How to Troubleshoot CocoIndex Connector Errors: 6 Common Issues and Fixes

Most CocoIndex connector errors stem from invalid identifiers, missing vector schemas, unloaded extensions, or network misconfigurations, and surface as Python ValueError or RuntimeError exceptions with specific validation messages.

CocoIndex connectors are thin, async-first adapters that translate CocoIndex target definitions into concrete operations on external systems like PostgreSQL, SQLite, and Neo4j. When you troubleshoot CocoIndex connector errors, you are typically tracing Python exceptions raised during validation, schema creation, or extension loading in the cocoindex-io/cocoindex repository.

1. Fix Invalid Identifier Validation Errors

Many connectors including surrealdb, neo4j, falkordb, and doris enforce strict naming conventions through the _validate_identifier helper function. This validation raises a ValueError when table names, column names, or graph identifiers contain illegal characters or empty strings.

In cocoindex/connectors/surrealdb/_target.py at line 74, the validation logic explicitly checks identifier patterns:

def _validate_identifier(name: str, kind: str) -> None:
    # raises ValueError on empty or malformed identifiers

Common symptoms and fixes:

• ValueError: Invalid SurrealDB table name … occurs when names contain spaces or start with digits. Verify strings passed to declare_table or declare_row match the regex [A-Za-z_][A-Za-z0-9_]*.
• ValueError: column name … is invalid indicates prohibited characters like hyphens. Ensure column names use only alphanumeric characters and underscores.

2. Resolve Vector Schema and Extension Errors

Connectors supporting vector search (postgres, sqlite, doris) require explicit VectorSchemaProvider definitions for NumPy ndarray types. Additionally, SQLite requires the optional sqlite-vec extension for vector operations.

In cocoindex/connectors/postgres/_target.py (lines 11, 850-860), the _get_type_mapping function validates vector specifications:

# Raises ValueError when NumPy array lacks VectorSchema or has invalid dimensions

For SQLite, cocoindex/connectors/sqlite/_target.py (lines 46-53) contains the _create_table method which raises RuntimeError when vector columns exist but the extension is missing.

Troubleshooting checklist:

Symptom	Root Cause	Solution
ValueError: VectorSpecProvider is required for NumPy ndarray type.	Missing VectorSchema for ndarray columns	Supply vector_schema=VectorSchema(size=384, dtype=np.float32) in table declarations
RuntimeError: sqlite-vec extension required for vec0 virtual tables	Extension not loaded	Call coco.connect(..., load_vec=True) or manually load vec0
ValueError: Invalid pgvector dimension: 0	Zero or negative vector size	Ensure VectorSchema.size is a positive integer

3. Correct Primary Key and Column Definition Mismatches

Connectors validate that every primary key column exists in the declared schema. Missing keys trigger ValueError during TableSchema construction.

As implemented in cocoindex/connectors/sqlite/_target.py (lines 13-18):

if pk not in self.columns:
    raise ValueError(f"Primary key column '{pk}' not found ...")

Fix primary key errors by ensuring all entries in primary_key=['id'] have corresponding ColumnDef declarations, or correct typos in column names.

4. Handle Extension Loading Failures

SQLite and Doris connectors depend on optional native extensions (sqlite-vec, doris-udf). If these binaries (.so or .dll) are unavailable, the connector raises RuntimeError.

Verify extension availability before running pipelines:

import sqlite3

def assert_vec_loaded(conn: sqlite3.Connection) -> None:
    conn.enable_load_extension(True)
    try:
        conn.load_extension("vec0")
    except sqlite3.OperationalError as exc:
        raise RuntimeError("sqlite-vec extension missing") from exc

# Test connection

conn = sqlite3.connect(":memory:")
assert_vec_loaded(conn)

To enable automatic loading in CocoIndex, use the connector's load_extension flags:

import cocoindex as coco
from cocoindex.connectors import sqlite

env = coco.Environment(
    connect=lambda: sqlite.connect("my.db", load_vec=True)
)

5. Diagnose Network and Authentication Failures

Remote connectors (PostgreSQL, Neo4j, Kafka, Qdrant) propagate underlying library exceptions directly without wrapping. You will see original stack traces from asyncpg, neo4j-python-driver, or aiohttp.

Common connection errors:

• asyncpg.exceptions.InvalidPasswordError: Verify dsn, user, and password parameters in coco.connect.
• ConnectionRefusedError from Kafka: Check broker addresses, firewall rules, and TLS settings.
• aiohttp.ClientConnectorError from Qdrant: Confirm the HTTP endpoint URL and port configuration.

Code Examples for Common Fixes

Catching Identifier Errors in SurrealDB

import cocoindex as coco
from cocoindex.connectors import surrealdb

try:
    target = await coco.use_mount(
        surrealdb.declare_table,
        "my table",               # ← illegal space

        columns={"id": int, "name": str},
    )
except ValueError as e:
    # e.g., "Invalid SurrealDB table name: my table"

    print("Fix the name:", e)

Enabling sqlite-vec for Vector Tables

import cocoindex as coco
from cocoindex.connectors import sqlite
import numpy as np

# Load the sqlite-vec extension automatically

env = coco.Environment(
    connect=lambda: sqlite.connect("my.db", load_vec=True)   # <- important

)

# Declare a table with a vector column

await env.use_mount(
    sqlite.declare_table,
    "embeddings",
    columns={
        "id": int,
        "vector": np.ndarray,            # vector column

    },
    vector_schema=coco.vector_schema(384, dtype=np.float32),
)

Handling Missing PostgreSQL Vector Schema

import cocoindex as coco
from cocoindex.connectors import postgres
import numpy as np

try:
    await coco.use_mount(
        postgres.declare_table,
        "items",
        columns={"id": int, "embedding": np.ndarray},
        # OOPS: forgetting vector_schema argument

    )
except ValueError as e:
    # "VectorSpecProvider is required for NumPy ndarray type."

    print("Add vector_schema:", e)

Summary

• Identifier validation in connectors like SurrealDB and Neo4j raises ValueError for names containing spaces or special characters.
• Vector columns require explicit VectorSchema providers and, for SQLite, the sqlite-vec extension loaded via load_vec=True.
• Primary key constraints must reference existing columns in the schema definition.
• Native extensions must be present in the runtime environment and explicitly enabled.
• Network errors pass through from underlying drivers—check credentials and connectivity directly.

Frequently Asked Questions

Why does CocoIndex raise "VectorSpecProvider is required" when using NumPy arrays?

This error occurs in cocoindex/connectors/postgres/_target.py when you declare a column as np.ndarray without providing a vector_schema parameter. The connector requires explicit dimension and dtype specifications to map the array to database vector types like pgvector. Add vector_schema=VectorSchema(size=384, dtype=np.float32) to your table declaration.

How do I fix "sqlite-vec extension required" errors in SQLite?

The RuntimeError originates from cocoindex/connectors/sqlite/_target.py lines 46-53 when creating virtual tables with vector columns. Install the sqlite-vec extension binary for your platform, then enable it by passing load_vec=True to the SQLite connection: sqlite.connect("db.sqlite", load_vec=True).

What causes "Primary key column not found" validation errors?

This ValueError fires during TableSchema construction when your primary_key list references a column name not present in the columns dictionary. Verify that every primary key string exactly matches a key in your column definitions, including case sensitivity.

Are network authentication errors wrapped by CocoIndex connectors?

No. Connectors for PostgreSQL, Neo4j, and Qdrant propagate native library exceptions such as asyncpg.exceptions.InvalidPasswordError or aiohttp.ClientConnectorError directly. Troubleshoot these by testing connections with the underlying driver outside of CocoIndex first.

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}