CocoIndex

Overview

CocoIndex is an ultra-performant real-time data transformation framework for AI with incremental processing. This skill enables building indexing flows that extract data from sources, apply transformations (chunking, embedding, LLM extraction), and export to targets (vector databases, graph databases, relational databases).

Core capabilities:

1. Write indexing flows - Define ETL pipelines using Python
2. Create custom functions - Build reusable transformation logic
3. Operate flows - Run and manage flows using CLI or Python API

Key features:

• Incremental processing (only processes changed data)
• Live updates (continuously sync source changes to targets)
• Built-in functions (text chunking, embeddings, LLM extraction)
• Multiple data sources (local files, S3, Azure Blob, Google Drive, Postgres)
• Multiple targets (Postgres+pgvector, Qdrant, LanceDB, Neo4j, Kuzu)

For detailed documentation: https://cocoindex.io/docs/ Search documentation: https://cocoindex.io/docs/search?q=url%20encoded%20keyword

When to Use This Skill

Use when users request:

• "Build a vector search index for my documents"
• "Create an embedding pipeline for code/PDFs/images"
• "Extract structured information using LLMs"
• "Build a knowledge graph from documents"
• "Set up live document indexing"
• "Create custom transformation functions"
• "Run/update my CocoIndex flow"

Flow Writing Workflow

Step 1: Understand Requirements

Ask clarifying questions to understand:

Data source:

• Where is the data? (local files, S3, database, etc.)
• What file types? (text, PDF, JSON, images, code, etc.)
• How often does it change? (one-time, periodic, continuous)

Transformations:

• What processing is needed? (chunking, embedding, extraction, etc.)
• Which embedding model? (SentenceTransformer, OpenAI, custom)
• Any custom logic? (filtering, parsing, enrichment)

Target:

• Where should results go? (Postgres, Qdrant, Neo4j, etc.)
• What schema? (fields, primary keys, indexes)
• Vector search needed? (specify similarity metric)

Step 2: Set Up Dependencies

Guide user to add CocoIndex with appropriate extras to their project based on their needs:

Required dependency:

• cocoindex - Core functionality, CLI, and most built-in functions

Optional extras (add as needed):

• cocoindex[embeddings] - For SentenceTransformer embeddings (when using SentenceTransformerEmbed)
• cocoindex[colpali] - For ColPali image/document embeddings (when using ColPaliEmbedImage or ColPaliEmbedQuery)
• cocoindex[lancedb] - For LanceDB target (when exporting to LanceDB)
• cocoindex[embeddings,lancedb] - Multiple extras can be combined

What's included:

• Base package: Core functionality, CLI, most built-in functions, Postgres/Qdrant/Neo4j/Kuzu targets
• embeddings extra: SentenceTransformers library for local embedding models
• colpali extra: ColPali engine for multimodal document/image embeddings
• lancedb extra: LanceDB client library for LanceDB vector database support

Users can install using their preferred package manager (pip, uv, poetry, etc.) or add to pyproject.toml.

For installation details: https://cocoindex.io/docs/getting_started/installation

Step 3: Set Up Environment

Check existing environment first:

1. Check if COCOINDEX_DATABASE_URL exists in environment variables

   • If not found, use default: postgres://cocoindex:cocoindex@localhost/cocoindex

2. For flows requiring LLM APIs (embeddings, extraction):

   • Ask user which LLM provider they want to use:
     • OpenAI - Both generation and embeddings
     • Anthropic - Generation only
     • Gemini - Both generation and embeddings
     • Voyage - Embeddings only
     • Ollama - Local models (generation and embeddings)
   • Check if the corresponding API key exists in environment variables
   • If not found, ask user to provide the API key value
   • Never create simplified examples without LLM - always get the proper API key and use the real LLM functions

Guide user to create .env file:

# Database connection (required - internal storage)
COCOINDEX_DATABASE_URL=postgres://cocoindex:cocoindex@localhost/cocoindex

# LLM API keys (add the ones you need)
OPENAI_API_KEY=sk-...          # For OpenAI (generation + embeddings)
ANTHROPIC_API_KEY=sk-ant-...   # For Anthropic (generation only)
GOOGLE_API_KEY=...             # For Gemini (generation + embeddings)
VOYAGE_API_KEY=pa-...          # For Voyage (embeddings only)
# Ollama requires no API key (local)

For more LLM options: https://cocoindex.io/docs/ai/llm

Create basic project structure:

# main.py
from dotenv import load_dotenv
import cocoindex

@cocoindex.flow_def(name="FlowName")
def my_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    # Flow definition here
    pass

if __name__ == "__main__":
    load_dotenv()
    cocoindex.init()
    my_flow.update()

Step 4: Write the Flow

Follow this structure:

@cocoindex.flow_def(name="DescriptiveName")
def flow_name(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    # 1. Import source data
    data_scope["source_name"] = flow_builder.add_source(
        cocoindex.sources.SourceType(...)
    )

    # 2. Create collector(s) for outputs
    collector = data_scope.add_collector()

    # 3. Transform data (iterate through rows)
    with data_scope["source_name"].row() as item:
        # Apply transformations
        item["new_field"] = item["existing_field"].transform(
            cocoindex.functions.FunctionName(...)
        )

        ...

        # Nested iteration (e.g., chunks within documents)
        with item["nested_table"].row() as nested_item:
            # More transformations
            nested_item["embedding"] = nested_item["text"].transform(...)

            # Collect data for export
            collector.collect(
                field1=nested_item["field1"],
                field2=item["field2"],
                generated_id=cocoindex.GeneratedField.UUID
            )

    # 4. Export to target
    collector.export(
        "target_name",
        cocoindex.targets.TargetType(...),
        primary_key_fields=["field1"],
        vector_indexes=[...]  # If needed
    )

Key principles:

• Each source creates a field in the top-level data scope
• Use .row() to iterate through table data
• CRITICAL: Always assign transformed data to row fields - Use item["new_field"] = item["existing_field"].transform(...), NOT local variables like new_field = item["existing_field"].transform(...)
• Transformations create new fields without mutating existing data
• Collectors gather data from any scope level
• Export must happen at top level (not within row iterations)

Common mistakes to avoid:

❌ Wrong: Using local variables for transformations

with data_scope["files"].row() as file:
    summary = file["content"].transform(...)  # ❌ Local variable
    summaries_collector.collect(filename=file["filename"], summary=summary)

✅ Correct: Assigning to row fields

with data_scope["files"].row() as file:
    file["summary"] = file["content"].transform(...)  # ✅ Field assignment
    summaries_collector.collect(filename=file["filename"], summary=file["summary"])

❌ Wrong: Creating unnecessary dataclasses to mirror flow fields

from dataclasses import dataclass

@dataclass
class FileSummary:  # ❌ Unnecessary - CocoIndex manages fields automatically
    filename: str
    summary: str
    embedding: list[float]

# This dataclass is never used in the flow!

Step 5: Design the Flow Solution

IMPORTANT: The patterns listed below are common starting points, but you cannot exhaustively enumerate all possible scenarios. When user requirements don't match existing patterns:

1. **Combine elements from multipl