A set of self-contained Jupyter notebooks that get you productive with the
Bigdata.com REST API — from your first call to
AI-powered research workflows. Every notebook uses plain HTTP (requests), so
the patterns translate to any language.
| # | Notebook | What you'll learn |
|---|---|---|
| 01 | 01_setup_and_authentication.ipynb |
Register, create an API key, authenticate |
| 02 | 02_knowledge_graph.ipynb |
Find companies by details; resolve by ISIN/CUSIP/SEDOL/listing |
| 03 | 03_search.ipynb |
Fast vs. smart search; entity/keyword/sentiment/date/type filters |
| 04 | 04_research.ipynb |
The AI Research Agent (streamed, cited answers) |
| 05 | 05_workflows.ipynb |
Reproducible, templated research at scale |
Work through them in order — each builds on the last.
- Python 3.9+
- A Bigdata.com API key (notebook 01 shows how to create one in the Developer Platform)
pip install -r requirements.txt
# Provide your API key via an environment variable (never hard-code it):
export BIGDATA_API_KEY="your_api_key_here"
jupyter notebook # or: jupyter lab| Service | Host | Endpoint |
|---|---|---|
| Knowledge Graph | api.bigdata.com |
POST /v1/knowledge-graph/companies |
| Search | api.bigdata.com |
POST /v1/search |
| Research Agent | agents.bigdata.com |
POST /v1/research-agent (SSE) |
| Workflows | agents.bigdata.com |
POST /v1/workflow/execute (SSE) |
Authentication is via the X-API-KEY request header on every call.
- API docs: https://docs.bigdata.com/api-rest/introduction
- Getting started: https://docs.bigdata.com/getting-started/introduction
- Cookbooks: https://github.com/Bigdata-com/bigdata-cookbook (incl. Batch Search)
- Agent Swarm: https://agent-swarm.labs.bigdata.com/
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