datasource-connectors

name	datasource-connectors
description	Ingestion patterns for CSV, JSON, REST API, SQL, Excel, and Parquet -- guides an LLM through loading data from any common source
lastReviewed	2026-05-02T00:00:00.000Z

Datasource Connectors

Guide the user through loading data from any common source into a working dataset. This module is a decision framework, not a library. It tells the LLM what to ask, what to watch for, and how to handle each format's quirks.

When to Use

• The brief names a data source that is not yet loaded
• The user says "the data is in X" where X is a file, database, or API
• The orchestrator's plan includes an Ingest step

When to Skip

• Data is already loaded (inline table, prior step output, user pasted it)
• The brief says "data is pre-loaded" or "use this dataframe"

Connector Selection

Ask the user what format the data is in, or infer from the path/URL:

Signal	Connector
.csv, .tsv, .txt (tabular)	CSV
.json, .jsonl, .ndjson	JSON
http://, https:// + returns JSON/XML	REST API
Connection string, .sql, database name	SQL
.xlsx, .xls	Excel
.parquet, .arrow	Parquet

If ambiguous, ask. Do not guess the format.

CSV / TSV

What to ask

• Does the file have a header row? (default: yes)
• What is the delimiter? (auto-detect: comma, tab, semicolon, pipe)
• What is the encoding? (default: UTF-8; watch for: Latin-1, Windows-1252)

Quirks to handle

Problem	Detection	Fix
Wrong delimiter	First row has one column	Try tab, semicolon, pipe
Encoding garbled	Non-ASCII chars show as ? or é	Re-read as Latin-1 or Windows-1252
Trailing commas	Row has one extra empty column	Strip trailing delimiter
Quoted fields with commas	Standard CSV; most parsers handle it	Use RFC 4180 compliant parser
Mixed line endings	\r\n and \n in same file	Normalize to \n
BOM marker	\xEF\xBB\xBF at file start	Strip or let parser handle

Output

A table with typed columns. Report: row count, column count, detected delimiter, detected encoding.

JSON / JSONL

What to ask

• Is it a single JSON object, an array, or newline-delimited (JSONL)?
• What path holds the data? (e.g., data.results[], root array)

Quirks to handle

Problem	Detection	Fix
Nested objects	Column values are objects, not scalars	Flatten with dot notation (address.city)
Mixed types in array	Some items have extra/missing keys	Union all keys; fill missing with null
Large file (> 100MB)	Slow to parse	Stream with JSONL or chunk
Date strings	ISO 8601 or Unix timestamps	Parse to date; state the format detected

Output

A flat table. Nested objects flattened to dot-notation columns. Report: record count, nesting depth, keys found.

REST API

What to ask

1. What is the endpoint URL?
2. What authentication is needed? (none, API key, OAuth, bearer token)
3. Does it paginate? (offset, cursor, link header, or single page)
4. What response path holds the data? (e.g., results[])

Authentication patterns

Method	How to use	Security
API key in header	Authorization: Api-Key <key> or custom header	Never log the key
Bearer token	Authorization: Bearer <token>	Store in env var or secret
OAuth 2.0	Client credentials or authorization code flow	Use refresh tokens
No auth	Public API	Still rate-limit respectfully

Never hardcode credentials. Use environment variables or secret storage.

Pagination patterns

Pattern	Detection	Handling
Offset	?offset=0&limit=100	Increment offset until empty page
Cursor	Response has next_cursor field	Pass cursor to next request
Link header	Link: <url>; rel="next"	Follow rel="next" until absent
Page number	?page=1&per_page=50	Increment page until empty

Set a maximum page limit (default: 100 pages) to prevent runaway loops.

Retry and timeout

Rule	Value
Timeout per request	30 seconds
Retries on 429/5xx	3 with exponential backoff (1s, 2s, 4s)
Retries on network error	2
Never retry	400, 401, 403, 404 (client errors are not transient)

Output

A flat table from all pages combined. Report: total records, pages fetched, any errors skipped.

SQL

What to ask

1. What database engine? (PostgreSQL, SQL Server, MySQL, SQLite)
2. Connection string or host/port/database?
3. What table or query?

Security rules

Rule	Reason
Always use parameterized queries	Prevents SQL injection
Never interpolate user input into SQL	Even for table names; use allowlists
Read-only connection	The pipeline only reads; never write
Credentials in env vars	Never in code, never in logs

Query patterns

-- Simple table read
SELECT * FROM schema.table_name LIMIT 1000;

-- Filtered read (parameterized)
SELECT * FROM sales WHERE region = @region AND date >= @start_date;

-- Aggregated read (push aggregation to the database when possible)
SELECT region, month, SUM(revenue) AS revenue
FROM sales
GROUP BY region, month;

Push aggregation and filtering to the database when possible. Pulling raw data and aggregating in the LLM wastes tokens and is slower.

Output

A table. Report: row count, column count, query execution time, database engine.

Excel

What to ask

1. Which sheet? (default: first sheet)
2. Does the data start at A1 or is there a header region to skip?
3. Are there named ranges?

Quirks to handle

Problem	Detection	Fix
Multiple sheets	Workbook has > 1 sheet	Ask which sheet; default to first
Header not in row 1	First rows are title/metadata	Ask for header row number
Merged cells	Values span multiple rows/columns	Unmerge and fill down
Formulas	Cells contain formulas, not values	Read values, not formulas
Mixed types in column	Numbers and text in same column	Coerce to string; flag for data-preparation
Date serial numbers	Dates stored as integers (e.g., 45678)	Convert using Excel epoch (1899-12-30)

Output

A table from the specified sheet and range. Report: sheet name, row count, column count, any merged cells detected.

Parquet / Arrow

What to ask

• Path to the file or directory (partitioned datasets use directories)

Advantages

Parquet is self-describing: schema, types, and encoding are embedded. No delimiter guessing, no encoding issues, no header detection.

Quirks to handle

Problem	Detection	Fix
Partitioned directory	Path is a directory with part-*.parquet files	Read all partitions
Nested columns (struct/list)	Schema shows complex types	Flatten or ask user which fields
Large file	> 1M rows	Sample or filter before loading all

Output

A typed table. Report: row count, column count, schema summary (types from the Parquet metadata).

Error Handling (all connectors)

Situation	Action
File not found	Report the exact path tried. Ask user to verify.
Permission denied	Report the error. Do not retry. Ask user to fix permissions.
Empty dataset (0 rows)	Report it. Push back: "file exists but has no data rows."
Partial data (truncated)	Report row count. Ask if this is expected.
Unsupported format	State what was detected. Ask user for the correct format.

Never fail silently. Every error must produce a specific, actionable message.

Output Contract

Pass to the next module (data-preparation):

• The loaded dataset (or a reference to it)
• A one-line summary: format, row count, column count, source path
• Any warnings (encoding fallback, partial data, skipped errors)