Dynamic Knowledge Retrieval: Creating Real-Time RAG Solutions with Gemini and Vector Search

Introduction

During my time at TIVIT, I was involved in the early stages of a mergers and acquisitions (M&A) process, where I assessed several companies in the Brazilian data and AI ecosystem.

One of the biggest challenges in such processes is the need to quickly analyze vast amounts of financial, legal, and technical documents, while also keeping up with real-time market trends and regulatory changes.

This experience inspired me to explore how RAG (Retrieval-Augmented Generation) combined with Gemini and Vector Search can revolutionize knowledge retrieval for high-stakes decision-making.

The Challenge: M&A Knowledge Bottleneck

M&A transactions involve multiple stakeholders—financial analysts, legal teams, and industry experts—all working with fragmented knowledge sources. Key pain points include:

Scattered Information: Documents are spread across data rooms, legal databases, and regulatory reports.

Time-Sensitive Decisions: Delays in retrieving relevant insights can impact negotiations and risk assessments.

Contextual Complexity: Extracting meaningful patterns from different formats, such as contracts, financial statements, and news reports, requires advanced AI-driven contextual understanding.

The Solution: A RAG-Powered M&A Intelligence Assistant

By integrating Gemini with RAG and Vertex AI’s Vector Search, we can build an M&A Intelligence Assistant that dynamically retrieves and synthesizes knowledge from structured and unstructured data sources.

How It Works

Ingest and Index Data:

Upload contracts, financial reports, and regulatory filings into a vector database.

Convert text into embeddings using Gemini to enable semantic search.

Integrate real-time news and market data feeds.

Dynamic Query Processing:

Users ask high-level questions (e.g., “What are the biggest compliance risks for acquiring this company?”).

The RAG system retrieves relevant documents using Vector Search.

Gemini generates an AI-powered response, citing the sources dynamically.

Actionable Insights for Decision-Makers:

Risk assessment: Identify contractual red flags and financial anomalies.

Regulatory compliance tracking: Monitor global and local regulations in real time.

Competitor intelligence: Extract market positioning insights from industry reports.

Implementation: Building the System on Vertex AI

Step 1: Setting Up Vector Search on Vertex AI

from google.cloud import aiplatform

# Initialize Vertex AI
project_id = "my-gcp-project"
aiplatform.init(project=project_id, location="us-central1")

# Create a Vector Search Index
vector_index = aiplatform.MatchingEngineIndex.create(
    display_name="mna-docs-index",
    contents_delta_uri="gs://my-bucket/embedding-data/"")

Step 2: Generating Embeddings with Gemini

from vertexai.language_models import TextEmbeddingModel

gemini = TextEmbeddingModel.from_pretrained("gemini-1")

def generate_embedding(text):
    return gemini.get_embeddings([text])[0]

Step 3: Querying Documents with RAG

def retrieve_knowledge(query):
    query_embedding = generate_embedding(query)
    results = vector_index.find_neighbors(query_embedding, top_k=5)
    return results

Step 4: Answering with Gemini

def generate_response(query):
    relevant_docs = retrieve_knowledge(query)
    context = "\n".join([doc["content"] for doc in relevant_docs])

    prompt = f"Based on the following documents, answer the query: {query}\n\n{context}"
    response = gemini.predict(prompt)
    return response

Conclusion

By leveraging Gemini, RAG, and Vector Search, we can transform M&A processes by eliminating knowledge retrieval bottlenecks and providing real-time, AI-powered insights. This approach not only enhances decision-making but also accelerates due diligence, making transactions more efficient and data-driven.

What other applications of RAG-powered AI do you see in your industry? Let’s discuss! 🚀