- name
- cognee-ingestion
- description
- Use when putting data into cognee memory with remember() — choosing inputs (text, files, folders, URLs, repos, databases), datasets and node_sets, loaders, ontologies, the graph extractor (LLM or GLiNER), chunking, dry-run cost estimates, or when remember() raises on a keyword argument.
Ingest data with remember()
remember() is cognee's ingestion API. One call stores the data, builds the
knowledge graph, and enriches it. Use it for all ingestion; every option in
this skill is a remember() argument unless it says otherwise.
import cognee
result = await cognee.remember("Einstein was born in Ulm.") # text
result = await cognee.remember(
["./notes.md", "./report.pdf"], # files
dataset_name="research",
)
print(result.status, result.dataset_id) # "completed", UUID
All cognee functions are async. Without dataset_name data goes to
main_dataset. Needs LLM_API_KEY unless you use the GLiNER extractor
(below).
Use it
Inputs
data accepts a string, a list of strings, file paths (absolute, file://,
s3://), http(s) URLs, binary streams, or a list mixing them.
- URLs are fetched and scraped (needs
ALLOW_HTTP_REQUESTS=true, the default). - Folders are ingested file by file. A folder that looks like a code
project, or a GitHub/GitLab URL, becomes one code repository (needs
giton PATH). - Code files (
.py,.ts,.go, …) go down the code-graph route: a deterministic graph, no LLM calls, searchable only withSearchType.CODE. To index a whole repository explicitly, passcontent_type="code". - Databases and dlt sources: a SQL connection string, a dlt
DltResource/DltSource, or a CSV. dlt is a core dependency, so no extra is needed (cognee[dlt]is an empty compatibility extra). Options:primary_key(default"id"),write_disposition("replace"default, or"append"),query,max_rows_per_table. - Skill playbooks (
SKILL.mdfiles):content_type="skills"; ingests into the target dataset (defaultmain_dataset), so passdataset_nameto keep skills in their own dataset.