LlamaIndex integration for RAG and data indexing

LlamaIndex: Why Knowledge Base Search Returns Garbage

The knowledge base is populated: policies, instructions, contracts, and ticket history. The assistant answers confidently — and misses the mark: instead of the return procedure, it quotes the acceptance section; instead of the internal policy, it summarizes a generic article. The cause is not the model, but the search layer that has not been built out before it.

Without indexing, documents remain a set of files: search runs on word matches rather than meaning, and the query “how to process a return” does not find the “termination procedure” section.

Chunking is not configured either — chunks are either ten pages long, and noise arrives in the context, or one line long, and the condition is torn from its definition. A working option is chunks of 300–500 words with overlap, so related parts do not drift apart.

The second half of the problem is the answer source.

Without metadata (document, revision, date, section) and a relevance threshold, duplicates and outdated versions enter the context; the model faithfully retells what it received, and produces two contradictory answers to one question. There is nothing to verify — there is no link to the chunk.

LlamaIndex covers exactly this layer: document indexing, chunking, ranking, and returning the source together with the answer. Below, we break down how this is configured and where it most often breaks.

What Managed Data Indexing Delivers in Practice

A single index changes the very way the knowledge base is used: instead of searching file names and department folders, the system answers a question with a document chunk.

Splitting text into chunks with overlap and linking each chunk to its source provides what searching by file names cannot — hitting the meaning, not a word match.

In practice, most of a specialist's time was consumed by manual search across dozens of revisions; after the index is built, this stage shrinks to a single query with link verification.

Search speed and the share of relevant hits are what we measure before putting it into operation.

We take a control set of real employee questions, run it, and calculate in what percentage of cases the needed document appeared in the top results and the answer was assembled from its chunks.

This gives a numerical basis for improving search: where synonyms are missing, where an outdated revision gets in the way, where a document is split too coarsely and the meaning becomes blurred.

Document version control is built into the assembly itself: the index is rebuilt on a source change event, old chunks are displaced, and a revoked revision cannot enter the answer.

This removes an entire class of errors that cause employees to stop trusting internal search — when the system confidently cites a document that is no longer in effect.

We separately address the “answer not from the source” class: every statement in the answer is backed by a link to a chunk indicating the document and location, and when there is no support, the system reports this instead of guessing.

Managed indexing on LlamaIndex is valuable here not in itself, but because the rules for chunking, updating, and retrieval are described explicitly and are reproduced on new datasets.

Before launch, a check is performed: the single index covers all agreed sources, updates occur on event, outdated revisions are excluded, the hit rate and search speed are measured on a control set, and the reduction in manual search is confirmed by timing typical tasks. If even one item is not met, the solution is not ready for use.

LlamaIndex Operating Modes: Comparing Approaches

An index in LlamaIndex is not just vectors, but a combination of nodes with metadata, the index itself, and the store it writes to.

The local option keeps everything in files next to the application; the external one moves nodes and vector representations into a separate database read by several services.

This fork determines who can write to the index, how quickly new documents appear in it, and what happens when rolling back an unsuccessful run.

In practice, the choice comes down to two questions: how often the corpus changes and how many services read the index.

The modes are compatible with each other — a local index is transferred to an external store without rebuilding, and a batch run is supplemented with streaming ingestion only for changed documents.

Implementation Stages: From Data Audit to Handover

We lock the route before the start; otherwise the index is assembled before the answer quality has been agreed upon. Each stage relies on the artifacts of the previous one, and validation runs on real work questions, not showcase examples.

1. Data source audit. We collect what exists: policies, knowledge base, tables, scanned correspondence. We record formats, owners, change frequency, and access rights — this determines what can be indexed and what must remain closed.
2. Index schema. On LlamaIndex, we describe what chunks a document is split into and what metadata is attached to them: section, date, user role. A large chunk blurs the answer, a small one loses context — we select the size by measurements, not by eye.
3. Search configuration. We assemble hybrid retrieval: semantic search plus search by exact wording and SKUs, with reranking. We select the number of chunks sent to the model: more means a more complete answer, but higher latency.
4. Validation on reference questions. We take 50–100 real employee questions with known answers and measure whether the needed source appears in the top results. We analyze discrepancies: usually they are chunking errors or unaccounted synonyms.
5. Refusal and access. We verify that the system responds “the sources contain no data” instead of making things up, and that an employee sees only the documents they are allowed to see. These scenarios break trust fastest.
6. Solution handover. We hand over configurations, the index schema, the reindexing policy, and instructions for the knowledge base owner, and show how to measure quality after changes yourself.

LlamaIndex Integration Artifacts: What You Keep

Integration is considered complete not when document search answers on demo examples, but when your team has a kit that can be reproduced without us.

Below is exactly what we hand over: every item can be opened, read, and changed by your own efforts, without reconstructing the logic from chat messages.

• Index configuration — parameters for splitting text into chunks, the selected embedding model, the similarity metric, and source weights. Everything in one file: changing the model or chunk size does not require edits to the application code.
• Document processing schema — where files are pulled from, how different formats are parsed, which fields go into metadata. This schema shows why a specific document did or did not appear in the results.
• Access rules — role-based separation at the metadata level: an employee sees only those chunks to which they have rights in the source system. The rules are kept separate from search logic and are checked by automated tests.
• Reindexing and quality control scripts — handling new and changed documents, a set of test questions with reference answers, and metrics for hitting the right chunk. A run takes minutes and shows degradation before users notice it.
• Solution documentation — a component map, external dependencies, failure points, and the procedure for rolling back to a previous index version. An engineer who did not participate in the project can deploy a copy of the environment using these instructions.
• Update policy — reindexing frequency, the person responsible for sources, actions when document formats change and when data volume grows. It defines what happens without our involvement and what requires further development.

We hand over each artifact in your repository and on your infrastructure — along with a breakdown of how to change it. Describe the task: we will return with an estimate and a plan for how the kit will fit into your processes.

Case Study: Cut the Share of Irrelevant Answers by a Factor of Three

The client was a support service with a distributed knowledge base: policies, articles, and correspondence on closed tickets.

Search across it answered confidently, but in 38% of cases relied on the wrong section, and operators manually rechecked every link — preparing an answer took up to six minutes.

What We Changed in Indexing

We built a single index on LlamaIndex instead of several disparate stores and rewrote data preparation: documents were split into semantic blocks with overlap so the answer would not cut off at a chunk boundary, and metadata was linked to each block — department, ticket type, and validity date.

Then we enabled hybrid search: vector retrieval is supplemented by lexical search on exact tariff wording and service names, and the retrieved chunks are reranked before being fed to the model. The answer is built only on the selected blocks and always with a link to the source.

How We Measured the Result

Before the changes, we assembled a reference set of two hundred operator questions with known correct sources — it became the ruler against which index versions were compared. Metrics were captured on every change, not once at the end.

The share of answers with an irrelevant source fell from 38% to 12%, top-3 retrieval hits grew from 54% to 89%, and the share of tickets closed without escalation to the second line rose from 61% to 84%. Answer preparation dropped from six minutes to two.

How Does Hybrid Search Differ from Vector Search?

Vector search compares meaning: the query and a knowledge base chunk are converted into vector representations, and similarity is calculated by the distance between them.

That is why “how to return a product” finds the article “return conditions,” even if not a single word matched. The weak spot is exact tokens: SKUs, error codes, and abbreviations are almost indistinguishable from one another in vector space.

Keyword search is structured as a mirror image: a lexical index with lemmatization and a frequency model catches a rare term precisely but misses paraphrases and synonyms.

In LlamaIndex, both modes are assembled as separate search branches, and hybrid search combines their results.

Hybrid mode runs a single query through both branches and merges the results: scores are normalized and combined with weights or merged by reciprocal rank fusion, after which the top candidates go to reranking. A query with a SKU is pulled up by the lexical branch, a semantic question by the vector one.

In terms of hit accuracy, the choice depends on traffic: for homogeneous queries without identifiers, the vector branch is enough, while for mixed ones, hybrid lifts into the top-3 what pure semantic search was losing.

Technical Doubts Before LlamaIndex Integration

Before integrating LlamaIndex, engineering teams ask the same questions: where the index will live, what will happen to the store as the database grows, how documents will be updated, and who will get access to the data. Below are answers on point, without generalities.

What will the load on the store be?

The index takes up noticeably more space than the source texts: vectors and chunk metadata are stored. We calculate the volume in advance and separate the hot index from the archive — database growth does not turn into an unmanageable avalanche.

How is the index updated?

Reindexing runs on a schedule or on a document change event, and only affected chunks are recalculated. A full rebuild remains a fallback and is performed in a separate environment without blocking answer retrieval.

How is data privacy ensured?

Access rights are inherited from the source: an employee sees in the answer only those chunks to which they have access. Queries and answers are logged, and the history remains with you.

How much effort does maintenance require?

The main things are data quality and drift control: answer hit metrics, monitoring of empty retrievals, and an update policy. Configured alerts remove most of the manual work.

Tell Us About the Task — We Will Return with an Estimate and Plan

To return with a work estimate and implementation plan, a minimum of inputs is needed — usually they are gathered in one pass and immediately eliminate half of the clarifying calls.

Below is a list of what speeds up project start: the more specific the inputs, the more accurately we name the stages, labor costs, and risks.

• Data composition. Which sources we index (file stores, knowledge base, internal portals, tables), in which formats, what the total volume is, and how often the content changes. Separately — who owns each source and through whom access is granted.
• Query scenarios. 15-30 real formulations from your employees, including those where search currently gives an incorrect result. We use them to configure document chunking and select search weights.
• Answer requirements. Whether a link to the primary source is needed, acceptable answer latency, output language, and what is considered an error: a missed fact or a confident incorrect answer.
• Deployment environment. Cloud or closed infrastructure, security service requirements, role-based access separation — this determines the index storage scheme and model access mode.
• Current developments. What you already use for search and vector storage, whether there are prototypes on LlamaIndex — we do not rewrite what works, we integrate it into the new scheme.
• Acceptance criteria. The share of correct answers on a test set of questions, response time, and a set of adversarial questions used to test robustness.
• People and timelines. Who makes decisions on the customer side, who tests, and by what date the working environment is needed.

Describe the task in free form — tables and exact numbers are optional at the first step. We will return with an estimate, a stage-by-stage implementation plan, and a list of inputs that were missing for the calculation.