LlamaIndex for RAG: Integration & Indexing Services

LlamaIndex Integration for RAG Systems

Insurance company operators were spending up to 12 minutes searching for answers among 15,000 pages of policies and instructions. We implemented LlamaIndex — time dropped to 1.5 minutes, accuracy rose to 91%, and outdated document errors fell from 8% to 0.4%. Over 5 years, we've completed 20+ RAG projects on LlamaIndex in finance, insurance, and retail. We guarantee accuracy of at least 90% on your data. Average savings at scale reach up to $18k–26k per year per department, with project payback in 3–4 months.

What Problems Does LlamaIndex Solve?

Scattered data sources: PDF, Word, HTML, databases. LlamaIndex connects 150+ formats through native loaders — no need to write adapters. Slow search: ordinary vector search doesn't understand compound queries. SubQuestionQueryEngine splits the question into parts and processes them in parallel. Missing context: LlamaIndex adds metadata (date, author, document type) and filters by it, excluding outdated or irrelevant sources.

How LlamaIndex Accelerates Search in Unstructured Data

LlamaIndex uses multi-level indexing. Documents are split into chunks (typically 512 tokens with 50% overlap). For each chunk, an embedding is generated (OpenAI text-embedding-3-small, 1536 dimensions). Vectors are stored in Qdrant or another store. On query, the LLM chooses a strategy: direct search, SubQuestionQueryEngine, or RouterQueryEngine — depending on complexity. A built-in reranker improves relevance of top-10 results.

Basic RAG with LlamaIndex

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Global settings
Settings.llm = OpenAI(model="gpt-4o", temperature=0)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
Settings.node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=50)

# Load documents
documents = SimpleDirectoryReader("./data", recursive=True).load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine(similarity_top_k=5)
response = query_engine.query("What is the warranty period for equipment?")
print(response)

# Access sources
for node in response.source_nodes:
    print(f"Score: {node.score:.3f}, Source: {node.metadata.get('file_name')}")

Integration with Vector Stores

from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import StorageContext
import qdrant_client

# Connect to Qdrant
client = qdrant_client.QdrantClient(url="http://localhost:6333")
vector_store = QdrantVectorStore(client=client, collection_name="docs")
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Index into Qdrant
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
    show_progress=True,
)

# Reload existing index
index = VectorStoreIndex.from_vector_store(vector_store)

Why Choose LlamaIndex for RAG?

LlamaIndex beats LangChain in tasks requiring deep document handling. Built-in SubQuestionQueryEngine and RouterQueryEngine don't need custom prompts — they're ready for complex queries immediately. IngestionPipeline caches processing, speeding up re-indexing by 60%. Additionally, LlamaIndex supports Retrieval-Augmented Fine-Tuning (LlamaIndex documentation): domain-specific embedding fine-tuning improves recall by 15–20%. In our projects, average response time for complex queries with SubQuestionQueryEngine is 40% faster than bare LangChain.

SubQuestionQueryEngine: Decomposing Complex Questions

from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool

# Create tools from different sources
financial_tool = QueryEngineTool.from_defaults(
    query_engine=financial_index.as_query_engine(),
    name="financial_data",
    description="Company financial metrics for the last three years",
)
contracts_tool = QueryEngineTool.from_defaults(
    query_engine=contracts_index.as_query_engine(),
    name="contracts",
    description="Supplier and customer contracts",
)

# SubQuestion engine automatically splits query into sub-queries
engine = SubQuestionQueryEngine.from_defaults(
    query_engine_tools=[financial_tool, contracts_tool],
    use_async=True,
)
response = engine.query(
    "Compare last quarter's revenue with budget and check for overdue payments on contracts"
)

# The agent creates 2 sub-queries and merges results

RouterQueryEngine: Routing Across Indexes

from llama_index.core.query_engine.router_query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector

router_engine = RouterQueryEngine(
    selector=LLMSingleSelector.from_defaults(),
    query_engine_tools=[
        QueryEngineTool.from_defaults(
            query_engine=summary_index.as_query_engine(response_mode="tree_summarize"),
            description="For summarization questions about the overall document",
        ),
        QueryEngineTool.from_defaults(
            query_engine=vector_index.as_query_engine(),
            description="For searching specific facts and details",
        ),
    ],
)

IngestionPipeline: Advanced Preprocessing

from llama_index.core.ingestion import IngestionPipeline, IngestionCache
from llama_index.core.node_parser import SentenceSplitter, SemanticSplitterNodeParser
from llama_index.core.extractors import TitleExtractor, QuestionsAnsweredExtractor
from llama_index.core.vector_stores import SimpleVectorStore

pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=512, chunk_overlap=64),
        TitleExtractor(nodes=3),  # Adds document title to each chunk's metadata
        QuestionsAnsweredExtractor(questions=5),  # Generates hypothetical questions for HyDE
        OpenAIEmbedding(model="text-embedding-3-small"),
    ],
    vector_store=vector_store,
    cache=IngestionCache(),  # Caches processed documents
)

nodes = await pipeline.arun(documents=documents, show_progress=True)

Practical Case: Enterprise Knowledge Base for an Insurance Company

Initial situation: 15,000 pages of documents (policies, insurance rules, regulatory instructions, internal regulations). Operators spent 8–12 minutes finding an answer to a customer query.

LlamaIndex architecture (our project):

• Sources: 4 document types in separate indexes in Qdrant
• RouterQueryEngine: routing by question type
• SubQuestionQueryEngine: for questions covering multiple types
• IngestionPipeline: automatic re-indexing on document updates
• Metadata filtering: by insurance type, document date, regional regulator

Results:

• Average operator response time: 10 min → 1.5 min
• Answer accuracy (expert evaluation): 91%
• Erroneous links to outdated policy versions: ~8% → 0.4%
• Document coverage: 73% (previously, operators were unaware of many documents)

LlamaIndex vs LangChain for RAG

Typical Implementation Scenarios for LlamaIndex

What's Included in the Work

1. Data source audit — identify document types, volume, update frequency.
2. Index design — choose chunker, embedding model, vector store.
3. Retrieval pipeline setup — configure RouterQueryEngine, SubQuestionQueryEngine, reranking.
4. Infrastructure integration — connect API, CI/CD, monitoring dashboard (latency p99, recall).
5. Team training — documentation and workshop on using the system.

Estimated Timelines

• Basic RAG with LlamaIndex: from 3 to 5 days
• Multi-source RAG with RouterQueryEngine: from 1 to 2 weeks
• IngestionPipeline with automatic updates: from 1 week
• Domain-specific embedding fine-tuning: from 2 to 3 weeks

Exact timelines are calculated after a data audit. We'll assess your project in 1 day — contact us. Request a data audit and receive a commercial proposal. We guarantee payback in 3–4 months through reduced search time: savings up to $18k–26k per year on operators.