LlamaIndex — The Data Framework for RAG

The Data Problem in Enterprise AI

You now understand how Retrieval-Augmented GenerationRetrieval-Augmented Generation (RAG)A pattern that combines document retrieval with LLM generation. The system searches a knowledge base for relevant context, then feeds it to the model to produce grounded, accurate answers.See glossary works: convert your documents into embeddingsEmbeddingsNumerical representations (vectors) of text that capture semantic meaning. Similar concepts produce vectors that are close together, enabling machines to understand relationships between words, sentences, or documents.See glossary, store them in a vector database, and retrieve relevant context when the LLM needs it. The concept is straightforward. The reality is considerably more complex.

Your bank's knowledge is not sitting in one tidy folder. It is scattered across SharePoint libraries, Confluence wikis, regulatory databases, PDF archives on network drives, emails in Exchange, structured data in SQL databases, and countless other systems accumulated over decades. Much of it is locked inside difficult documents: scanned financial statements, multi-column annual reports, contracts with nested clauses. Getting this data into a format that an LLM can use -- parsed, cleaned, chunked, embedded, indexed, and kept current -- is the unglamorous but critical work that determines whether your RAG system actually works.

LlamaIndex was built to solve this problem. Today it plays two roles. The first is an open-source framework for building RAG and agent applications on your own data. The second is a commercial document-intelligence service: in February 2026 the company began renaming its enterprise platform from LlamaCloud to LlamaParse, which it describes as an agentic document processing platform. Its strength is turning messy PDFs, tables and forms -- financial statements, contracts, invoices -- into usable data. For banks, that second role is often the more valuable one.

Core Architecture

LlamaIndex organizes the data pipeline into three layers:

Data Connectors and Parsing

Data connectors handle the ingestion of data from source systems. LlamaIndex provides connectors for hundreds of data sources, including:

• Document stores: SharePoint, Google Drive, Confluence, Notion, Dropbox
• Databases: PostgreSQL, MySQL, MongoDB, Snowflake
• File formats: PDF, Word, Excel, PowerPoint, HTML, Markdown
• APIs and services: Slack, email (IMAP), web scraping, RSS feeds
• Specialized sources: SEC filings, financial data feeds, regulatory databases

Each connector handles the specific API calls, authentication, pagination, and data extraction required to pull content from that source. Parsing then turns what was pulled in into clean text and tables, keeping the document's structure -- headings, sections, table rows -- rather than flattening everything into one block of words.

Indexes

Once data is ingested, LlamaIndex organizes it into indexes -- data structures optimized for retrieval. Current practice centres on two:

• Vector Store Index: The standard approach -- chunks documents, generates embeddings, and stores them in a vector databaseVector DatabaseA specialized database optimized for storing and querying high-dimensional vectors (embeddings). Enables fast similarity search across millions of documents for RAG and recommendation systems.See glossary for semantic similarity search
• Keyword (BM25) retrieval: Matches exact terms, and is usually combined with the vector index so a single search catches both meaning and precise wording (hybrid search)

Older index types such as tree and keyword-table indexes are now treated as legacy. Document structure is preserved differently: by keeping the chapter and section hierarchy as metadata during parsing. Your compliance policy manual keeps its chapters, sections and subsections attached to every chunk, so an answer can cite "§4.2.3" -- while customer complaint data, which has no such structure, simply goes through semantic search.

Query Engines

Query engines sit on top of indexes and handle the logic of converting a user question into effective retrieval operations:

• Simple query: Convert the question to an embedding and find the closest matches
• Multi-step query: Break complex questions into sub-questions, retrieve for each, and synthesize
• Router query: Analyze the question and route it to the most appropriate index
• Sub-question query: Decompose a compound question ("Compare our CRE policy with the OCC guidance") into individual retrievals and combine results

Banking-Specific Value

Connecting Institutional Knowledge

The typical mid-size bank has policy documents in SharePoint, credit analysis templates in Excel, regulatory guidance bookmarked in PDF form, training materials in a learning management system, and institutional knowledge locked in email threads. LlamaIndex can connect to all of these, creating a unified knowledge layer that an LLM can search across.

Handling Complex Document Formats

Banking documents are notoriously complex -- nested tables in regulatory filings, multi-column layouts in annual reports, embedded charts in credit memos, scanned financial statements in loan files. This is where the commercial LlamaParse service focuses: extracting tables, fields and structure accurately enough that downstream retrieval and analysis can trust them. If a spreading analyst would not trust the extracted balance sheet, neither should your AI.

Chunking Strategy Flexibility

How you split documents into chunks dramatically affects retrieval quality. LlamaIndex provides multiple chunking strategies -- by paragraph, by section, by semantic boundary, with configurable overlap -- and the ability to use different strategies for different document types. Your regulatory guidance documents (long, structured) might need different chunking than your customer complaint records (short, unstructured).

LlamaIndex vs. LangChain

A common question is when to use LlamaIndex versus LangChain. The two used to divide the work neatly; today they overlap considerably:

• LlamaIndex remains strongest at the data layer -- parsing, ingestion, indexing, and retrieval -- and now has its own workflow and agent features
• LangChain reached version 1.0 in October 2025 and now positions itself as an agent engineering platform: LangGraph for running agents and LangSmith for monitoring them

Some production systems still use both: LlamaIndex for ingestion and indexing, LangChain or LangGraph for the agent workflow that queries the index. But because each can now do much of what the other does, many banks pick one as the standard and add the other only where it is clearly stronger -- the same way you would avoid running two core banking platforms for the same product line.

If your primary use case is document search and retrieval without complex multi-step workflows, LlamaIndex alone may be sufficient.