Unstructured shipping manifest
AI Models & Assistants / Knowledge library
LlamaIndex
document ingestion indexing retrieval and llm workflows
Why learn LlamaIndex?
Useful for document-backed assistants and retrieval pipelines. Data permissions, chunking, and retrieval evaluation remain application responsibilities.
Where it came from
LlamaIndex provides tools for connecting data to language-model applications, including ingestion, indexing, retrieval, and workflow components.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Ingest a small corpus with source identifiers.
- Choose a chunking strategy and inspect boundaries.
- Retrieve passages for questions with known answers.
- Generate answers that retain source references.
- Evaluate retrieval misses separately from generation errors.
Documentation and sources
- Official documentation (developers.llamaindex.ai)
Catalog research notes
- Research snapshot: September 9, 2026. This entry summarizes the linked primary documentation; it does not assert a latest release version.
Connect the ideas
Explore next
Real World Example
Automated Logistics Compliance Extraction
Global logistics firms managing thousands of unstructured shipping manifests and customs declarations.
The business problem
Manual data entry from scanned, non-standardized PDF documents leads to high operational costs and frequent compliance errors.
How this topic solves it
Utilize LlamaIndex with LlamaParse to perform agentic OCR on complex documents, converting them into structured Markdown for indexing and automated retrieval by an LLM-powered agent.
- Install the LlamaIndex framework and LlamaCloud SDK.
- Upload raw PDF manifests to LlamaParse for agentic OCR processing.
- Convert parsed Markdown pages into LlamaIndex Document objects.
- Initialize a VectorStoreIndex to enable semantic search over the parsed data.
- Wrap the index as a tool for an autonomous agent to query specific compliance fields.
The flow from raw document input to agentic query response.
Agentic OCR engine
Semantic search index
LLM-powered assistant
- Raw PDF → extracts markdown → LlamaParse
- LlamaParse → ingests documents → VectorStoreIndex
- VectorStoreIndex → provides context → Agent
Implementation example
This code demonstrates the ingestion pipeline for Automated Logistics Compliance Extraction by parsing raw PDFs into structured documents and indexing them for the agent.
from llama_cloud import LlamaCloud
from llama_index.core import VectorStoreIndex, Document
# Initialize client for parsing
client = LlamaCloud()
# Parse complex PDF into markdown
result = client.parsing.parse(file_id="manifest_001", tier="agentic")
documents = [Document(text=p.markdown) for p in result.markdown.pages]
# Create index for agent retrieval
index = VectorStoreIndex.from_documents(documents)Expected business outcome
Reduced manual processing time and improved accuracy in identifying compliance discrepancies across diverse document formats.
Limitations and checks
The reliance on cloud-based parsing for complex documents introduces a latency trade-off; verify performance by monitoring the parsing duration of high-page-count manifests.