Building Knowledge Graphs with Gemini
type: NewsArticle
description: A developer has demonstrated how to build structured knowledge graphs from unstructured documents using Google's Gemini AI model. The approach involves prototyping with Gemini to extract relationships and entities from raw text, then optimizing prompts and scaling up to process entire books or legal contracts. The method can visualize extracted narratives and contractual network graphs from documents.
date published: 2026-06-12T17:37:01+00:00
date modified: 2026-06-12T17:40:57+00:00
language: en
is accessible for free: true
article body: In this exploration, we'll see how to turn raw, unstructured documents into structured knowledge graphs using Gemini. We'll start by prototyping to develop our intuition. Then, we'll optimize our prompts and outputs, and finally scale up to process entire books or dense legal contracts. By the end, we'll even visualize extracted book narratives and contractual network graphs! *A few notes before we start:* Documents are everywhere. We use them for business, daily operations, legal matters, technical docs, education, and even just for fun. However, documents are not databases. They're generally unstructured, and fully understanding them requires multiple reading passes. So, can we extract structured knowledge from documents using only the following? Let's try with Gemini… We'll use the following packages: `google-genai` for calling Gemini with the `networkx` for graph managementWe'll also need: `tenacity` for request management (a dependency of `google-genai` )`matplotlib` and `pillow` for data visualization (dependencies of `networkx` ) ``` %pip install --quiet "google-genai>=2.6.0" "networkx[default]" ``` To use the Gemini API, we have two main options: **🛠️ Option 1 - Gemini API via Agent Platform** Requirements: Gen AI SDK environment variables: `GOOGLE_GENAI_USE_ENTERPRISE="True"` `GOOGLE_CLOUD_PROJECT="<PROJECT_ID>"` `GOOGLE_CLOUD_LOCATION="<LOCATION>"` 💡 For preview models, the location must be set to `global` . For generally available models, we can choose the closest location among the[Google model endpoint locations].ℹ️ Learn more about [setting up a project and a development environment]. **🛠️ Option 2 - Gemini API via Google AI Studio** Requirement: Gen AI SDK environment variables: `GOOGLE_GENAI_USE_ENTERPRISE="False"` `GOOGLE_API_KEY="<API_KEY>"` ℹ️ Learn more about [getting a Gemini API key from Google AI Studio]. 💡 You can store your environment configuration outside of the source code: | Environment | Method | |---|---| | IDE | `.env` file (or equiva…
publisher: Web Pulse
keywords: artificial-intelligence, large-language-models, generative-ai, natural-language-processing, ai-tools
mentions: Gemini
mentions: Gemini API
mentions: Agent Platform
mentions: Google Cloud
citation: CreativeWork