# On-premise structured extraction with LLM using Ollama

> Learn to use CocoIndex to extract structured data from PDF/Markdown with Ollama's local LLM models. All running on premise without sending data to external APIs.

Published: 2025-03-17 · Canonical: https://cocoindex.io/blogs/cocoindex-ollama-structured-extraction-from-pdf/

In this blog, we will show you how to use Ollama to extract structured data that you can run locally and deploy on your own cloud/server.

You can find the full code [here](https://github.com/cocoindex-io/cocoindex/blob/main/examples/manuals_llm_extraction/main.py). Only ~ 100 lines of Python code. Check it out 🤗! 

If you like this post and our work, please ⭐ star [CocoIndex on GitHub](https://github.com/cocoindex-io/cocoindex) to support us ❤️. Thank you so much with a warm coconut hug 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)

## Prerequisites
### Install Postgres
If you don't have Postgres installed, please refer to the [installation guide](https://cocoindex.io/docs/getting_started/installation).

### Install Ollama
Ollama allows you to run LLM models on your local machine easily. To get started:

[Download](https://ollama.com/download) and install Ollama.
Pull your favorite LLM models by the ollama pull command, e.g.

```sh
ollama pull llama3.2
```

## Extract structured data from Markdown files
### 1. Define output
We are going to extract the following information from the Python Manuals as structured data.

So we are going to define the output data class as the following. The goal is to extract and populate `ModuleInfo`.

``` python
@dataclasses.dataclass
class ArgInfo:
    """Information about an argument of a method."""
    name: str
    description: str

@dataclasses.dataclass
class MethodInfo:
    """Information about a method."""
    name: str
    args: cocoindex.typing.List[ArgInfo]
    description: str

@dataclasses.dataclass
class ClassInfo:
    """Information about a class."""
    name: str
    description: str
    methods: cocoindex.typing.List[MethodInfo]

@dataclasses.dataclass
class ModuleInfo:
    """Information about a Python module."""
    title: str
    description: str
    classes: cocoindex.typing.List[ClassInfo]
    methods: cocoindex.typing.List[MethodInfo]
```

### 2. Define CocoIndex Flow
Let's define the CocoIndex flow to extract the structured data from markdowns, which is super simple.

First, let's add Python docs in markdown as a source. We will illustrate how to load PDFs a few sections below.

```python
@cocoindex.flow_def(name="ManualExtraction")
def manual_extraction_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.LocalFile(path="markdown_files")) 

    modules_index = data_scope.add_collector()
```

`flow_builder.add_source` will create a table with the following sub fields, see [documentation](https://cocoindex.io/docs/sources) here.
- `filename` (key, type: `str`): the filename of the file, e.g. `dir1/file1.md`
- `content` (type: `str` if `binary` is `False`, otherwise `bytes`): the content of the file

Then, let's extract the structured data from the markdown files. It is super easy: you just need to provide the LLM spec, and pass down the defined output type.

CocoIndex provides builtin functions (e.g. ExtractByLlm) that process data using an LLM. We provide built-in support for Ollama, which allows you to run LLM models on your local machine easily. You can find the full list of models [here](https://ollama.com/library). We also support OpenAI API. You can find the full documentation and instructions [here](https://cocoindex.io/docs/ai/llm).

```python
    # ...
    with data_scope["documents"].row() as doc:
        doc["module_info"] = doc["content"].transform(
            cocoindex.functions.ExtractByLlm(
                llm_spec=cocoindex.LlmSpec(
                     api_type=cocoindex.LlmApiType.OLLAMA,
                     # See the full list of models: https://ollama.com/library
                     model="llama3.2"
                ),
                output_type=ModuleInfo,
                instruction="Please extract Python module information from the manual."))
```

After the extraction, we just need to cherrypick anything we like from the output using the `collect` function from the collector of a data scope defined above.

```python
    modules_index.collect(
        filename=doc["filename"],
        module_info=doc["module_info"],
    )
```

Finally, let's export the extracted data to a table.

```python
    modules_index.export(
        "modules",
        cocoindex.storages.Postgres(table_name="modules_info"),
        primary_key_fields=["filename"],
    )
```

### 3. Query and test your index
🎉 Now you are all set!

Run the following command to set up and update the index.
```sh
cocoindex update -L main
```
You'll see the index updates state in the terminal.

After the index is built, you have a table with the name `modules_info`. You can query it at any time, e.g., start a Postgres shell:

```sh
psql postgres://cocoindex:cocoindex@localhost/cocoindex
```

And run the SQL query:

```sql
SELECT filename, module_info->'title' AS title, module_summary FROM modules_info;
```

You can see the structured data extracted from the documents. Here's a screenshot of the extracted module information:

### CocoInsight
[CocoInsight](https://cocoindex.io/blogs/cocoinsight) is a tool to help you understand your data pipeline and data index.
CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: [Watch on YouTube](https://www.youtube.com/watch?v=ZnmyoHslBSc).

#### 1. Run the CocoIndex server

```sh
cocoindex server -ci main
```

to see the CocoInsight dashboard https://cocoindex.io/cocoinsight.  It connects to your local CocoIndex server with zero data retention.

There are two parts of the CocoInsight dashboard:

- Flows: You can see the flow you defined, and the data it collects.
- Data: You can see the data in the data index.

On the data side, you can click on any data and scroll down to see the details. In this data extraction example, you can see the data extracted from the markdown files and the structured data presented in tabular format.

For example, for the array module, you can preview the data by clicking on the data.

Lots of great updates coming soon, stay tuned!

## Add summary to the data
Using CocoIndex as a framework, you can easily add any transformation on the data (including LLM summary), and collect it as part of the data index.
For example, let's add a simple summary to each module - like the number of classes and methods, using a simple Python function.

We will add an LLM example later.

### 1. Define output
First, let's add the structure we want as part of the output definition.

``` python
@dataclasses.dataclass
class ModuleSummary:
    """Summary info about a Python module."""
    num_classes: int
    num_methods: int
```

### 2. Define CocoIndex Flow
Next, let's define a custom function to summarize the data. You can see detailed documentation [here](https://cocoindex.io/docs/core/custom_function#option-1-by-a-standalone-function) 

``` python
@cocoindex.op.function()
def summarize_module(module_info: ModuleInfo) -> ModuleSummary:
    """Summarize a Python module."""
    return ModuleSummary(
        num_classes=len(module_info.classes),
        num_methods=len(module_info.methods),
    )
```

### 3. Plug in the function into the flow

``` python
    # ...
    with data_scope["documents"].row() as doc:
      # ... after the extraction
      doc["module_summary"] = doc["module_info"].transform(summarize_module)
```

🎉 Now you are all set!

Run the following command to set up and update the index.
```sh
cocoindex update --setup main
```

## Extract structured data from PDF files
Ollama does not support PDF files directly as input, so we need to convert them to markdown first.

To do this, we can plug in a custom function to convert PDF to markdown. See the full documentation [here](https://cocoindex.io/docs/core/custom_function).

### 1. Define a function spec

The function spec of a function configures the behavior of a specific instance of the function. 

``` python
class PdfToMarkdown(cocoindex.op.FunctionSpec):
    """Convert a PDF to markdown."""
```

### 2. Define an executor class

The executor class is a class that implements the function spec. It is responsible for the actual execution of the function. 

This class takes PDF content as bytes, saves it to a temporary file, and uses PdfConverter to extract the text content. The extracted text is then returned as a string, converting PDF to markdown format.

It is associated with the function spec by `spec: PdfToMarkdown`.

``` python
@cocoindex.op.executor_class(gpu=True, cache=True, behavior_version=1)
class PdfToMarkdownExecutor:
    """Executor for PdfToMarkdown."""

    spec: PdfToMarkdown
    _converter: PdfConverter

    def prepare(self):
        config_parser = ConfigParser({})
        self._converter = PdfConverter(create_model_dict(), config=config_parser.generate_config_dict())

    def __call__(self, content: bytes) -> str:
        with tempfile.NamedTemporaryFile(delete=True, suffix=".pdf") as temp_file:
            temp_file.write(content)
            temp_file.flush()
            text, _, _ = text_from_rendered(self._converter(temp_file.name))
            return text

```
You may wonder why we want to define a spec + executor (instead of using a standalone function) here. The main reason is there's some heavy preparation work (initializing the parser) that needs to be done before being ready to process real data.

### 3. Plug it into the flow

``` python
    # Note the binary = True for PDF
    data_scope["documents"] = flow_builder.add_source(cocoindex.sources.LocalFile(path="manuals", binary=True))
    modules_index = data_scope.add_collector()

    with data_scope["documents"].row() as doc:
        # plug in your custom function here
        doc["markdown"] = doc["content"].transform(PdfToMarkdown())

```

🎉 Now you are all set!

Run the following command to set up and update the index.

```sh
cocoindex update --setup main
```

## Community

We love to hear from the community! You can find us on [GitHub](https://github.com/cocoindex-io/cocoindex) and [Discord](https://discord.com/invite/zpA9S2DR7s).

If you like this post and our work, please **⭐ star [CocoIndex on GitHub](https://github.com/cocoindex-io/cocoindex) to support us**. Thank you with a warm coconut hug 🥥🤗.