBand). For example, an 8×A100 80GB server (like an NVIDIA DGX node) has NVSwitch connecting all GPUs, making it easier to load a large model across them. If the model still doesn’t fit 8×80GB (which is 640GB total, not enough for 440B in fp16), we could distribute across two such servers (16 GPUs total). Note: Running multi-node requires InfiniBand networking for fast GPU-to-GPU communication. As a reference, even a 70B model typically needs at least 2 GPUs in parallel to fit into memory, so a 440B might need on the order of 6–8 GPUs minimum with optimized memory use.
• Throughput vs. Latency: Large models are slower. To support user queries with reasonable latency (say 2–5 seconds per answer), we might not use the full 440B for every single query – it might be reserved for when high accuracy is paramount. In some cases, we might use a somewhat smaller model (e.g. 70B or 175B) for general use and bring out the 440B for specific complex queries or off-line analysis. Hardware planning will consider the worst-case of using the largest model live.
• GPU Type: For consistency and future-proofing, NVIDIA A100 80GB GPUs (or newer H100 80GB) are recommended. H100s offer faster compute and memory bandwidth (and support FP8 which could further reduce memory). They also allow larger models in INT8 with minimal loss. If budget allows, equipping servers with H100s will ensure we can load and infer the model as efficiently as possible. If using older generations (V100 32GB, etc.), far more GPUs would be needed which becomes inefficient.
• Scaling Out: To handle many simultaneous queries, we will run multiple inference servers: We can have, say, two instances of the LLM loaded (each on its own set of GPUs), so that two heavy queries can be served concurrently. With a bit smaller model or if using batching, one instance could also serve multiple queries interleaved, but given the 10k user scenario, multiple instances are safer. For example, production might have 2 nodes each with 8×80GB GPUs. Each node loads the full model (with tensor parallel across its 8 GPUs). A load balancer routes new queries to whichever node is free. This way, we double the throughput. We could scale further to 3–4 nodes if needed (limited by cost). This essentially forms a GPU cluster dedicated to the RAG LLM. Another approach is to maintain a pool of different models for different tasks (a smaller model for quick FAQs vs. the largest for detailed analysis). However, to keep the system straightforward, we’ll likely stick to one primary model and scale that horizontally.

2. Memory (RAM):

• System RAM is important especially for the vector DB and for hosting models before they’re fully loaded into GPU.
• For the ChromaDB server, 256 GB or more ensures the entire vector index (which could be tens of gigabytes for millions of embeddings) can reside in memory for fast access. It also leaves room for OS cache of SSD data.
• The LLM servers should have a substantial amount of RAM as well – when loading a 440B model, the model weights (hundreds of GB) might initially be read into CPU memory or at least streamed through it. We don’t want to be limited by RAM when assembling the model shards for GPUs. 512 GB RAM on the LLM nodes is a good target. This also allows us to run multiple smaller processes or allocate space for caching frequent embeddings, etc. Additionally, if we ever use CPU offloading for less-used model layers (some frameworks allow paging some layers to CPU RAM), having abundant memory helps.
• Ingestion nodes can function well with 64–128 GB RAM; parsing documents is not extremely memory-heavy per process, but if many are in flight or if dealing with large documents (hundreds of pages PDFs), memory helps. Also, any in-memory queues or batch storage benefit from having headroom.

3. Storage:

• Vector Store & Data Storage: Anticipate needing fast, large storage for: The persistent embedding store (ChromaDB files). For a rough estimate: if we have 10 million embeddings of dimension 768, that’s ~30KB per embedding (float32) – about 300 GB total, plus overhead. Even with compression or smaller count, we easily talk a few hundred GB for vectors. We plan for a few TB of storage for the vector database to allow growth (and possibly storing multiple indexes or backups). Using NVMe SSDs on the vector DB server will significantly improve performance for searches that hit disk. We propose at least 2 TB NVMe on that server, configured in a RAID1 or some redundancy if possible for reliability. Ingestion also requires storage for raw and processed data: we will have a repository of the source documents (or at least pointers). This might reside on a network storage or the data lake itself. We should have local disk space to stage files during processing. A modest amount (say 1–2 TB) on ingestion servers is sufficient for temporary files and logs. Data Lake Integration: The system will read from the data lake (which might be an HDFS, S3, or other large store). We aren’t duplicating the entire data lake, just indexing content. However, we might cache certain datasets. If, for example, we ingest a large table from a database by turning it into text, we might store that text locally. So allocate space accordingly if large exports are expected. For code repositories, storing a local clone of repos is useful (for faster access and to diff changes). This is usually on the order of a few GB, not a big issue.
• Model Checkpoints: The LLM model files themselves are huge: A 440B model in 16-bit could be ~880 GB file(s). Even in 8-bit it’s ~440 GB. We need a place to store these model weights (likely as multiple files for each layer or shard). We will provision a high-speed shared storage (or ensure each node has enough local disk) to hold model data. One approach: use an NFS or network filesystem so that we maintain one copy of the model, and both LLM servers can load from it. This storage should be extremely fast (to not bottleneck loading). Alternatively, maintain a separate copy on each LLM server’s NVMe for maximum speed (at the cost of duplication). Given 2–3 nodes, duplication is manageable. We recommend each LLM server have at least 1–2 TB of NVMe dedicated to model and runtime storage. This covers the largest model and some space for intermediate files. NVMe ensures that even memory-mapped model parts or swap (if any) is quick. Regular backup storage for models and data (e.g. a NAS or tape backup) should also be accounted for, but that can be part of IT’s routine backups of these directories.
• Storage Summary: In production, a safe estimate is to have ~10 TB of total storage allocated to the RAG system: spread as 2TB NVMe on each critical node and some network storage. This leaves ample room for future expansion (e.g. adding more data sources). We will monitor disk usage as more data is ingested.

4. Networking:

• Intra-Cluster Network: High bandwidth and low latency networking is crucial, especially for model parallelism and data transfer between components: Between the LLM GPUs (if multi-node): InfiniBand (100 Gbps or higher) is recommended to connect GPU servers. This allows the GPUs on different servers to communicate quickly as if they were in one system. For example, splitting a model across two 8-GPU servers would require passing tensors over IB each forward pass – a slow network would kill performance. A 100 Gbit IB or NVIDIA NVLink Bridge between nodes will be needed for such cases. Within a single server, NVSwitch connects GPUs (e.g. in an 8-GPU HGX baseboard) – we will use servers that have that for intra-node GPU comms. General cluster networking (for API calls, DB queries, etc.): 10 GbE minimum, but preferably 25 or 40 Gb Ethernet for future-proofing. The vector DB queries, embedding RPC calls, etc., involve sending vectors of a few KB and receiving perhaps hundreds of KB of text. 10GbE can handle a lot of that traffic, but with thousands of concurrent requests, 25GbE ensures no saturation. We plan to connect all servers to a high-speed switch fabric. Latency on Ethernet for these microservices is fine (sub-millisecond). Separate networks/VLANs may be used for different traffic: one for HPC (GPU to GPU), one for service calls. This isolation can improve performance and security (e.g. only the GPU nodes are on the IB network).
• Internet/Cloud Access: Although on-prem, certain operations require outgoing internet access: Data ingestion from web URLs needs egress to the public internet. We will route these through Enertprize ’s secure web proxy or firewall, with appropriate filtering. This access should be restricted to the ingestion service. If the system is allowed to retrieve the latest from external sources (like pulling open-source model updates or Python packages), those servers need outbound connectivity (at least temporarily for setup). Cloud integration: if in the future we burst to cloud GPUs or call a cloud service, a secure VPN or direct connect to that cloud environment will be needed. For now, our hybrid model might simply use cloud for backup or non-prod environments. Nonetheless, ensure networking is configured to allow the needed connections (for example, allow the RAG network to reach Azure/AWS if using any managed service). All such traffic will be encrypted.
• Resiliency: Use redundant network links where possible (bonded NICs) to avoid single points of failure. Also ensure the data center network has low latency between the servers hosting RAG components (ideally, they sit in the same rack or L2 domain).

5. Environment & Other Considerations:

• Cluster Orchestration: We will likely use Kubernetes or a similar orchestrator to manage these services. Containerizing Ollama, ChromaDB, the API, etc., makes it easier to deploy and scale. However, the LLM serving might run directly on the host OS for performance, or via specialized frameworks (we can still manage it with Kubernetes using device plugins for GPUs). Kubernetes allows us to define node pools – one pool of GPU nodes, one of CPU nodes – aligning with hardware. This approach also makes it easier to port components to cloud if needed. “Using containerization technologies like Docker and Kubernetes enables consistent and portable deployment across environments”, which is valuable for dev/staging/prod parity.
• Power & Cooling: High-end GPU servers (8×A100) will draw a lot of power (e.g. 3–5 kW each) and generate heat. Enertprize ’s data center must be prepared for this load. Ensure adequate cooling and power redundancy (UPS, generators) for these critical nodes. If on-prem resources are constrained, we might house some of this in a co-location or utilize cloud for the heaviest parts, but the plan assumes on-prem readiness.
• Hardware for Environments: We outline below a scaling plan from POC to production. This helps request appropriate resources from infrastructure at each stage.

Hardware Specifications by Deployment Scale

Deployment Stage

Compute Nodes (GPU/LLM)

Supporting Nodes (CPU)

Storage

Networking

Proof-of-Concept Development / Pilot)

1× GPU node: e.g. 1× NVIDIA A100 (40GB), 16 CPU cores, 128GB RAM. This node can host a smaller LLM (7–13B) and the embedding service together.

1× general node: 8 CPU cores, 32GB RAM – can run ChromaDB and ingestion on small scale (or these could even co-reside on the GPU node if needed for pilot).

~1 TB NVMe on GPU node for model + data; Regular SSD on CPU node (500GB).

10 Gb Ethernet for internal traffic; Internet access for ingestion (through corp firewall).

Staging QA / Pre-Prod)

1× GPU node: 4× A100 80GB (or 4 smaller GPUs), 32 CPU cores, 256GB RAM. This can host a mid-size model (70B) across 4 GPUs, approximating production conditions.

1× vector DB node: 16 CPU cores, 64GB RAM for ChromaDB. 1× ingestion node: 8 cores, 32GB RAM for parallel ingestion jobs. (Alternatively, one beefier CPU server can handle both roles in staging.)

~5 TB storage: e.g. 2TB NVMe on GPU node (model + scratch), 2TB NVMe on DB node, plus 1TB shared NAS for data files.

25 Gb Ethernet for faster sync and testing multi-user load; If multi-node model: InfiniBand or NVLink between GPUs (in one node, NVLink connects 4 GPUs).

Production Full Scale Deployment)

2× GPU nodes: each with 8× A100 80GB (or H100), dual 32-core CPUs, 512GB RAM. 16 GPUs total across cluster, capable of hosting one 440B model instance per node, or splitting a larger model over both.) This supports running 1–2 large LLM instances in parallel for high throughput.

1× vector DB node: 32 cores, 256GB RAM – dedicated ChromaDB server. 2× ingestion & API nodes: each 16 cores, 64GB RAM – handle continuous ingestion, and also run the query orchestrator/API services. These can be load-balanced for ingestion tasks and for API calls. (Additional smaller nodes can be added as needed for redundancy).

~10 TB total: e.g. each GPU node with 2TB NVMe (for local model files and temp data), the DB node with 4TB NVMe (for embeddings index), and 2TB allocated on shared storage for document repository, logs, and backups. All sensitive data volumes encrypted.

100 Gbps InfiniBand linking GPU nodes (for multi-node model or future expansion); 25–40 Gb Ethernet for general traffic between services and user interface; Dedicated connectivity to cloud (VPN/DirectConnect) for any hybrid needs; Strict network segmentation for security (model nodes in secure enclave).

Table: Hardware recommendations by environment. The production setup uses multiple specialized nodes to ensure scalability and fault tolerance, while POC can consolidate on minimal hardware. Production GPUs assume ~440B model usage; if a smaller model is used, fewer GPUs could suffice, but we size for worst-case to be safe.

Notes:

• The hardware specs above ensure that even a very large model (hundreds of billions of params) can be loaded and served. For instance, Production’s 16×80GB GPUs provide 1280GB GPU memory in total; with optimized use (4-bit quantization), this could handle ~1 trillion parameters if ever needed. This is forward-looking and gives headroom as model architectures evolve.
• The CPU and memory allocations in production allow the system to handle high ingestion rates (e.g. adding thousands of documents per day) and high query concurrency (Vector DB and API nodes can scale horizontally if needed by adding more instances behind load balancers).
• The networking in production is designed to avoid any bottleneck: even if one component needs to fetch large context (say 5 chunks of 2KB each = 10KB) for 100 simultaneous queries (1MB total), 25Gb can handle that easily. The InfiniBand is mostly for ensuring that if the model is distributed, the latency in communication doesn’t hurt inference speed significantly.

Deployment Architecture (On-Premises with Hybrid Cloud Capability)

The RAG system will be primarily deployed on-premises in Enterprise ’s data center to maintain control over data and compliance. However, we design it with a hybrid cloud approach in mind: this means on-prem will handle all core operations, but we retain the flexibility to leverage cloud resources for certain tasks or future scaling if necessary. The deployment architecture is as follows:

• On-Premises Cluster: All core components (Ingestion services, Ollama embedding service, ChromaDB, LLM servers, API/frontend) will run on a dedicated on-prem cluster. This cluster will be managed via Kubernetes for containerized components, and possibly a Kubernetes setup for the large LLM jobs if not containerized. By deploying on Enterprise ’s own infrastructure, we ensure complete control over hardware, software, and the security environment

, which is crucial given sensitive internal data. We avoid sending internal data to third-party clouds during normal operations, alleviating many privacy concerns.

• Network Topology: The on-prem deployment will sit behind Enterprise ’s firewall. Users will access the frontend via the corporate network or VPN. The servers themselves will be on a secured VLAN. Only specific ports are opened (e.g. HTTPS 443 for the web UI/API, and maybe SSH or k8s control ports internally). For any cloud communication, we will use secure channels: If ingesting internet data (web scraping), the ingestion node accesses the web through a proxy that audits traffic. If connecting to cloud for model downloads or backups, use encrypted connections (TLS) and possibly a static egress IP so that cloud side can whitelist Enterprise ’s IP. We can also integrate with Enertprize ’s cloud account if needed. For example, if we want to use an external GPU service for overflow, the architecture could route certain requests to a cloud function. In the current plan, this is optional and would be carefully controlled (e.g. only non-sensitive queries, if any, would ever go to an external LLM API). For now, the on-prem hardware is sized to avoid needing external inference.
• Hybrid Cloud Use Cases: While on-prem will handle steady-state, hybrid could be used in scenarios like: Burst Capacity: If demand spikes beyond on-prem capacity, and if allowed by policy, spin up cloud-based LLM instances temporarily. The system could have a toggle to route to these when needed. (This requires that no highly confidential data is in those queries or that the cloud environment is within Enertprize ’s controlled tenancy – perhaps using a cloud region with strict access.) Non-Prod Environments in Cloud: We might deploy the dev or staging environment on cloud VMs to save on-prem resources, especially if the data used is anonymized or sample. This makes it convenient for developers to iterate without impacting on-prem cluster. Model Training/Fine-tuning: If we decide to fine-tune models or run heavy training jobs (outside the immediate RAG scope), cloud GPU clusters might be used due to scalability and then resulting models deployed on-prem.

Essentially, “a hybrid deployment combines cloud and edge (on-prem) solutions to balance performance, cost, and scalability”

. We design the system so it can run fully isolated on-prem, but have network and deployment flexibility to incorporate cloud if needed. This ensures Enterprise  can leverage the best of both worlds: on-prem for security and low latency to internal data sources, cloud for elasticity and specialized services.

• Security in Deployment: All on-prem servers will adhere to Enertprize  IT security standards (hardened OS, regular patching, monitoring agents installed). The Kubernetes cluster (if used) will be restricted to internal access. Role-based access will control who can deploy or view logs. The LLM servers, because they host large models, will be in a secure segment with limited access – only the necessary services talk to them (the orchestrator service via APIs or RPC). This “zero trust” approach inside the network means even within on-prem, each component authenticates with others (for example, the query service uses an API key or service account to query the vector DB).
• Continuous Integration/Deployment (CI/CD): We will set up CI/CD pipelines for the RAG components. This likely involves a Git repository for code (ingestion scripts, orchestrator, UI) and a container registry. On updates, our pipeline can build new Docker images and deploy to a staging environment, then to production. The infrastructure team should be prepared to support this with either an existing CI/CD tool or allow our team to use one. This ensures we can roll out improvements or patches with minimal downtime.
• Monitoring & Logging: Deploy monitoring agents on all servers (Prometheus/Grafana for Kubernetes, or enterprise monitoring solutions) to watch metrics: CPU/GPU utilization, memory, query latency, etc. Particularly, we’ll monitor GPU temperatures and utilization to ensure the expensive hardware is used efficiently and not overheating. Logging will be centralized (e.g. ELK stack) so that all query logs, errors, etc., are collected. This aids in both debugging and security auditing.
• High Availability & Failover: The architecture avoids single points of failure: We run multiple instances of critical services (at least 2 API pods, 2 ingestion workers, etc.). If one fails, the others continue serving. The vector DB could be a single node initially; to avoid downtime, we plan backups and could set up a warm standby node that can be switched to if needed. Future versions of Chroma may support clustering – we will keep an eye on that and enable replication when possible. LLM nodes: we have more than one, so if one goes down, the system loses some capacity but continues operating on the other. We will also maintain the ability to reload the model on a replacement server if a hardware failure occurs. Using orchestration, we can automate moving a model pod to a spare node.
• Isolation: On-prem deployment gives the option to physically and logically isolate this system from other corporate systems for safety. For example, the RAG servers might be in a separate DMZ-like network zone with only specific ingress/egress routes. This reduces the risk that any vulnerability in these new services could be used to pivot into other systems. Given the importance of data, we’ll enforce strict firewall rules: e.g. only allow the UI/API to be accessed by user subnets, only allow the RAG servers to query the internal data lake or dev APIs on certain ports, etc.

In summary, the deployment will treat on-premises as the default “home” for the RAG system, leveraging Enterprise ’s existing infrastructure investments and data locality. At the same time, by containerizing components and using standard tools, we ensure that moving or extending parts of the system to cloud can be done without redesign (for instance, deploying the same containers on an EKS/AKS cluster in cloud if needed). This hybrid-readiness is a form of futureproofing and offers flexibility to the infrastructure team.

Security and Compliance Considerations

Deploying a RAG system in an enterprise like Enterprise  requires strict adherence to security best practices and compliance requirements. Even if some specifics (e.g. exact regulatory obligations) are not known, we will implement “privacy-by-design” and “defense-in-depth” principles from the start

. Below are the key considerations:

• Data Security (Encryption): All data handled by the system – both at rest and in transit – will be encrypted. This means: Encryption at Rest: Enable disk encryption on all storage containing sensitive data (document texts, vector embeddings, model files if they contain any learned internal data). For example, if using Linux, LUKS encryption or self-encrypting drives can be used on the vector DB volumes. ChromaDB itself can also encrypt its persisted data store for an extra layer. This ensures that if a disk is removed or an image is copied, the raw data is not accessible.
• Encryption in Transit: Use TLS for all client-server communications. The frontend web UI will be served over HTTPS with a valid certificate. Internal service calls (e.g. from the orchestrator to the vector DB or to the LLM service) should also use TLS, even within the data center, or at least be confined to secure networks. This prevents eavesdropping or tampering by any network actor.
• We will also consider embedding-level encryption – i.e. not storing actual sensitive text in plain form. However, since we need the text for the LLM, we will store plain text but protect it via the measures above. In future, techniques like using secure enclaves or homomorphic encryption for embeddings could be explored (as hinted by research, but that’s currently complex).
• Access Control & Authentication: Role-Based Access Control (RBAC): Implement RBAC such that only authorized personnel can use the system, and even among them, sensitive data is limited. For the user interface, integrate with SSO – users log in with their corporate credentials. We can assign roles (e.g. regular user, power user, admin). The system will “ensure that only authorized users can retrieve specific data”. For instance, if there are confidential documents meant only for managers, we tag those in metadata and configure the system to only surface them if the user’s role is manager (the query layer can filter by metadata given the user’s role attribute).
• API Security: All API calls (from UI or other clients) will require an auth token. We will likely use OAuth2 or JWT-based auth issued by our SSO. Each microservice will validate tokens so that only known services or users can call. “Securing API access with token-based authentication (e.g., OAuth2) prevents unauthorized data access”.
• Admin Interfaces: Any admin tooling (like re-index triggers or system monitors) will be placed behind additional security (VPN access or admin-only network). Only the infrastructure team or system admins should be able to, say, re-run ingestion or view raw logs on the server.
• Audit and Compliance Logging: We will log all queries made and which data was retrieved to answer them. This creates an audit trail in case of any security review. If someone accessed a piece of information they shouldn’t, we can trace it. However, we must be cautious: logging the full query might itself capture sensitive info (like if a user types a person’s name or an account number). We will implement data minimization in logs – perhaps hashing certain fields or truncating long queries in logs.
• Compliance requirements such as GDPR might be relevant if any personal data of EU individuals is processed. GDPR principles like data minimization and purpose limitation will be respected: the system will only use personal data if absolutely needed to answer a query, and we won’t store it unnecessarily. For example, if the data lake has customer PII, we might decide not to ingest that into the RAG at all to avoid risks.
• If required by regulations, implement a way to remove a user’s data from the system (right to be forgotten). This could mean if a certain personal document is deleted from source, we must delete its embedding and any cached content promptly from ChromaDB.
• Regular Audits: We will conduct periodic permission audits – reviewing who has access to the system and ensuring it aligns with least privilege. Also audit the content: ensure no documents that violate compliance (like containing credit card numbers or health info, if not allowed) have been ingested. If found, remove them or mask sensitive fields. “Implementing strict access controls, encrypting sensitive data and embeddings, tokenizing PII, and monitoring for suspicious activity” are concrete steps we will take.
• Data Privacy and PII Handling: The system should be configured to avoid inadvertent exposure of PII. For instance, if some internal documents contain customer names or addresses, do we want them retrievable by all users? Likely not. We might classify documents by sensitivity and restrict queries on them. Alternatively, we can anonymize or tokenize PII during ingestion. For example, replace occurrences of customer names with a placeholder or hash in the text before embedding. This way, an answer might say “Customer [ID123] reported an issue…” rather than the real name, unless the querying user is authorized to see that name. This is a design decision to be made with Enterprise ’s privacy office.
• Train the LLM (via prompt instructions) not to reveal sensitive info verbatim unless it was provided in context. Because the LLM has a vast training background, we’ll instruct it that if a query asks for something like “list of all customer emails,” it should refuse since that’s likely disallowed.
• Minimize Data Retention: The system will not store user queries or outputs beyond what’s needed. We won’t, for example, keep a database of all questions asked indefinitely (unless for analytics with scrubbed data). This reduces risk of leaking query contents.
• System Hardening and Monitoring: Patching: Keep Ollama, ChromaDB, and all software up to date with security patches. Subscribe to security bulletins for these projects. Containerize to manage versions explicitly. Intrusion Detection: Use Enertprize ’s IDS/IPS on the network to monitor unusual traffic. Also, enable logging of admin commands on servers. If someone tries to, say, copy out a large chunk of vector data, it should alert. Penetration Testing: Have security team pen-test the RAG API and UI. For example, ensure no SQL injection (if any DB queries are used), no API keys accidentally exposed, etc. Also test that the system properly isolates user sessions (though most is stateless, but e.g. one shouldn’t access another’s cached context). LLM-specific Risks: One novel risk is prompt injection – where a user might craft an input that tries to get the system to divulge hidden prompts or secure info. We will deploy the LLM with a system prompt that strongly says to follow instructions and ignore attempts to subvert it. And we won’t include any truly secret info in the prompt context except the retrieved chunks. So the worst a malicious prompt could do is get the model to output those chunks (which the user is allowed to see anyway if they triggered retrieval of them). Rate Limiting: Implement user-level rate limits to prevent misuse or DDoS-like behavior by an insider or compromised account. For example, no single user should be able to make 1000 queries per minute and scrape all data. We can throttle queries per user and have an approval process for bulk access if needed.
• Compliance Standards: Enterprise  likely has to comply with various standards (PCI for payment data, maybe CCPA for California customer data, etc.). While RAG deals more with internal knowledge, if any covered data is included: PCI DSS: If credit card data is in scope (probably not in documents, but if it were, we’d absolutely exclude or tokenize it – it should never be processed by the LLM). HIPAA (health data): Unlikely for Enertprize  unless pharmacy records. If any health-related info is present, we would need HIPAA safeguards. Likely out of scope for now. CCPA/GDPR: As mentioned, handle personal data carefully – allow data deletion, be transparent about data usage, etc. Document the compliance measures in our design documentation so that audit committees see that we have thought through the privacy implications (the RAG architecture inherently “connects LLMs to internal data”, so we must ensure that internal data is handled as per policy).
• User Training and Policy: Even with a secure system, users should be guided on proper use: Don’t input highly sensitive personal data as queries unless the system is meant for that (to avoid it being logged or stored in vector form). Treat the answers as internal – not to be copy-pasted externally without validation. We will display a disclaimer perhaps: “For internal use only – do not share responses containing confidential info outside authorized channels.”

By implementing these security measures, we aim to “ensure the RAG system is deployed safely, preserving data confidentiality and integrity while complying with data protection regulations”. Security will be continuously revisited as the system evolves, with updates made promptly when new risks or compliance requirements are identified.

Integration with Enterprise ’s Internal Systems

One of the strengths of a RAG system is its ability to pull together knowledge from across the enterprise and present it in a unified interface. For Enterprise , integration with existing systems will maximize value. We plan the following integrations:

• Internal Dashboards and Portals: Enterprise  likely has internal web dashboards for analytics, inventory, sales, etc. We will integrate the RAG UI or API into these: For instance, on a sales metrics dashboard, an “Ask Insight” button could allow users to query “Why did sales spike in region X last week?” The RAG system would retrieve context (maybe an internal memo or news about a promotion) and answer. This turns passive dashboards into interactive analytical tools. Technically, this can be done via embedding an iframe pointing to the RAG web app, or a JavaScript SDK that calls the RAG API and displays the answer in a chat bubble. We will work with the teams owning major dashboards to pilot this. Likely targets: financial reporting portal, store operations portal, supply chain dashboard, etc., where a lot of documentation exists that RAG can leverage to explain the numbers. Integration must respect security: the embedded component will still enforce login, or we use the existing login session (via SSO) so the user doesn’t log in twice. We can use something like OAuth token exchange to let the dashboard securely call the RAG API on behalf of the user.
• CRM and Customer Support Systems: In a CRM context, time is of the essence for customer support. RAG can serve as an assistant for support agents: Use Case: When a support agent is looking at a customer’s profile in the CRM, they might ask, “Has this customer reported issues with their coupons before?” The RAG system could search through past support tickets or emails (if those are ingested) to find relevant notes. Or if an agent needs a policy, they can query, “What’s the refund policy for perishable goods?” and get an immediate answer drawn from policy docs. Implementation: We can integrate via the CRM’s extension points. Many CRM platforms allow custom widgets or side panels. We’d add a panel that contains either our web app in a trimmed-down mode or directly calls the RAG API. The key here is contextual integration: the CRM can pass the current customer ID or case ID to the RAG system. Then, the RAG’s retrieval layer could use that as a filter (only look at docs related to that customer or highlight their data). This is advanced, and may not be in initial scope, but the design will allow the query API to accept optional context parameters (like customer_id) which it can use to fetch relevant info via a direct database query or as a filter on the vector search (if we stored ticket embeddings tagged by customer). Additionally, the RAG system can integrate with chatbots or IVR for customer self-service. That might be later on: exposing certain knowledge base answers directly to customers through automated chat on Enertprize .com. Before that, we’d ensure the knowledge base is cleaned of internal-only info and approved for public use.
• Data Lake and Analytics Platforms: Enterprise ’s data lake (and associated analytics tools) hold a wealth of structured data, but users often need to consult documentation to use it. Integration ideas: Data Catalog Q&A: If Enterprise  has a data catalog (glossary of data definitions, table schemas, etc.), RAG can ingest that and allow users (especially analysts and data scientists) to ask in plain English about data. E.g., “What does the field PROMO_CODE_USED indicate?” and get the definition from the catalog. We can integrate this into the data exploration tools. BI Tool Integration: Modern BI tools (like PowerBI, Tableau) are starting to incorporate AI assistants. We can potentially integrate RAG via an extension in those tools. For example, in Tableau, a user could highlight a data point and ask the RAG system “Why is this outlier so high?” – RAG could retrieve an internal analysis doc that explains it. Also, RAG could assist in SQL generation: if a user asks a question that requires aggregating data, the system might not directly do that (since it’s not connected to a live database in this design), but it could provide guidance: “To find this, you might look at table X with a query like ...”. This is speculative, but shows how RAG could interface with data tools.
• Internal Knowledge Bases & SharePoint: Enterprise  likely has SharePoint sites or Confluence pages for internal documentation (HR policies, IT runbooks, etc.). Our ingestion can directly pull those (through their APIs or exporting to PDF). The integration here is making sure when those sources update, we re-ingest. We can set up connectors for SharePoint so that any new page or updated page triggers re-index. This is part of ingestion integration with source systems: We will coordinate with IT knowledge managers so that our system becomes the unified search. Often employees struggle searching across many SharePoint sites; RAG will solve that by collating all knowledge in one QA interface. We may integrate by adding a link on those sites: “Can’t find something? Ask the AI Assistant.”
• Alerts and Workflows: Another integration aspect is having the RAG system be callable from scripts or workflows: For instance, an automated report might call the RAG API to generate a narrative summary. E.g., a weekly sales report script could do query = "Summarize key events affecting sales this week" and get an answer that it then emails to stakeholders. This is feasible since our API can return answers in JSON. Integration with orchestrators like Airflow or CI/CD: maybe not obvious, but devOps might use it to quickly fetch documentation during a pipeline (though likely easier for them to just search a wiki).
• Output to Dashboards: We could allow the RAG system’s answers to be fed back into dashboards. For example, if multiple users ask similar questions, perhaps the answer should be published as a Q&A entry in an internal FAQ. Integration with an internal Q&A system (if exists) would be good – we don’t want the RAG to exist in a silo; it should enhance the existing knowledge management. Perhaps after some time, we gather popular queries and add them to a curated FAQ database.
• Integration with Identity Systems: As mentioned, hooking into SSO/AD is critical for both auth and using group info for access control. We’ll use something like SAML or OIDC with the corporate identity provider to manage user sessions.

Integration Summary: By weaving the RAG system into Enterprise ’s existing tools, we ensure it becomes a natural part of the workflow. Instead of being a standalone app one has to remember to use, it will be embedded where the users already are – in their dashboards, CRM screens, and portals. This approach was shown in case studies to greatly improve adoption and reduce workload on expert teams

(because employees can self-serve information easily). Our system essentially acts as a smart intermediary that “draws from a vast pool of information and delivers contextually relevant answers” to users wherever they need it

Technically, the integration will rely on our API-centric design – any platform that can make an HTTPS call can use the RAG service. We will produce documentation for internal developers on how to query the API and will likely build a few custom UI components (JavaScript widgets) to make embedding the Q&A interface easy. As adoption grows, we’ll gather feedback to improve integration, such as adding new metadata to better filter answers for specific contexts (like filtering by department or data domain when called from certain apps).

Infrastructure Request and Next Steps

To implement this plan, we request the following infrastructure resources and support from Enterprise ’s IT/infrastructure team:

• Server Provisioning: GPU Servers: Two high-end GPU servers for production (each with 8× 80GB GPUs, dual CPUs, 512GB RAM as specified). Additionally, one smaller GPU server for staging (4× GPUs) and one for development (1–2 GPUs). These servers ideally should be identical models for easier maintenance (e.g. NVIDIA DGX or similar). CPU Servers: One powerful server for the vector DB (large RAM), and at least two general-purpose servers for the ingestion and application components in production. Plus, corresponding smaller instances for staging/dev. If physical servers are not readily available, consider utilizing an existing Kubernetes cluster with node pools that meet these specs, or a private cloud setup that can supply VMs with GPU passthrough. However, physical or bare metal for the GPU nodes is preferred for performance. Storage: Allocate ~10 TB of fast storage as outlined (NVMe on key servers and NAS for shared data). Concretely, we request provisioning of: 2TB NVMe or local SSD on each GPU server. 4TB high-speed disk on the DB server. Access to a network storage share of 4TB for general use (document repository, backups). Network: Ensure the aforementioned servers have at least dual 25GbE connections and IB adapters if multi-node GPU coordination is needed. Work with network team to configure a VLAN for these servers and necessary firewall rules (allow internal user subnet to access the web UI/API, allow egress to specific services like GitHub or OCR model downloads, etc.).
• Software & Platform Setup: Prepare a Kubernetes cluster or similar orchestration environment on these nodes. If one doesn’t exist, we request setting up a new k8s cluster with these nodes assigned (with GPU support via NVIDIA device plugin). Alternatively, provide us with VM or bare-metal access and we will configure Docker Swarm or a simpler orchestration if needed. Install base OS (Linux, preferably Ubuntu 22.04 LTS or RHEL/CentOS 8) on all servers with SSH access for our team. Install NVIDIA drivers and CUDA on GPU servers. Ensure connectivity between servers is in place (they should resolve each other, etc.). Configure any required load balancers or DNS entries (e.g., a DNS name like rag.Enertprize .int pointing to the frontend service IP).
• Security & Compliance Support: Engage the security team to review our plan for RBAC and data handling. We might need them to provision an SSO client application for our UI (so that we can do single sign-on). We will need appropriate OAuth2 client credentials or SAML integration details. Provide guidance on any specific compliance data we should avoid or treat specially (if not already known). If there are internal data classification levels, share those so we tag our data accordingly and implement controls. Set up logging/monitoring infrastructure for us: e.g., give us access to Splunk or ELK to push our logs, so that everything is centralized as per company standard.
• Development & Testing Environment: Allocate a smaller-scale environment (perhaps in a sandbox network or using some cloud credits) for initial development. For example, we could use a few VMs or smaller GPU (like a single A100 on a dev server) to start coding and testing pipeline. This doesn’t have to be production-grade, but we may need the infra team to open some firewalls for dev (e.g. internet access to download models). Alternatively, if corporate policy allows, we might do early dev in a cloud sandbox (using non-sensitive dummy data) and then port to on-prem once it’s stable. Coordination on this would be helpful.
• Timeline & Collaboration: We outline the phases:

• Phase 1 – Prototype (Next ~1-2 months): Use dev resources to stand up a minimal RAG pipeline with a small model and a subset of data. Infra team deliverables: dev server availability, access to sample data sources.
• Phase 2 – Pilot (Following 2-3 months): Scale to staging environment with more data and larger model (e.g. 70B). Infra: staging servers up, SSO integration, etc. In this phase, we’ll involve a group of pilot users and iterate.
• Phase 3 – Production Rollout (Target ~6 months): Deploy full production cluster as specified, with the 440B model capability, all pipelines automated, monitoring in place. We’ll do a phased rollout, maybe department by department.

• We request the infra team to prioritize provisioning the hardware for Phase 2 and 3 as early as possible due to lead times (GPU machines might take time to arrive or set up). If some hardware (like second GPU node) will come later, we can adjust the rollout but ideally have all by production go-live.
• Support & Maintenance: Determine ownership of maintenance. Likely our AI team will own the application (Ollama, Chroma, etc.), but infra will manage the hardware, OS, and possibly Kubernetes. We should define incident response: e.g., if a server goes down at 2am, who responds? We will have monitoring to alert both our team and infra as appropriate. Setting up proper communication channels (Slack or pager for critical alerts) is needed.

Finally, we will need ongoing dialogue with data owners (to feed new data) and with users (to tune the system). But from an infrastructure standpoint, the above requests summarize what’s needed to get this RAG system up and running at enterprise scale. Once in place, this system is expected to significantly streamline knowledge access within Enterprise , “delivering accurate and comprehensive answers quickly” to employees, and thus, we appreciate the support in making the required infrastructure available.

With the infrastructure provisioned and the plan detailed in this report, we are prepared to proceed with implementation, adhering to the outlined architecture, security measures, and integration strategy to ensure a successful deployment of the RAG system at Enterprise .