Article

In-Memory Vector Magic: Build Faster Chatbots with FAISS

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April 1, 2025

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5 min read

If you've been on tech Twitter or tech news lately, you've definitely seen the vibe coding revolution - where non-programmers build functional apps just by describing what they want to AI. It's democratizing software creation in ways we never imagined. But here's what nobody's talking about: these vibe-coded apps hit a wall when they need to understand your specific data.

The real challenge isn't building chatbots that answer generic questions - it's making AI agents truly understand the depth and relationships in your company's information without drowning in infrastructure complexity. Let’s walk through how to solve this problem step by step using Facebook AI Similarity Search (FAISS) with AWS data lakes.

In order to picture how to build AI agent, especially in this context where we may have various data sources and have to make sense of a knowledge base, let’s examine a practical example: an AI chatbot that analyzes unstructured contacts from Salesforce and contract data from Google Drive stored in AWS S3 Iceberg tables.

Architecture

Before diving into implementation details, let's visualize the complete architecture of our conversational data analysis system. The diagram below shows how data flows from various sources through our pipeline to ultimately answer user queries:

Architecture Diagram for AI Chatbot Connecting Data Sources to AWS S3 Data Lake

As you can see in the diagram, our system begins with diverse data sources on the left (Google Drive documents, Salesforce data, CSV files), which are ingested through Airbyte into our AWS S3 Data Lake. From there, AWS Athena helps retrieve and preprocess the data, which then flows into our FAISS vector store after being chunked and embedded. When a user submits a query (shown at the bottom), the system retrieves relevant documents from FAISS and generates natural language responses.

The underlying structure of this application, from the data ingestion layer to what the user sees and interacts with, involves these components:

Data Ingestion Layer (Airbyte) - Brings data from source systems into AWS
Storage Layer (AWS S3 + Iceberg) - Stores structured and unstructured data
Document Processing Layer - Retrieves, processes and enriches documents
Vector Embedding Layer (FAISS) - Stores vector embeddings for semantic search
Query Processing Layer - Handles user questions and retrieves relevant context
Response Generation Layer - Creates natural language responses

Implementation

The Foundation - Data Ingestion with Airbyte

Before implementing an AI agent, we need a solid data foundation. This is where Airbyte becomes essential.

Airbyte creates robust data pipelines that connect to hundreds of sources including Salesforce, Google Drive, and various business systems. It:

Loads information reliably into AWS S3 buckets, enabling Iceberg table formats
Maintains data freshness through scheduled syncs and comprehensive monitoring

With this foundation established, you're ready to build conversational AI that truly understands your business context.

Document Processing for Vector Embedding

For effective vector search, we must first perform proper document preprocessing:

# Create structured document (Object) with source metadata
doc = Document(
    page_content=content,
    metadata={"source": "google_drive", "type": "contract", "id": doc_id}
)
documents.append(doc)

This enrichment adds essential metadata to each document, enabling better filtering and contextual relevance in responses. Before any vector operations, we need these well-structured document objects that maintain source information and other critical attributes.

Bringing in FAISS - The Vector Processing Layer

The Vector Processing Layer creates searchable knowledge through several critical steps:

Documents are retrieved via Athena queries from AWS S3
Text is chunked using RecursiveCharacterTextSplitter (1000-token chunks with 100-token overlap) - see architecture diagram above!
Documents are transformed into 1536-dimensional vectors using OpenAI embeddings
These vectors are stored in a FAISS in-memory index with persistent serialization to disk
Entity relationships are detected through vector similarity

FAISS Implementation Details

Now that we have properly processed documents, here's how we initialize the vector store:

# Initialize vector store from disk cache or create new
if os.path.exists(vector_store_path):
    vector_store = FAISS.load_local(vector_store_path, embeddings)
else:
    # Create fresh vector store from processed documents
    documents = fetch_and_process_data()
    vector_store = FAISS.from_documents(documents, embeddings)
    # Persist to disk for future use
    vector_store.save_local(vector_store_path)

This approach combines in-memory performance with disk persistence, allowing the system to restart quickly without reprocessing data. This is different from traditional vector stores which may require more configuration.

Intelligent Question Answering

With our vector store ready, we can implement adaptive retrieval based on question complexity:

# Determine query type through semantic pattern matching
is_relationship_query = any(phrase in question.lower() for phrase in relationship_patterns)
is_analytical_query = any(phrase in question.lower() for phrase in analytical_patterns)

# Retrieve more context for complex questions, less for simple ones
k_value = 20 if is_relationship_query or is_analytical_query else 10
docs = vector_store.similarity_search(question, k=k_value)

This approach balances retrieval depth with performance, ensuring complex questions get sufficient context while simple queries remain lightning-fast.

FAISS Resilience & AWS Token Refresh

A major advantage of FAISS is its resilience through potential credential issues. Imagine having to deal with a bunch of painful authentication errors every time you run your application - this would save you a lot of energy!

try:
    # Normal AWS data access flow
    results = query_aws_data(question)
    return generate_response(results)
except CredentialError:
    # Fallback to cached FAISS index when AWS is unavailable
    results = vector_store.similarity_search(question)
    return generate_response(results, using_cached=True)

This design ensures uninterrupted service even during authentication challenges, providing a seamless user experience regardless of backend connectivity status.

Connecting with AI Interfaces

To make this solution accessible to end users, the FAISS-powered backend can be connected to various AI interfaces. For details on implementing this via Model Context Protocol (MCP), see our article on integrating MCP with Airbyte.

Performance Considerations

The in-memory approach with FAISS offers several practical advantages:

Response Time: FAISS typically provides faster query speeds than traditional database approaches, especially for real-time applications
Infrastructure Simplicity: Reducing external dependencies can streamline your architecture and maintenance requirements
Development Experience: Integration with existing AWS infrastructure can be more straightforward than adopting new vector database services
Resilience: The local caching mechanism helps maintain availability during temporary connectivity issues

Conclusion: Simplifying Data Access Through Conversation

In-memory vector stores like FAISS offer a reasonable approach to building conversational interfaces for your data. They provide speed and simplicity advantages, especially when working with data already in AWS environments. By combining this approach with proper document preprocessing and embedding techniques, you can create powerful question-answering systems without excessive infrastructure complexity.

The real value here is making your organization's knowledge more accessible. When people across your company can simply ask questions and get relevant answers from your data, you can actually see the valuable insights gained!

For this system to work effectively, you need data flowing reliably from your various sources into your data lake. That's where data pipeline tools like Airbyte can help – they handle the extraction and loading processes, ensuring your vector store has access to fresh, relevant information from across your organization!

If you're interested in exploring this approach further, check out the GitHub repositories below for code examples and implementation details!

Github Repos for reference: