ExamplescriptintermediateRunnableresearch-brief

Search

Runnable example (intermediate) for script using docling, ipykernel.

Key Facts

Level: intermediate
Runtime: Python • OpenAI API
Pattern: Context-backed research with explicit evidence
Interaction: Live sandbox • Script
Updated: 14 March 2026

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Library
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Reopen the wider library to compare adjacent patterns and linked learning paths.
Interaction
Run sandbox now
Try the interaction directly in this example’s guided sandbox surface.
Source
Open full source
Read the real implementation, highlighted checkpoints, and runtime requirements.
MCP
Call via MCP
Use the same resource inside agents, deterministic exports, and MCP setup flows.

Linked principles

Apply progressive disclosure to system agency Expose meaningful operational state, not internal complexity Establish trust through inspectability

High-level flow

How this example moves from input to execution and reviewable output

Search -> Retrieve relevant context -> User request -> System execution -> Reviewable output -> Apply progressive disclosure to…

Trigger

Runtime

Retrieve relevant context

Outcome

User request

Why this page exists

This example is shown as both real source code and a product-facing interaction pattern so learners can connect implementation, UX, and doctrine without leaving the library.

Visual flowReal sourceSandbox or walkthroughMCP access

How should this example be used in the platform?

Use the sandbox to understand the experience pattern first, then inspect the source to see how the product boundary, model boundary, and doctrine boundary are actually implemented.

UX pattern: Context-backed research with explicit evidence

Apply progressive disclosure to system agency

Expose meaningful operational state, not internal complexity

Establish trust through inspectability

Source references

Library entry: knowledge-docling-4-search
Source path: content/example-library/sources/knowledge/docling/4-search.py
Libraries: docling, ipykernel, lancedb, openai, pydantic, python-dotenv, requests, streamlit, tiktoken
Runtime requirements: OPENAI_API_KEY, streamlit
Related principles: Apply progressive disclosure to system agency, Expose meaningful operational state, not internal complexity, Establish trust through inspectability

4-search.py

content/example-library/sources/knowledge/docling/4-search.py

python

import lancedb

# --------------------------------------------------------------
# Connect to the database
# --------------------------------------------------------------

uri = "data/lancedb"
db = lancedb.connect(uri)


# --------------------------------------------------------------
# Load the table
# --------------------------------------------------------------

table = db.open_table("docling")


# --------------------------------------------------------------
# Search the table
# --------------------------------------------------------------

result = table.search(query="what's docling?", query_type="vector").limit(3)
result.to_pandas()

What should the learner inspect in the code?

Look for the exact place where system scope is bounded: schema definitions, prompt framing, runtime configuration, and the call site that turns user intent into a concrete model or workflow action.

Look for output contracts and validation

Look for the exact execution call

Look for what the product could expose to the user

How does the sandbox relate to the source?

The sandbox should make the UX legible: what the user sees, what the system is deciding, and how the result becomes reviewable. The source then shows how that behavior is actually implemented.

Enter a question or load a sample query.

Run the search or retrieval step.

Review the final brief with sources and retrieved context.

SandboxContext-backed research with explicit evidence

Research brief lab

This sandbox shows how a search or retrieval request should expose query planning, retrieved context, and the final answer.

UX explanation

The user should not only see a final answer. The product should reveal what was searched, what context shaped the response, and where the system boundary stops.

AI design explanation

These examples combine retrieval, web search, or file context with a synthesis step. The best surface exposes search plan, useful evidence, and a reviewable output.

Interaction walkthrough

1Enter a question or load a sample query.
2Run the search or retrieval step.
3Review the final brief with sources and retrieved context.

Principles in focus

Apply progressive disclosure to system agency Expose meaningful operational state, not internal complexity Establish trust through inspectability

Research question

Search planRetrieved context

Plan

The search plan appears here.

Final brief

The final brief appears here alongside the context used.

Used in courses and paths

This example currently stands on its own in the library, but it still connects to the principle system and the broader example family.

Related implementation patterns

Related principles

Runtime architecture

Use this example in your agents

This example is also available through the blueprint’s agent-ready layer. Use the For agents page for the public MCP, deterministic exports, and Claude/Cursor setup.

Define triggers, context, and boundaries before increasing autonomy

Make control, observability, and recovery explicit in the runtime

Choose the right operational patterns before delegating to workflows

Open For agents