DEV Community: Summiya ali

Building a Scalable RAG Pipeline on Google Cloud Using Vertex AI and Cloud Run

Summiya ali — Mon, 16 Mar 2026 07:13:26 +0000

1. Introduction

Organizations today generate massive volumes of unstructured information such as documentation, knowledge base articles, technical manuals, compliance guidelines, and internal procedures. While cloud storage platforms provide efficient ways to store this information, retrieving relevant insights from large document collections remains a major challenge.

Traditional search systems rely primarily on keyword matching. This approach works well when users know the exact words contained in a document, but it becomes ineffective when queries are phrased differently from the original content. As document repositories grow, the limitations of keyword-based search become more pronounced.

Retrieval Augmented Generation (RAG) addresses this problem by combining semantic document retrieval with generative AI models. Instead of generating answers solely from model training data, the system retrieves relevant documents and uses them as contextual input during response generation.

Google Cloud provides several services that enable developers to build scalable RAG pipelines. Services such as Vertex AI, Cloud Run, Cloud Storage, and vector search infrastructure allow organizations to construct intelligent knowledge systems capable of answering complex questions based on internal data sources.

This article explains how to design and implement a scalable RAG architecture on Google Cloud using these services.

2. Understanding Retrieval Augmented Generation

2.1 The Limitations of Traditional Language Models

Large language models are trained on vast datasets and are capable of generating coherent natural language responses. However, these models do not inherently have access to proprietary enterprise knowledge or newly created documentation.

When users ask questions related to internal company information, the model may produce incomplete or inaccurate answers because the required knowledge was never included in its training data.

2.2 The RAG Solution

Retrieval Augmented Generation modifies the standard language model workflow by introducing a retrieval stage before response generation.

Instead of sending the user query directly to the model, the system performs the following sequence:

Convert the user query into a vector embedding.
Search a vector database containing document embeddings.
Retrieve the most relevant document segments.
Provide these segments as context to the language model.
Generate a response grounded in the retrieved knowledge.

This process ensures that the model’s response is informed by real organizational documents rather than purely statistical predictions.

2.3 Typical RAG Workflow

A simplified workflow of a RAG system can be represented as follows:

User Query
↓
Embedding Generation
↓
Vector Similarity Search
↓
Relevant Documents Retrieved
↓
LLM Response Generation

By grounding responses in actual documents, RAG systems significantly reduce hallucinations and improve answer accuracy.

3. High-Level System Architecture

3.1 Core Components

A scalable RAG architecture on Google Cloud typically includes the following components.

Cloud Storage serves as the primary repository for enterprise documents.

A document processing pipeline extracts text from stored files and prepares it for indexing.

Vertex AI generates embeddings for document segments and user queries.

A vector database stores embeddings and enables semantic search.

Cloud Run hosts an API service responsible for query processing.

Vertex AI language models generate context-aware responses.

3.2 Query Processing Flow

The interaction between these components follows a structured pipeline.

A user submits a question through a web interface or chatbot.
The query is sent to an API service deployed on Cloud Run.
The API generates an embedding for the query using Vertex AI.
The system searches the vector database for similar embeddings.
Relevant document segments are retrieved.
The retrieved context is passed to a language model.
The model generates a final response that is returned to the user.

This architecture allows the system to scale automatically as query volume increases.

4. Preparing Enterprise Documents for Retrieval

4.1 Document Ingestion

Enterprise documents are typically stored in a centralized Cloud Storage bucket. Files may include PDFs, Word documents, technical manuals, policy documents, and training materials.

An ingestion pipeline continuously monitors the storage bucket for newly uploaded documents.

4.2 Text Extraction

After ingestion, the system extracts textual content from each document. Different extraction techniques are applied depending on file type.

PDF parsing tools extract text from structured documents. Optical Character Recognition may be used for scanned images or handwritten materials.

4.3 Document Chunking

Large documents must be divided into smaller sections to improve retrieval accuracy.

Chunking strategies typically split documents into segments containing 300–800 tokens. Overlapping chunks are sometimes used to preserve contextual continuity between segments.

4.4 Metadata Enrichment

Each document chunk is enriched with metadata such as document title, author, department, and creation date.

Metadata fields enable more refined filtering during search queries and improve knowledge organization.

5. Generating Semantic Embeddings with Vertex AI

5.1 Embedding Models

Vertex AI provides embedding models capable of transforming natural language text into high-dimensional numerical vectors.

These vectors represent semantic meaning rather than simple word frequency.

5.2 Document Embedding Generation

During indexing, each document chunk is processed through the embedding model to produce a vector representation.

These vectors are stored in the vector database along with their corresponding text segments.

5.3 Query Embedding Generation

When a user submits a query, the same embedding model converts the query into a vector representation.

This vector is then compared against stored embeddings to identify semantically similar documents.

6. Implementing Vector Search

6.1 Vector Databases

Vector databases are optimized for performing similarity searches across large collections of embeddings.

Common options available for Google Cloud architectures include:

Vertex AI Vector Search
Pinecone
Weaviate
Elasticsearch with vector search extensions

6.2 Similarity Search

Similarity search algorithms measure the distance between vectors within a high-dimensional space.

The most common methods include cosine similarity and Euclidean distance.

The system retrieves the top-ranked document segments whose embeddings most closely match the query embedding.

6.3 Retrieval Optimization

Retrieval accuracy can be improved by:

adjusting chunk size
using hybrid keyword + semantic search
applying metadata filtering
ranking results based on relevance scores

7. Deploying the Query Service with Cloud Run

7.1 API Service Architecture

The backend query service orchestrates the entire RAG workflow.

Its responsibilities include:

receiving user queries
generating embeddings
performing vector search
constructing prompts
calling the language model
returning responses to the user

7.2 Containerized Deployment

Cloud Run enables developers to deploy containerized services that automatically scale based on traffic.

The service can be packaged within a Docker container containing:

a Python or Node.js application
vector database client libraries
Vertex AI SDK integration

7.3 Automatic Scaling

Cloud Run automatically provisions additional instances when request volume increases. This ensures that the system can handle large numbers of concurrent queries without manual infrastructure management.

8. Prompt Engineering for Contextual Responses

8.1 Context Construction

The retrieved document segments must be combined into a structured prompt before being sent to the language model.

The prompt typically includes:

retrieved context
user question
instructions for the model

8.2 Grounded Response Generation

A common prompt instruction is:

“Answer the question using only the provided context.”

This instruction helps prevent the model from generating unsupported claims.

9. Security and Governance

9.1 Identity and Access Management

Google Cloud IAM allows administrators to define fine-grained permissions for accessing storage buckets, AI models, and API services.

Only authorized services should be allowed to retrieve sensitive enterprise documents.

9.2 Data Protection

Enterprise knowledge bases may contain confidential information. Security measures include encrypted storage, secure API communication, and controlled service access.

9.3 Audit Logging

Cloud Logging can track system interactions such as document retrieval, query activity, and model responses.

Logs are essential for debugging, compliance monitoring, and incident investigation.

10. Monitoring and Observability

Production AI systems require continuous monitoring to maintain reliability.

Cloud Monitoring provides visibility into system performance metrics such as request latency, error rates, and query volume.

Developers can configure alerts that trigger notifications when system performance deviates from expected thresholds.

Monitoring also helps identify opportunities to optimize document retrieval and model response quality.

11. Enterprise Use Case: AI Knowledge Assistant

Consider a large enterprise with thousands of internal documents covering policies, procedures, and engineering guidelines.

Employees often struggle to locate specific information quickly, leading to increased support requests and reduced productivity.

A RAG-based AI assistant built on Google Cloud can address this problem.

Employees submit questions through a chatbot interface integrated into the company intranet.

The system retrieves relevant documentation, summarizes the information, and provides direct links to the original documents.

Such systems significantly reduce manual document searches and improve organizational knowledge accessibility.

12. Conclusion

Retrieval Augmented Generation has become a foundational architecture for building reliable AI-powered knowledge systems. By combining semantic document retrieval with large language models, organizations can create intelligent assistants capable of answering complex questions based on internal documentation.

Google Cloud provides a powerful platform for implementing these systems using services such as Cloud Storage, Vertex AI, vector search infrastructure, and Cloud Run.

As enterprise data continues to expand, RAG pipelines will play an increasingly important role in transforming static document repositories into dynamic, AI-driven knowledge platforms.

Building a Knowledge Base using SharePoint and AI Search

Summiya ali — Mon, 16 Mar 2026 06:46:38 +0000

Introduction

Organizations generate large volumes of documents every day, including policies, project documentation, technical manuals, and support guides. While Microsoft SharePoint is widely used as a document management platform, finding the right information quickly can still be difficult when content grows at scale.

Traditional keyword-based search often fails when users do not know the exact terms used in documents. This is where AI-powered search can significantly improve the experience.

By combining SharePoint’s document storage capabilities with AI search technologies, organizations can build intelligent knowledge bases that allow employees to retrieve accurate information through natural language queries.

This article demonstrates how to design and build a knowledge base using SharePoint Online and AI-powered search services, enabling faster knowledge discovery across enterprise content.

Why Build a Knowledge Base with SharePoint

SharePoint already acts as a centralized repository for enterprise documents. However, its default search capabilities may not always provide context-aware results.

A knowledge base built on top of SharePoint can provide:

Centralized document storage
Structured knowledge organization
AI-powered semantic search
Faster access to internal knowledge
Improved employee productivity

Organizations commonly use such systems for:

IT support documentation
HR policies and guidelines
onboarding resources
technical documentation
internal training materials

High-Level Architecture

An AI-powered knowledge base typically consists of the following components:

SharePoint Online
Stores enterprise documents such as PDFs, Word files, policies, and guides.

Document Indexing Pipeline
Extracts text content from documents stored in SharePoint.

AI Search Engine
Processes and indexes the extracted content to enable semantic search.

Application Interface
Allows users to query the knowledge base using natural language.

A simplified architecture looks like this:

Users
   ↓
Web Portal / Chatbot
   ↓
AI Search Engine
   ↓
Indexed Content
   ↓
SharePoint Document Libraries

The AI search layer allows users to ask questions such as:

“What is the company policy for remote work?”

Instead of searching manually through documents, the system retrieves the most relevant knowledge from SharePoint.

Setting Up SharePoint as a Knowledge Repository

The first step is organizing enterprise documents inside SharePoint.

1 Create a Knowledge Base Site

In SharePoint Admin Center:

Create a new Team Site or Communication Site
Name it something like:

Company Knowledge Hub

Configure appropriate access permissions.

2 Organize Document Libraries

Create structured document libraries such as:

HR Policies
IT Support Guides
Training Materials
Technical Documentation

Proper organization improves both search performance and knowledge management.

3 Use Metadata for Better Search

Metadata helps AI systems understand document context.

Examples:

Metadata Field	Example
Category	HR Policy
Department	Human Resources
Document Type	Guideline
Last Updated	2026

These fields help AI models deliver more accurate results.

Extracting Data from SharePoint

To enable AI search, documents must be retrieved and processed from SharePoint.

Developers typically use the Microsoft Graph API to access SharePoint data programmatically.

Example request to retrieve documents from SharePoint:

import requests

endpoint = "https://graph.microsoft.com/v1.0/sites/{site-id}/drive/root/children"

headers = {
    "Authorization": "Bearer ACCESS_TOKEN"
}

response = requests.get(endpoint, headers=headers)

documents = response.json()

print(documents)

This API call retrieves files stored in a SharePoint document library.

The extracted documents are then processed and indexed by the AI search engine.

Building the AI Search Layer

AI search engines allow users to query information using natural language rather than exact keywords.

Common technologies used include:

Azure AI Search
Elasticsearch with AI plugins
vector databases
LLM-powered retrieval systems

The search engine typically performs three steps:

Document processing
Embedding generation
Semantic search retrieval

Example: Creating Vector Embeddings

AI search works by converting document text into numerical representations called embeddings.

Example using Python:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

text = "Company employees are allowed to work remotely two days per week."

embedding = model.encode(text)

print(embedding)

These embeddings allow the system to measure semantic similarity between queries and documents.

Implementing Semantic Search

Once documents are indexed, the system can answer user queries intelligently.

Example user query:

"What is the leave policy for employees?"

The system processes the query as follows:

Convert the query into an embedding
Compare it with stored document embeddings
Retrieve the most relevant knowledge sections

Example simplified search logic:

query = "What is the remote work policy?"

query_embedding = model.encode(query)

# Compare with stored embeddings
results = vector_database.search(query_embedding)

print(results)

The system returns the most relevant document passages stored in SharePoint.

Building a User Interface for the Knowledge Base

The knowledge base can be accessed through several interfaces:

Web applications
internal portals
chatbots
Microsoft Teams bots

For example, a chatbot interface can allow employees to ask questions like:

"How do I reset my corporate password?"

The AI system retrieves the answer from SharePoint documentation instantly.

Security and Access Control

Enterprise knowledge systems must enforce strict security policies.

Important considerations include:

SharePoint Permissions

Use SharePoint’s built-in role system:

Site owners
members
visitors

Ensure sensitive documents are only accessible to authorized users.

API Security

When accessing SharePoint programmatically:

Use Azure AD authentication
implement OAuth tokens
enforce least privilege permissions

Data Encryption

Protect enterprise knowledge with:

HTTPS communication
encrypted document storage
secure API access

These controls ensure compliance with enterprise security policies.

Practical Use Case: Enterprise IT Knowledge Assistant

Consider an internal IT support system built using this architecture.

Employees can ask questions such as:

"How do I connect to the company VPN?"
"Where can I download company security policies?"
"What is the process for requesting new software?"

The system retrieves relevant instructions directly from SharePoint documentation and presents them to users.

This reduces support tickets and improves employee productivity.

Benefits of AI-Powered Knowledge Bases

Organizations adopting AI-powered knowledge bases gain several advantages:

faster knowledge discovery
reduced time spent searching for documents
improved employee productivity
scalable knowledge management
intelligent enterprise search capabilities

As enterprise data continues to grow, intelligent knowledge systems become increasingly essential.

Conclusion

SharePoint provides a strong foundation for enterprise document management, but integrating it with AI-powered search significantly enhances its capabilities.

By combining SharePoint Online, AI search engines, and semantic retrieval techniques, organizations can build powerful knowledge bases that allow employees to access information quickly and efficiently.

As generative AI and enterprise search technologies evolve, AI-powered knowledge systems will become a core component of modern digital workplaces.

Implementing such systems enables organizations to transform static document repositories into intelligent knowledge platforms that support faster decision-making and better collaboration.

Understanding Gradient Descent for Beginners: The Core of Neural Network Learning

Summiya ali — Wed, 11 Jun 2025 21:21:32 +0000

Gradient Descent is an optimization algorithm that helps neural networks learn by adjusting weights to reduce errors in predictions.

What is Gradient Descent?
A Simple Analogy
Why Is It Important in Neural Networks? (Cat vs. Dog Example)
The Gradient Descent Formula Explained
Why the Negative Sign? Why “Descent”?
Types of Gradient Descent
Drawbacks of Gradient Descent
Conclusion: Smarter Alternatives Today

1. What is Gradient Descent?

Gradient Descent is a method that helps neural networks reduce prediction errors by changing the internal weights (which are like settings) in the direction that minimizes the loss function (a formula that tells how wrong the prediction was).

2. A Simple Analogy

Imagine you're blindfolded and standing on a hill, and your goal is to reach the lowest point in the area (like finding the least error). Here's how the key terms relate:

Loss Function → the shape of the hill (how high or low you are, based on error)
Gradient → the steepness and direction of the hill at your feet
Step size (learning rate) → how big a step you take in each move
Gradient Descent → you slowly move in the direction down the hill (to reduce error)

You feel the slope under your feet and always take small steps downhill. You don't want to go uphill (where the error increases), so you follow the opposite direction of the gradient.

3. Why Is It Important in Neural Networks?

Imagine you're training a neural network to recognize cats vs. dogs in images.

At first, your model might think a cat is a dog. That’s an error.

Gradient Descent helps the model learn from its mistakes by:

Measuring how wrong the prediction was (loss)
Calculating the direction to adjust weights (gradient)
Updating the weights to improve future predictions

Every image it sees (cat or dog), the model gets a bit better by moving closer to the correct answer — step by step.

Without Gradient Descent (or a similar method), the network wouldn’t know how to improve itself.

4. The Gradient Descent Formula Explained

w=w−η⋅ dw/dL

Let’s break this down:

w = weight (what the model is trying to adjust)

η (eta) = learning rate (how big the update step is)

dw/dL = the gradient (i.e., how much the loss changes when weight changes)

The idea:
The model checks how much the weight contributed to the error, then adjusts it a little to make the error smaller next time.

5. Why the Negative Sign? Why “Descent”?

The gradient ($\frac{dL}{dw}$) tells you how to increase the loss.

But we don’t want more error — we want less.

So we move in the opposite direction of the gradient — that’s why the formula has a minus sign.

We’re always going downhill on the loss curve — hence the name "Gradient Descent".

6. Types of Gradient Descent

There are three main versions, based on how much data we use to update weights:

1. Batch Gradient Descent

Uses the entire dataset to calculate the gradient before updating.
Very accurate, but slow if the dataset is large.

2. Stochastic Gradient Descent (SGD)

Updates weights using one data point at a time.
Much faster, but more noisy and may fluctuate.

3. Mini-Batch Gradient Descent

Uses small groups of data (e.g., 32 samples) to update weights.
Combines the best of both: efficient and more stable.
Most widely used in practice.

7. Drawbacks of Gradient Descent

Despite being powerful, Gradient Descent has some key challenges:

1. Slow Convergence

Training deep neural networks can take a long time to reach good performance.

2. Local Minima

The algorithm might get stuck in a small dip (a local minimum) and miss the best solution (global minimum).

3. Oscillations

If the learning rate is too high, the algorithm may overshoot the minimum and bounce back and forth, never settling.

8. Conclusion: Smarter Alternatives Today

Gradient Descent is the foundation of how neural networks learn — but it’s not perfect.

Today, we often use improved versions like:

Momentum — keeps moving in a direction to avoid getting stuck
Adam Optimizer — adapts learning rates based on past steps
RMSProp, Nesterov Accelerated Gradient, and others

These are all built on the core idea of Gradient Descent, but with extra tools to make learning faster and smarter.

How I Built a React App with AI, VS Code, and Pure Vibes!

Summiya ali — Sat, 31 May 2025 17:27:27 +0000

Spoiler alert: There’s a cat. And it’s zesty.

Idea Spark: Talking to AI While Half-Asleep

One fine day (or night, who knows anymore), I had a thought:

"What if I had a sleepy cat in my React app that would sass me every time I clicked it?"

Normal people would sleep.
I opened VS Code and said:

"Yo ChatGPT, help me make a zesty cat app."

And just like that, I began coding a full React project — powered by a bash script, AI code generation, and zero patience for googling things manually.

The Setup: VS Code, Git Bash & Me

Tools I Used:

Visual Studio Code (because real devs use dark mode with pride)
Git Bash (for terminal drama)
ChatGPT (my unpaid AI intern)
OneDrive Desktop (accidentally. Don't judge.)

My Working Directory?

Yep. I know. Even the folder names scream “this user is chaotic neutral.”

Phase 1: Writing the Bash Script Like a Boss

I told ChatGPT:

“Write me a bash script that sets up a full React project with a ZestyCat component.”

It spat out gold. A script that:

Runs npx create-react-app
Creates a ZestyCat.js component and CSS file
Injects personality (and sass) into the cat
Updates App.js to feature the cat as the main character

And I saved it as:
create-zesty-cat.sh

Phase 2: "Why Won’t You Run?!"

Of course, no dev journey is complete without errors that question your sanity.

Problem:

Running the bash script in Git Bash gave me:

Me:

“I literally just told you to create it!”

Realization:

The create-react-app command was after the cd in the script.
I was trying to walk into a house before it was built.

Fix: I moved the npx create-react-app line to the top of the script.

Phase 3: React App is Born... and Broken

I ran:

Boom shakalakaaa! The app was created. Files in place. Components looking good.
I ran:

And React said:

My Reaction:

“AJ-who? I don’t even know that guy!”

Fix:

I did what any experienced dev would do:

rm -rf node_modules package-lock.json
npm cache clean --force
npm install

A cup of coffee later, it finally worked.

Meet ZestyCat: Your Judgmental Dev Buddy

ZestyCat is a React component with one job — to sass you when you click it.

Features:

Sleeps peacefully until disturbed
Gets angry when clicked
Judges you with a "😾" face
Tells you to stop bothering it because “it can vibe-code later”

Just like a real dev cat.

The Look: Pure Comic Sans Chaos

The cat is styled with:

Yes. Comic Sans. Because nothing says “I take front-end seriously” like Comic Sans in a dashed border.

The Final Test: npm start

With everything in place, the moment of truth:

Boom.
My local server came alive. ZestyCat was breathing but sleeping.

I clicked it because it is annoying to let him sleep while I cant.

It got angry — as it should. My mission was complete.

TL;DR: How I Did It (So You Can Too)

Talk to ChatGPT — Tell it what you want. Be as chaotic as you like.
Write a bash script — Let the AI generate it.
Run it in Git Bash — Fix paths if needed.
Fix broken node_modules — It's a React tradition.
Launch your app — Watch your digital cat get annoyed.

Final Thoughts

I didn’t just build a React app.
I built a sassy, sleepy cat with attitude — entirely from talking to AI and using VS Code.

No Stack Overflow. No Googling. Just me, my terminal, and my virtual cat overlord.

Want the full source code or help customizing it?
Drop a 🐱 below, or just poke ChatGPT again — it never sleeps.

Happy Hacking!

5 Common Flutter Errors and How to Fix Them (2025)

Summiya ali — Fri, 23 May 2025 09:26:48 +0000

Flutter is powerful — but even the most seasoned developers hit unexpected bugs. In this post, I’ll walk through five real Flutter issues I ran into, what caused them, and how I solved them. Whether you're new to Flutter or knee-deep in a build, these solutions might save you serious debugging time.

When you start working with Flutter, you quickly realize it’s both amazing and... frustrating. The "hot reload" feels magical, but some bugs? Not so much.

Here are 5 real bugs I encountered while working with Flutter — and how I solved them. Hopefully, this will save you hours of hair-pulling.

1. `The method 'receiveGuardedBroadcastStream' isn't defined for the class 'EventChannel'.`

What Happened

After updating some Firebase packages, I started getting this scary error related to EventChannel.

Root Cause

The receiveGuardedBroadcastStream method was removed from Flutter. Older versions of Firebase packages used it.

Solution

Upgrade all FlutterFire packages to versions compatible with the latest Flutter SDK:

flutter pub upgrade --major-versions

Also, clean your cache to remove corrupted or old packages:

flutter clean
flutter pub get

2. `Error: The system cannot find the path specified` when importing `_flutterfire_internals`

What Happened

My build was failing with missing files, even though the package was installed.

Root Cause

My .pub-cache was corrupted or partially downloaded.

Solution

Force re-fetch the package:

flutter pub cache repair

If that fails, delete the specific directory:

rm -rf ~/.pub-cache/hosted/pub.dev/_flutterfire_internals-*
flutter pub get

3. `accentColor` Is Deprecated

What Happened

Using accentColor in my ThemeData caused a deprecation warning.

Root Cause

Flutter moved from accentColor to the ColorScheme API with Material 3.

Solution

Use colorScheme.secondary instead:

colorScheme: ColorScheme.fromSeed(
  seedColor: Colors.teal,
  secondary: Colors.amber,
),

And when styling widgets:

color: Theme.of(context).colorScheme.secondary

4. Custom `ThemeData` Not Applying to Widgets

What Happened

Despite setting a custom textTheme, my Text widgets still looked default.

Root Cause

I was overriding styles directly in widgets instead of relying on the theme.

Solution

Avoid this:

Text('Hello', style: TextStyle(fontSize: 18, color: Colors.black))

Do this:

Text('Hello', style: Theme.of(context).textTheme.bodyLarge)

Make sure it’s defined in your ThemeData:

textTheme: const TextTheme(
  bodyLarge: TextStyle(fontSize: 18.0),
),

5. `RenderFlex Overflow` on Smaller Devices

What Happened

My layout looked fine on my emulator but broke on smaller phones.

Root Cause

Hardcoded height or padding values caused overflow.

Solution

Use flexible layouts:

Expanded(
  child: ListView(
    children: [...],
  ),
)

Or wrap with SingleChildScrollView if needed:

SingleChildScrollView(
  child: Padding(
    padding: const EdgeInsets.all(16),
    child: Column(
      children: [...],
    ),
  ),
)

Test with different device sizes using Flutter DevTools or:

flutter run --device-id

Final Thoughts

Every bug is a lesson. These bugs taught me to:

Use the latest stable package versions
Stick to theme-aware styling
Trust Flutter’s layout system
Keep my cache and dependencies clean

If you've hit similar issues or have your own war stories, drop them in the comments! Let’s debug together!

DEV Community: Summiya ali

Building a Scalable RAG Pipeline on Google Cloud Using Vertex AI and Cloud Run

1. Introduction

2. Understanding Retrieval Augmented Generation

2.1 The Limitations of Traditional Language Models

2.2 The RAG Solution

2.3 Typical RAG Workflow

3. High-Level System Architecture

3.1 Core Components

3.2 Query Processing Flow

4. Preparing Enterprise Documents for Retrieval

4.1 Document Ingestion

4.2 Text Extraction

4.3 Document Chunking

4.4 Metadata Enrichment

5. Generating Semantic Embeddings with Vertex AI

5.1 Embedding Models

5.2 Document Embedding Generation

5.3 Query Embedding Generation

6. Implementing Vector Search

6.1 Vector Databases

6.2 Similarity Search

6.3 Retrieval Optimization

7. Deploying the Query Service with Cloud Run

7.1 API Service Architecture

7.2 Containerized Deployment

7.3 Automatic Scaling

8. Prompt Engineering for Contextual Responses

8.1 Context Construction

8.2 Grounded Response Generation

9. Security and Governance

9.1 Identity and Access Management

9.2 Data Protection

9.3 Audit Logging

10. Monitoring and Observability

11. Enterprise Use Case: AI Knowledge Assistant

12. Conclusion

Building a Knowledge Base using SharePoint and AI Search

Introduction

Why Build a Knowledge Base with SharePoint

High-Level Architecture

Setting Up SharePoint as a Knowledge Repository

1 Create a Knowledge Base Site

2 Organize Document Libraries

3 Use Metadata for Better Search

Extracting Data from SharePoint

Building the AI Search Layer

Example: Creating Vector Embeddings

Implementing Semantic Search

Building a User Interface for the Knowledge Base

Security and Access Control

SharePoint Permissions

API Security

Data Encryption

Practical Use Case: Enterprise IT Knowledge Assistant

Benefits of AI-Powered Knowledge Bases

Conclusion

Understanding Gradient Descent for Beginners: The Core of Neural Network Learning

Table of Contents

1. What is Gradient Descent?

2. A Simple Analogy

3. Why Is It Important in Neural Networks?

4. The Gradient Descent Formula Explained

5. Why the Negative Sign? Why “Descent”?

6. Types of Gradient Descent

1. Batch Gradient Descent

2. Stochastic Gradient Descent (SGD)

3. Mini-Batch Gradient Descent

7. Drawbacks of Gradient Descent

1. Slow Convergence

2. Local Minima

3. Oscillations

8. Conclusion: Smarter Alternatives Today

How I Built a React App with AI, VS Code, and Pure Vibes!

Idea Spark: Talking to AI While Half-Asleep

The Setup: VS Code, Git Bash & Me

Tools I Used:

My Working Directory?

Phase 1: Writing the Bash Script Like a Boss

Phase 2: "Why Won’t You Run?!"

1. `The method 'receiveGuardedBroadcastStream' isn't defined for the class 'EventChannel'.`

2. `Error: The system cannot find the path specified` when importing `_flutterfire_internals`

3. `accentColor` Is Deprecated

4. Custom `ThemeData` Not Applying to Widgets

5. `RenderFlex Overflow` on Smaller Devices