Draft a reply to the newest Gmail inbox message using Granola meeting notes and past emails stored in cognee. Incorporates prior discussions, commitments, and writing style, then displays the draft without sending it.
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Draft a reply to your newest Gmail email that knows what you discussed in your Granola meetings, what you promised the sender, and how you write, using cognee memory. Use when someone asks to answer or reply to their latest email in their own tone. Runs locally; reads Gmail read-only; the draft is printed, never sent.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Runs the personalized email cookbook in examples/cookbooks/personalized_email/. It
remembers your Granola meeting notes and your Gmail inbox and sent mail in the cognee
dataset personalized_email, then drafts a reply to the newest inbox email. Setup, the
scripts and what each one does are in the cookbook's
README.md.
Run every command from the repo root.
With neither Gmail nor Granola set up, the commands below run on a sample mailbox (the
[setup] line says so) and draft a reply to a fictional email; only LLM_API_KEY is
needed. Tell the user the reply is to sample data. Add --sample to use the sample even
when their accounts are set up.
uv run python examples/cookbooks/personalized_email/personalized_email.py --check.
[setup] MISSING: line names one fix. Don't create credentials yourself.
Tell the user exactly which line to fix, using the README's "What it needs" table,
and stop. If only the Granola line is missing, ask whether to run with --no-granola
instead.uv run python examples/cookbooks/personalized_email/personalized_email.py. Add
--days N if the user named a time range for meetings, and --no-granola if they
don't want Granola used.[draft] Reply:. Give it to the user, and say
which email it answers (the [draft] Answering: line) and which sources went in (the
[ingest_granola] and [ingest_email] lines). The draft is never sent: hand it to the
user to send.personalized_email.py exits 1 with a line naming what went wrong.
Run that script from examples/cookbooks/personalized_email/scripts/ on its own to
look closer.credentials.json, token.json or .env.personalized_email before it ingests (the [clear]
line). On the user's own data, pass --clear only when they asked to start over: it
forgets everything the dataset holds.uv run cognee-cli forget --dataset personalized_email
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Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.
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Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.
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Define custom entities and relationships in cognee, a memory system for AI applications. This skill guides model design, identity matching, searchable fields, and troubleshooting when entities are duplicated or relationships are missing.
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Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.
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Run cognee in Docker, starting with a prebuilt image for local document ingestion and questions. It also covers Docker Compose profiles for adding the UI, MCP server, Postgres, and Neo4j, plus persistent data storage.
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Find and remove stored content from cognee using its SDK, HTTP API, or CLI. Inspect documents before deletion, choose document or dataset removal, or keep raw files while clearing derived graph relationships and search vectors.
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