In the age of information overload, understanding what's trending—and why—is crucial for developers, researchers, and data engineers. HackerNews is one of the most influential tech communities, but manually tracking emerging topics across thousands of threads and comments is practically impossible.

What if you could automatically index HackerNews content, extract topics using AI, and query trending discussions in real-time? That's exactly what CocoIndex enables through its Custom Sources framework combined with LLM-powered extraction.

In this post, we'll explore the HackerNews Trending Topics example, a production-ready pipeline that demonstrates some of the most powerful concepts in CocoIndex: incremental data syncing, LLM-powered information extraction, and queryable indexes.

Custom Sources are one of the most powerful concepts in CocoIndex. They let you turn any API—internal or external — into a first-class, incremental data stream that the framework can automatically diff, track, and sync.

Think of it as React for data flows: you describe the shape of your data, and CocoIndex handles incremental updates, state persistence, lineage, and downstream sync. You get predictable, debuggable, fault-tolerant pipelines without the usual orchestration overhead.

In this example, we build a custom connector for HackerNews. It fetches recent stories + nested comments, indexes them, and exposes a simple search interface powered by Postgres full-text search.

If this example is helpful, we’d appreciate a ⭐ on CocoIndex GitHub!

Why Use a Custom Source?​

In many scenarios, pipelines don't just read from clean tables. They depend on:

• Internal REST services
• Partner APIs
• Legacy systems
• Non-standard data models that don’t fit traditional connectors

CocoIndex’s Custom Source API makes these integrations declarative, incremental, and safe by default. Instead of writing ad-hoc scripts, you wrap your API as a “source component,” and CocoIndex takes it from there.

Project Walkthrough — Building a HackerNews Index​

Goals​

1. Call HackerNews Search API
2. Fetch nested comments
3. Update only modified threads
4. Store content in Postgres
5. Expose a text search interface

CocoIndex handles change detection, idempotency, lineage, and state sync automatically.

Overview​

The pipeline consists of three major parts:

1. Define a custom source (HackerNewsConnector)
   • Calls HackerNews API
   • Emits rows for changed/updated threads
   • Pulls full thread + comment tree
2. Build an index with CocoIndex Flow
   • Collect thread content
   • Collect all comments recursively
   • Export to a Postgres table (hn_messages)
3. Add a lightweight query handler
   • Uses PostgreSQL full-text search
   • Returns ranked matches for a keyword query

Each cocoindex update only processes changed HN threads and keeps everything in sync.

The project is open source and available on GitHub.

Prerequisites​

• Install Postgres if you don't have one.

Defining the Data Model​

Every custom source defines two lightweight data types:

• Key Type → uniquely identifies an item
• Value Type → the full content for that item

In hacker news, each news is a thread, and each thread can have multiple comments.

For HackerNews, let’s define keys like this:

class _HackerNewsThreadKey(NamedTuple):
    """Row key type for HackerNews source."""
    thread_id: str

Keys must be:

• hashable
• serializable
• stable (doesn’t change over time)

Values hold the actual dataset:

@dataclasses.dataclass
class _HackerNewsComment:
    id: str
    author: str | None
    text: str | None
    created_at: datetime | None

@dataclasses.dataclass
class _HackerNewsThread:
    """Value type for HackerNews source."""
    author: str | None
    text: str
    url: str | None
    created_at: datetime | None
    comments: list[_HackerNewsComment]

This tells CocoIndex exactly what every HackerNews “item” looks like when fully fetched. _HackerNewsThread holds a post and all its comments, while _HackerNewsComment represents individual comments.

Building a Custom Source Connector​

A Custom Source has two parts:

1. SourceSpec — declarative configuration
2. SourceConnector — operational logic for reading data

Writing the SourceSpec​

A SourceSpec in CocoIndex is a declarative configuration that tells the system what data to fetch and how to connect to a source. It doesn’t fetch data itself — that’s handled by the source connector.

class HackerNewsSource(SourceSpec):
    """Source spec for HackerNews API."""
    tag: str | None = None
    max_results: int = 100

Fields:

• tag
  • Optional filter for the type of HackerNews content.
  • Example: "story", "job", "poll".
  • If None, it fetches all types.
• max_results
  • Maximum number of threads to fetch from HackerNews at a time.
  • Helps limit the size of the index for performance or testing.

Defining the connector​

Sets up the connector's configuration and HTTP session so it can fetch HackerNews data efficiently.

@source_connector(
    spec_cls=HackerNewsSource,
    key_type=_HackerNewsThreadKey,
    value_type=_HackerNewsThread,
)
class HackerNewsConnector:
    """Custom source connector for HackerNews API."""

    _spec: HackerNewsSource
    _session: aiohttp.ClientSession

    def __init__(self, spec: HackerNewsSource, session: aiohttp.ClientSession):
        self._spec = spec
        self._session = session

    @staticmethod
    async def create(spec: HackerNewsSource) -> "HackerNewsConnector":
        """Create a HackerNews connector from the spec."""
        return HackerNewsConnector(spec, aiohttp.ClientSession())

• source_connector tells CocoIndex that this class is a custom source connector. It specifies:
  • spec_cls: the configuration class (HackerNewsSource)
  • key_type: how individual items are identified (_HackerNewsThreadKey)
  • value_type: the structure of the data returned (_HackerNewsThread)
• create() is called by CocoIndex to initialize the connector, and it sets up a fresh aiohttp.ClientSession for making HTTP requests.

Listing Available Threads​

The list() method in HackerNewsConnector is responsible for discovering all available HackerNews threads that match the given criteria (tag, max results) and returning metadata about them. CocoIndex uses this to know which threads exist and which may have changed.

async def list(
    self,
) -> AsyncIterator[PartialSourceRow[_HackerNewsThreadKey, _HackerNewsThread]]:
    """List HackerNews threads using the search API."""
    # Use HackerNews search API
    search_url = "https://hn.algolia.com/api/v1/search_by_date"
    params: dict[str, Any] = {"hitsPerPage": self._spec.max_results}
    
    if self._spec.tag:
        params["tags"] = self._spec.tag
    async with self._session.get(search_url, params=params) as response:
        response.raise_for_status()
        data = await response.json()
        for hit in data.get("hits", []):
            if thread_id := hit.get("objectID", None):
                utime = hit.get("updated_at")
                ordinal = (
                    int(datetime.fromisoformat(utime).timestamp())
                    if utime
                    else NO_ORDINAL
                )
                yield PartialSourceRow(
                    key=_HackerNewsThreadKey(thread_id=thread_id),
                    data=PartialSourceRowData(ordinal=ordinal),
                )

list() fetches metadata for all recent HackerNews threads.

• For each thread:
  • It generates a PartialSourceRow with:
    • key: the thread ID
    • ordinal: the last updated timestamp
• Purpose: allows CocoIndex to track what threads exist and which have changed without fetching full thread content.

Fetching Full Thread Content​

This async method fetches a single HackerNews thread (including its comments) from the API, and wraps the result in a PartialSourceRowData object — the structure CocoIndex uses for row-level ingestion.

async def get_value(
    self, key: _HackerNewsThreadKey
) -> PartialSourceRowData[_HackerNewsThread]:
    """Get a specific HackerNews thread by ID using the items API."""

    # Use HackerNews items API to get full thread with comments
    item_url = f"https://hn.algolia.com/api/v1/items/{key.thread_id}"

    async with self._session.get(item_url) as response:
        response.raise_for_status()
        data = await response.json()

        if not data:
            return PartialSourceRowData(
                value=NON_EXISTENCE,
                ordinal=NO_ORDINAL,
                content_version_fp=None,
            )
        return PartialSourceRowData(
            value=HackerNewsConnector._parse_hackernews_thread(data)
        )

• get_value() fetches the full content of a specific thread, including comments.
• Parses the raw JSON into structured Python objects (_HackerNewsThread + _HackerNewsComment).
• Returns a PartialSourceRowData containing the full thread.

Ordinal Support​

Tells CocoIndex that this source provides timestamps (ordinals).

def provides_ordinal(self) -> bool:
    return True

CocoIndex uses ordinals to incrementally update only changed threads, improving efficiency.

Parsing JSON into Structured Data​

This static method takes the raw JSON response from the API and turns it into a normalized _HackerNewsThread object containing:

• The post (title, text, metadata)
• All nested comments, flattened into a single list
• Proper Python datetime objects

It performs recursive traversal of the comment tree.

@staticmethod
def _parse_hackernews_thread(data: dict[str, Any]) -> _HackerNewsThread:
    comments: list[_HackerNewsComment] = []

    def _add_comments(parent: dict[str, Any]) -> None:
        children = parent.get("children", None)
        if not children:
            return
        for child in children:
            ctime = child.get("created_at")
            if comment_id := child.get("id", None):
                comments.append(
                    _HackerNewsComment(
                        id=str(comment_id),
                        author=child.get("author", ""),
                        text=child.get("text", ""),
                        created_at=datetime.fromisoformat(ctime) if ctime else None,
                    )
                )
            _add_comments(child)

    _add_comments(data)

    ctime = data.get("created_at")
    text = data.get("title", "")
    if more_text := data.get("text", None):
        text += "\n\n" + more_text
    return _HackerNewsThread(
        author=data.get("author"),
        text=text,
        url=data.get("url"),
        created_at=datetime.fromisoformat(ctime) if ctime else None,
        comments=comments,
    )

• Converts raw HackerNews API response into _HackerNewsThread and _HackerNewsComment.
• _add_comments() recursively parses nested comments.
• Combines title + text into the main thread content.
• Produces a fully structured object ready for indexing.

Putting It All Together in a Flow​

Your flow now reads exactly like a React component.

Define the flow and connect source​

@cocoindex.flow_def(name="HackerNewsIndex")
def hackernews_flow(
    flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
) -> None:

    # Add the custom source to the flow
    data_scope["threads"] = flow_builder.add_source(
        HackerNewsSource(tag="story", max_results=500),
        refresh_interval=timedelta(minutes=1),
    )

    # Create collectors for different types of searchable content
    message_index = data_scope.add_collector()

Process each thread and collect structured information​

with data_scope["threads"].row() as thread:
    # Index the main thread content
    message_index.collect(
        id=thread["thread_id"],
        thread_id=thread["thread_id"],
        content_type="thread",
        author=thread["author"],
        text=thread["text"],
        url=thread["url"],
        created_at=thread["created_at"],
    )

Process each comment of a thread and collect structured information​

with thread["comments"].row() as comment:
    message_index.collect(
        id=comment["id"],
        thread_id=thread["thread_id"],
        content_type="comment",
        author=comment["author"],
        text=comment["text"],
        created_at=comment["created_at"],
    )

Export to database tables

message_index.export(
    "hn_messages",
    cocoindex.targets.Postgres(),
    primary_key_fields=["id"],
)

CocoIndex now:

• polls the HackerNews API
• tracks changes incrementally
• flattens nested comments
• exports to Postgres
• supports live mode

Your app can now query it as a real-time search index.

Querying & Searching the HackerNews Index​

At this point you are done with the index flow. As the next step, you could define query handlers — so you can run queries in CocoInsight. You can use any library or framework of your choice to perform queries. You can read more in the documentation about Query Handler.

@hackernews_flow.query_handler()
def search_text(query: str) -> cocoindex.QueryOutput:
    """Search HackerNews threads by title and content."""
    table_name = cocoindex.utils.get_target_default_name(hackernews_flow, "hn_messages")

    with connection_pool().connection() as conn:
        with conn.cursor() as cur:
            # Simple text search using PostgreSQL's text search capabilities
            cur.execute(
                f"""
                SELECT id, thread_id, author, content_type, text, created_at,
                       ts_rank(to_tsvector('english', text), plainto_tsquery('english', %s)) as rank
                FROM {table_name}
                WHERE to_tsvector('english', text) @@ plainto_tsquery('english', %s)
                ORDER BY rank DESC, created_at DESC
                """,
                (query, query),
            )

            results = []
            for row in cur.fetchall():
                results.append(
                    {
                        "id": row[0],
                        "thread_id": row[1],
                        "author": row[2],
                        "content_type": row[3],
                        "text": row[4],
                        "created_at": row[5].isoformat(),
                    }
                )

            return cocoindex.QueryOutput(results=results)

This code defines a query handler that searches HackerNews threads and comments indexed in CocoIndex. It determines the database table storing the messages, then uses PostgreSQL full-text search (to_tsvector and plainto_tsquery) to find rows matching the query.

Results are ranked by relevance (ts_rank) and creation time, formatted into dictionaries, and returned as a structured cocoindex.QueryOutput. Essentially, it performs a full-text search over the indexed content and delivers ranked, structured results.

Running Your HackerNews Custom Source​

Once your custom source and flow are ready, running it with CocoIndex is straightforward. You can either update the index on-demand or keep it continuously in sync with HackerNews.

1. Install Dependencies​

Make sure you have Python installed and then install your project in editable mode:

pip install -e .

This installs CocoIndex along with all required dependencies, letting you develop and update the connector without reinstalling.

2. Update the Target (On-Demand)​

To populate your target (e.g., Postgres) with the latest HackerNews threads:

cocoindex update main

• Only threads that have changed will be re-processed.
• Your target remains in sync with the most recent 500 HackerNews threads.
• Efficient incremental updates save time and compute resources.

Note that each time when you run the update command, CocoIndex will only re-process threads that have changed, and keep the target in sync with the recent 500 threads from HackerNews. You can also run update command in live mode, which will keep the target in sync with the source continuously:

cocoindex update -L main

• Runs the flow in live mode, polling HackerNews periodically.
• CocoIndex automatically handles incremental changes and keeps the target synchronized.
• Ideal for dashboards, search, or AI pipelines that require real-time data.

3. Troubleshoot & Inspect with CocoInsight​

CocoInsight lets you visualize and debug your flow, see the lineage of your data, and understand what’s happening under the hood.

Start the server:

cocoindex server -ci main

Then open the UI in your browser: https://cocoindex.io/cocoinsight

CocoInsight has zero pipeline data retention — it’s safe for debugging and inspecting your flows locally.

Note that this requires QueryHandler setup in previous step.

What You Can Build Next​

This simple example opens the door to a lot more:

• Build a trending-topic detector
• Run LLM summarization pipelines on top of indexed threads
• Add embeddings + vector search
• Mirror HN into your internal data warehouse
• Build a real-time HN dashboard
• Extend to other news sources (Reddit, Lobsters, etc.)

Because the whole pipeline is declarative and incremental, extending it is straightforward.

Since Custom Sources allow you to wrap any Python logic into an incremental data stream, the best use cases are usually "Hard-to-Reach" data—systems that don't have standard database connectors, have complex nesting, or require heavy pre-processing.

The Knowledge Aggregator for LLM Context​

Building a context engine for an AI bot often requires pulling from non-standard documentation sources.

The "Composite" Entity (Data Stitching)​

Most companies have user data fragmented across multiple microservices. You can build a Custom Source that acts as a "virtual join" before the data ever hits your index. For example the Source:

1. Fetches a User ID from an Auth Service (Okta/Auth0).
2. Uses that ID to fetch billing status from Stripe API.
3. Uses that ID to fetch usage logs from an Internal Redis.

Instead of managing complex ETL joins downstream, the Custom Source yields a single User360 object. CocoIndex tracks the state of this composite object; if the user upgrades in Stripe or changes their email in Auth0, the index updates automatically.

The "Legacy Wrapper" (Modernization Layer)​

Enterprises often have valuable data locked in systems that are painful to query (SOAP, XML, Mainframes). You get a modern, queryable SQL interface (via the CocoIndex target) on top of a 20-year-old system without rewriting the legacy system itself.

Public Data Monitor (Competitive Intelligence)​

Tracking changes on public websites or APIs that don't offer webhooks.

• The Source:
  • Competitor Pricing: Scraping e-commerce product pages.
  • Regulatory Feeds: Polling a government RSS feed or FDA drug approval database.
  • Crypto/Stocks: Hitting a CoinGecko or Yahoo Finance API.

The CocoIndex Value: Using the diff capabilities, you can trigger downstream alerts only when a price changes by >5% or a new regulation is posted, rather than spamming your database with identical polling results.

Why This Matters

Custom Sources extend this model to any API — internal, external, legacy, or real-time.

This unlocks a simple but powerful pattern:

If you can fetch it, CocoIndex can index it, diff it, and sync it.

Whether you’re indexing HackerNews or orchestrating dozens of enterprise services, the framework gives you a stable backbone with:

• persistent state
• deterministic updates
• automatic lineage
• flexible target exports
• minimal infrastructure overhead

⭐ Try It, Fork It, Star It​

If you found this useful, a star on GitHub means a lot — it helps others discover CocoIndex and supports further development.

This tutorial shows how to use BAML together with CocoIndex to build a data pipeline that extracts structured patient information from PDF intake forms. The BAML definitions describe the desired output schema and prompt logic, while CocoIndex orchestrates file input, transformation, and incremental indexing.

We’ll walk through setup, defining the BAML schema, generating the Python client, writing the CocoIndex flow, and running the pipeline. Throughout, we follow best practices (e.g. caching heavy steps) and cite documentation for key concepts.

The full project is open sourced here ⭐. To see more examples build with CocoIndex, you could refer to the examples page.

BAML​

BAML, created by BoundaryML, is a typed prompt engineering language that makes LLM workflows predictable, testable, and production-safe. Instead of treating prompts as fragile strings, BAML lets developers define clear input parameters, output schemas, and model configurations—transforming prompts into strongly typed functions.

CocoIndex​

CocoIndex is a unified data processing engine built for AI-native applications. It lets you define transformations in one declarative workflow—then keeps everything continuously up to date with real-time, incremental processing. Designed for reliability and scale, CocoIndex ensures that every derived artifact (embeddings, metadata, extractions, models) always reflects the latest source data, making it the foundation for fast, consistent RAG, analytics, and automation pipelines.

Flow Overview​

• Read PDF files from a directory.
• For each file, call the BAML function to get a structured Patient.
• Collect results and export to Postgres.

Prerequisites​

1. Install Postgres if you don't have one.

2. Install dependencies

   pip install -U cocoindex baml-py
   

3. Create a .env file. You can copy it from .env.example first:

   cp .env.example .env
   

   Then edit the file to fill in your GEMINI_API_KEY.

Structured Extraction Component with BAML​

Create a baml_src/ directory for your BAML definitions. We’ll define a schema for patient intake data (nested classes) and a function that prompts Gemini to extract those fields from a PDF. Save this as baml_src/patient.baml

Define Patient Schema​

Classes: We defined Pydantic-style classes (Contact, Address, Insurance, etc.) to match the FHIR-inspired patient schema. These become typed output models. Required fields are non-nullable; optional fields use ?.

class Contact {
  name string
  phone string
  relationship string
}

class Address {
  street string
  city string
  state string
  zip_code string
}

class Pharmacy {
  name string
  phone string
  address Address
}

class Insurance {
  provider string
  policy_number string
  group_number string?
  policyholder_name string
  relationship_to_patient string
}

class Condition {
  name string
  diagnosed bool
}

class Medication {
  name string
  dosage string
}

class Allergy {
  name string
}

class Surgery {
  name string
  date string
}

class Patient {
  name string
  dob string
  gender string
  address Address
  phone string
  email string
  preferred_contact_method string
  emergency_contact Contact
  insurance Insurance?
  reason_for_visit string
  symptoms_duration string
  past_conditions Condition[]
  current_medications Medication[]
  allergies Allergy[]
  surgeries Surgery[]
  occupation string?
  pharmacy Pharmacy?
  consent_given bool
  consent_date string?
}

Define the BAML function to extract patient info from a PDF​

function ExtractPatientInfo(intake_form: pdf) -> Patient {
  client Gemini
  prompt #"
    Extract all patient information from the following intake form document.
    Please be thorough and extract all available information accurately.
    {{ _.role("user") }}
    {{ intake_form }}

    Fill in with "N/A" for required fields if the information is not available.

    {{ ctx.output_format }}
  "#
}

We specify client Gemini and a prompt template. The special variable {{ intake_form }} injects the PDF, and {{ ctx.output_format }} tells BAML to expect the structured format defined by the return type. The prompt explicitly asks Gemini to extract all fields, filling “N/A” if missing.

Why role("user") matters?

• For OpenAI models (e.g., GPT-4, GPT-4o), if the file's content is not presented in the user message, the model won't "see" the PDF at all—your extraction will fail or be empty.
• For Gemini and Anthropic, it's more forgiving and can sometimes work anyway, which makes this confusing to debug across providers.

We only discovered this after a discussion on the BAML repo and our own investigations. If you skip the explicit role("user"), you might waste hours debugging inconsistent extractions.

Takeaway:
When building extraction flows with BAML, always set the role to "user" before adding file content to your prompt. That makes your workflow robust and portable across LLM providers.

Thanks to Deepu and Prashanth from our discord community for working with us on this issue. You can see a real-world debugging journey in our Discord thread.

Configure the LLM client to use Google’s Gemini model​

client<llm> Gemini {
  provider google-ai
  options {
    model gemini-2.5-flash
    api_key env.GEMINI_API_KEY
  }
}

Configure BAML generator​

In baml_src folder add generator.baml

generator python_client {
  output_type python/pydantic
  output_dir "../"
  version "0.213.0"
}

The generator block tells baml-cli to create a Python client with Pydantic models in the parent directory.

When we run baml-cli generate

This will compile the .baml definitions into a baml_client/ Python package in your project root. It contains:

• baml_client/types.py with Pydantic classes (Patient, etc.).
• baml_client/sync_client.py and async_client.py with a callable b object. For example, b.ExtractPatientInfo(pdf) will return a Patient.

Continuous Data Transformation flow with incremental processing​

Next we will define data transformation flow with CocoIndex. Once you declared the state and transformation logic, CocoIndex will take care of all the state change for you from source to target.

CocoIndex Flow​

Declare Flow​

Declare a Cocoindex flow, connect to the source, add a data collector to collect processed data.

@cocoindex.flow_def(name="PatientIntakeExtractionBaml")
def patient_intake_extraction_flow(
    flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
) -> None:
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.LocalFile(
            path=os.path.join("data", "patient_forms"), binary=True
        )
    )
    
    patients_index = data_scope.add_collector()

This iterates over each document. We transform doc["content"] (the bytes) by our extract_patient_info function. The result is stored in a new field patient_info. Then we collect a row with the filename and extracted patient info.

Define a custom function to use BAML extraction to transform a PDF​

@cocoindex.op.function(cache=True, behavior_version=1)
async def extract_patient_info(content: bytes) -> Patient:
    pdf = baml_py.Pdf.from_base64(base64.b64encode(content).decode("utf-8"))
    return await b.ExtractPatientInfo(pdf)

• The extract_patient_info function is decorated with @cocoindex.op.function(cache=True, behavior_version=1). Setting cache=True causes CocoIndex to cache outputs of this function for incremental runs (so unchanged inputs skip rerunning the LLM). We increase behavior_version (start at 1) so that any prompt or logic changes will force a refresh.
• Inside the function, we convert bytes to a BAML Pdf (via base64) and then call await b.ExtractPatientInfo(pdf). This returns a Patient dataclass instance (mapped from the BAML output)

Process each document​

1. Transform each doc with BAML
2. collect the structured output

with data_scope["documents"].row() as doc:
    doc["patient_info"] = doc["content"].transform(extract_patient_info)

    patients_index.collect(
        filename=doc["filename"],
        patient_info=doc["patient_info"],
    )

It is common to have heavy nested data, CocoIndex is natively designed to handle heavily nested data structures.

Export to Postgres​

patients_index.export(
    "patients",
    cocoindex.storages.Postgres(),
    primary_key_fields=["filename"],
)

we export the collected index to Postgres. This will create/maintain a table patients keyed by filename, automatically deleting or updating rows if inputs change. Because CocoIndex tracks data lineage, it will handle updates/deletions of source files incrementally

Running the Pipeline​

Generate BAML client code (required step, in case you didn’t do it earlier. )

baml generate

This generates the baml_client/ directory with Python code to call your BAML functions.

Update the index:

cocoindex update main

CocoInsight

I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. It just connects to your local CocoIndex server, with zero pipeline data retention.

cocoindex server -ci main

Composable by Default: Use the Best Components for Your Use Case​

While CocoIndex provides a rich set of building blocks for building LLM pipelines, it is fundamentally designed as an open system. Developers can bring in their preferred transformation components tailored to their domain — from document parsers to structured extractors like BAML.

This flexibility enables deep composability with other open ecosystems. The synergy between CocoIndex and BAML highlights this philosophy: BAML brings powerful prompt-driven schema extraction, while CocoIndex orchestrates and maintains the flow at scale. There’s no lock-in — developers and enterprises experimenting at the frontier can adapt, extend, and integrate freely.

Summary​

By combining BAML and CocoIndex, we get a robust, schema-driven workflow: BAML ensures the prompt-to-schema mapping is correct and type-safe, while CocoIndex handles data ingestion, transformation, and incremental storage. This example extracted patient intake information (names, insurance, medications, etc.) from PDFs, but the pattern applies to any structured data extraction task.

• For more details on CocoIndex flows, see its documentation.
• For BAML usage and prompt engineering, refer to the BoundaryML docs.

There’s more need for open data infrastructure for AI, than ever.

Data for humans → to data for AI​

Traditionally, people build data frameworks heavily in this space to prepare data for humans. Over the years, we’ve seen massive progress in analytics-focused data infrastructure. Platforms like Spark and Flink fundamentally changed how the world processes and transforms data, at scale.

But with the rise of AI, entirely new needs — and new capabilities — have emerged. A new generation of data transformations is now required to support AI-native workloads.

PDFs are rich with both text and visual content — from descriptive paragraphs to illustrations and tables. This example builds an end-to-end flow that parses, embeds, and indexes both, with full traceability to the original page.

In this example, we split out both text and images, link them back to page metadata, and enable unified semantic search. We’ll use CocoIndex to define the flow, SentenceTransformers for text embeddings, and CLIP for image embeddings — all stored in Qdrant for retrieval.

We’re excited to announce Custom Sources — a new capability in CocoIndex that lets you read data from any system you want. Whether it’s APIs, databases, file systems, cloud storage, or other external services, CocoIndex can now ingest data incrementally, track changes efficiently, and integrate seamlessly into your flows.

After this change, users for CocoIndex are not bounded by any connectors, targets or some prebuilt libraries. You can use CocoIndex for anything, and enjoy the robust incremental computing to build fresh knowledge for AI.

Custom sources are the perfect complement to custom targets, giving you full control over both ends of your data pipelines.

🚀 Get started with custom sources by following the documentation now.

We are launching a major feature in both CocoIndex and CocoInsight to help users fast iterate with the indexing strategy, and trace back all the way to the data — to make the transformation experience more seamlessly integrated with the end goal.

We deeply care about making the overall experience seamless. With the new launch, you can define query handlers, so that you can easily run queries in tools like CocoInsight.

CocoIndex is one framework for building incremental data flows across structured and unstructured sources.

In CocoIndex, AI steps -- like generating embeddings -- are just transforms in the same flow as your other types of transformations, e.g. data mappings, calculations, etc.

Why One Framework for Structured + Unstructured?​

• One mental model: Treat files, APIs, and databases uniformly; AI steps are ordinary ops.
• Incremental by default: Use an ordinal column to sync only changes; no fragile glue jobs.
• Consistency: Embeddings are always derived from the exact transformed row state.
• Operational simplicity: One deployment, one lineage view, fewer moving parts.

This blog introduces the new PostgreSQL source and shows how to take data from PostgreSQL table as source, transform with both AI models and non-AI calculations, and write them into a new PostgreSQL table for semantic + structured search.

Do you have a messy collection of scanned documents, PDFs, academic papers, presentation slides, and standalone images — all mixed together with charts, tables, and figures — that you want to process into the same vector space for semantic search or to power an AI agent?

In this example, we’ll walk through how to build a visual document indexing pipeline using ColPali for embedding both PDFs and images — and then query the index using natural language.
We’ll skip OCR entirely — ColPali can directly understand document layouts, tables, and figures from images, making it perfect for semantic search across visual-heavy content.

We’ve shipped 20+ releases — packed with production-ready features, scalability upgrades, and runtime improvements. 🚀 Huge thanks to our amazing users for the feedback and for running CocoIndex at scale!

We’re excited to announce that CocoIndex now supports native integration with ColPali — enabling multi-vector, patch-level image indexing using cutting-edge multimodal models.

With just a few lines of code, you can now embed and index images with ColPali’s late-interaction architecture, fully integrated into CocoIndex’s composable flow system.

CocoIndex now provides robust and flexible support for typed vector data — from simple numeric arrays to deeply nested multi-dimensional vectors. This support is designed for seamless integration with high-performance vector databases such as Qdrant, and enables advanced indexing, embedding, and retrieval workflows across diverse data modalities.

We’re excited to announce that CocoIndex now officially supports custom targets — giving you the power to export data to any destination, whether it's a local file, cloud storage, a REST API, or your own bespoke system.

This new capability unlocks a whole new level of flexibility for integrating CocoIndex into your pipelines and allows you to bring your own "building blocks" into our flow model.

CocoIndex supports multi-modal processing natively - it could process both text and image with the same programming model and observe in the same user flow (in CocoInsight).

In this blog, we’ll walk through a comprehensive example of building a scalable face recognition pipeline using CocoIndex. We’ll show how to extract and embed faces from images, structure the data relationally, and export everything into a vector database for real-time querying.

CocoInsight can now visualize identified sections of an image based on the bounding boxes and makes it easier to understand and evaluate AI extractions - seamlessly attaching computed features in the context of unstructured visual data.

In this blog we will walk through a comprehensive example of indexing research papers with extracting different metadata — beyond full text chunking and embedding — and build semantic embeddings for indexing and querying.

From day zero, we envisioned CocoInsight as a fundamental companion to CocoIndex — not just a tool, but a philosophy: making data explainable, auditable, and actionable at every stage of the data pipeline with AI workloads. CocoInsight has been in private beta for a while, it is one of the most loved feature for our users building ETL with coco, with significant boost on developer velocity, and lowering the barrier to entry for data engineering.

We are officially launching CocoInsight today - it has zero pipeline data retention and connects to your on-premise CocoIndex server for pipeline insights. This makes data directly visible and easy to develop ETL pipelines.

CocoIndex supports Qdrant natively - the integration features a high performance Rust stack with incremental processing end to end for scale and data freshness. 🎉 We just rolled out our latest change that handles automatic target schema setup with Qdrant from CocoIndex indexing flow.

CocoIndex now provides native support for Kuzu as a target graph data store. This integration features a high performance knowledge graph stack with real-time updates.

CocoIndex now provides native support for Amazon S3 as a data source. Additionally, CocoIndex integrates with AWS Simple Queue Service (SQS), enabling true real-time incremental processing of your S3 data.

In this project, we will build image search and query it with natural language. You can search for “a cute animal” or “a red car”, and the system returns visually relevant results — no manual tagging needed.

In this blog, we will build index with text embeddings and query it with natural language. We try to keep it minimalistic and focus on the gist of the indexing flow.

We have been working on CocoIndex - a real-time data framework for AI for a while, with lots of excitement from the community. We officially crossed 1k stars earlier this week. Huge thanks to everyone who starred, forked, contributed, or shared the love ❤️!

In this blog, we will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.

CocoIndex makes it easy to build and maintain knowledge graphs with continuous source updates. In this blog, we will process a list of documents (using CocoIndex documentation as an example). We will use LLM to extract relationships between the concepts in each document.