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How do I install PDF4LLM for .NET?

Install via the .NET CLI: 
dotnet add package PDF4LLM
Or via the Visual Studio Package Manager Console: 
Install-Package PDF4LLM
Or by adding a PackageReference directly to your .csproj: 
<PackageReference Include="PDF4LLM" Version="*" />
MuPDF.NET is installed automatically as a dependency — you do not need to add it separately.

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Which .NET targets are supported?

PDF4LLM targets .NET Standard 2.0, making it compatible with any framework that implements that standard:
Target frameworkSupported
.NET 8.0✓
.NET 7.0✓
.NET 6.0✓
.NET 5.0✓
.NET Standard 2.0✓
.NET Framework 4.8✓
.NET Framework 4.7.2✓
.NET Framework 4.6.1✓

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How do I verify my installation?

Add a using directive and call ToMarkdown on any PDF: 
using MuPDF.NET;
using PDF4LLM;

string markdown = PdfExtractor.ToMarkdown("document.pdf");
Console.WriteLine(markdown);
If this prints Markdown to the console, everything is wired up correctly.

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I’m seeing an “assembly with the same simple name” conflict. How do I fix it?

Your installed version of MuPDF.NET already bundles PDF4LLM internally. Having both packages referenced simultaneously causes the conflict. Remove the explicit PDF4LLM package reference and rely on the bundled version: 
dotnet remove package PDF4LLM
Or remove the line from your .csproj manually: 
<!-- Remove this line -->
<PackageReference Include="PDF4LLM" Version="*" />
The API is identical either way — using PDF4LLM; and PdfExtractor.* work regardless of which package supplies the assembly.
A future release of MuPDF.NET will stop bundling PDF4LLM, allowing both packages to coexist without conflict.

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How do I convert a PDF to Markdown?

Open a Document and pass it to PdfExtractor.ToMarkdown(): 
using MuPDF.NET;
using PDF4LLM;

Document doc      = new Document("my-document.pdf");
string   markdown = PdfExtractor.ToMarkdown(doc);
doc.Close();

Console.WriteLine(markdown);
To save the result to a file: 
File.WriteAllText("output.md", markdown, System.Text.Encoding.UTF8);

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What output formats are supported?

There are three extraction methods on PdfExtractor, all sharing a consistent interface:
MethodReturnsBest for
ToMarkdown()stringLLM ingestion and RAG pipelines
ToJson()string (JSON)Custom pipelines needing bounding boxes and layout metadata
ToText()stringSearch indexing and NLP preprocessing
string markdown = PdfExtractor.ToMarkdown(doc);
string json     = PdfExtractor.ToJson(doc);
string text     = PdfExtractor.ToText(doc);

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How do I extract only specific pages?

Pass a zero-based list of page indices to the pages parameter. This works across all three extraction methods: 
string markdown = PdfExtractor.ToMarkdown(
    doc,
    pages: new List<int> { 0, 1, 2 }
);
Page numbers are zero-indexed — page 1 of the document is 0, page 2 is 1, and so on.

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What document formats are supported as input?

Standard formats — PDF, XPS, EPUB, MOBI, and more — are supported out of the box. See the Supported Formats guide for a full list of supported input and output formats.

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How do I analyse visual layout regions (columns, figures, sidebars)?

Use PdfExtractor.ParseDocument(). It analyses the document and returns a typed ParsedDocument object containing a list of ParsedPage objects, each with its detected blocks, tables, images, and bounding boxes in reading order. 
ParsedDocument parsed = PdfExtractor.ParseDocument(doc);

foreach (var page in parsed.Pages)
{
    Console.WriteLine($"Page {page.Number}: {page.Blocks.Count} blocks");
}

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How do I extract AcroForm field values from a filled PDF?

Use PdfExtractor.GetKeyValues(). It returns a List<FormField>, each with Name, Value, and Page properties: 
List<FormField> fields = PdfExtractor.GetKeyValues(doc);

foreach (var field in fields)
{
    Console.WriteLine($"{field.Name} (page {field.Page}): {field.Value}");
}
Note: GetKeyValues() does not accept a pages parameter — it always processes the full document.

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Does it handle scanned or image-based PDFs?

Yes, via Tesseract OCR. Unlike the Python library, OCR is not triggered automatically — you must opt in with useOcr: true: 
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true);
The same flag works across all three extraction methods: 
string text           = PdfExtractor.ToText(doc, useOcr: true);
ParsedDocument parsed = PdfExtractor.ParseDocument(doc, useOcr: true);
OCR output goes through the same layout analysis as native text, so reading order, heading detection, and table detection all apply.

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How do I install Tesseract for OCR?

Tesseract must be installed on the host system and available on the PATH. PDF4LLM does not bundle it. Windows — Download the installer from UB Mannheim Tesseract builds and add the install directory (e.g. C:\Program Files\Tesseract-OCR) to your PATH. macOS 
brew install tesseract
Linux (Debian / Ubuntu) 
sudo apt-get install tesseract-ocr
Verify Tesseract is reachable from the application’s environment: 
tesseract --version
If Tesseract is installed but not on the PATH, you will get a TesseractNotFoundException at runtime.

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How do I use OCR with a non-English language?

Pass a Tesseract language code to ocrLanguage. The default is "eng". Combine multiple languages with a +: 
// Single language
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "fra");

// Multiple languages mixed on the same pages
string markdown = PdfExtractor.ToMarkdown(doc, useOcr: true, ocrLanguage: "eng+deu");
The corresponding Tesseract language packs must be installed on your system first. On Debian/Ubuntu: 
sudo apt-get install tesseract-ocr-fra tesseract-ocr-deu
Common language codes: eng (English), fra (French), deu (German), spa (Spanish), jpn (Japanese), chi_sim (Simplified Chinese), chi_tra (Traditional Chinese).

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How do I handle documents that mix scanned and native text pages?

Use a per-page native probe to identify which pages need OCR, then extract each set separately: 
var scannedPages = new List<int>();

for (int i = 0; i < doc.PageCount; i++)
{
    string native = PdfExtractor.ToText(doc, pages: new List<int> { i });
    if (native.Trim().Length < 50)
        scannedPages.Add(i);
}

string ocrMarkdown    = scannedPages.Count > 0
    ? PdfExtractor.ToMarkdown(doc, pages: scannedPages, useOcr: true)
    : string.Empty;

var nativePages       = Enumerable.Range(0, doc.PageCount).Except(scannedPages).ToList();
string nativeMarkdown = nativePages.Count > 0
    ? PdfExtractor.ToMarkdown(doc, pages: nativePages)
    : string.Empty;
Adjust the character threshold (< 50) to suit your documents — pages with only a page number or short heading will score low.

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How do I run OCR in a Docker container?

Add Tesseract to your Dockerfile: 
FROM mcr.microsoft.com/dotnet/runtime:8.0

RUN apt-get update && apt-get install -y \
    tesseract-ocr \
    tesseract-ocr-eng \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app
COPY --from=build /app/publish .
ENTRYPOINT ["dotnet", "MyApp.dll"]
Verify Tesseract is on the PATH inside the built image: 
docker run --rm your-image tesseract --version
If you install language data to a custom location, set TESSDATA_PREFIX explicitly: 
ENV TESSDATA_PREFIX=/usr/share/tesseract-ocr/4.00/tessdata/

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Does it integrate with LlamaIndex?

Yes. Use PdfExtractor.LlamaMarkdownReader() to get a PDFMarkdownReader instance, then call LoadData() to get one LlamaDocument per page with Markdown text and metadata: 
var reader = PdfExtractor.LlamaMarkdownReader();
var pages  = reader.LoadData("product-manual.pdf");

foreach (var page in pages)
{
    int    pageNum  = (int)page.ExtraInfo["page"];
    string filePath = (string)page.ExtraInfo["file_path"];
    Console.WriteLine($"Page {pageNum}: {page.Text.Length} chars");
}

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How do I use PDF4LLM with Azure OpenAI?

Install the Azure OpenAI SDK alongside PDF4LLM: 
dotnet add package PDF4LLM
dotnet add package Azure.AI.OpenAI
Extract Markdown and pass it to a chat completion for summarisation or Q&A: 
using Azure;
using Azure.AI.OpenAI;
using MuPDF.NET;
using PDF4LLM;

AzureOpenAIClient client = new(
    new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
    new AzureKeyCredential(Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")!)
);

Document doc      = new Document("briefing.pdf");
string   markdown = PdfExtractor.ToMarkdown(doc);
doc.Close();

var chatClient = client.GetChatClient("gpt-4o");

var result = await chatClient.CompleteChatAsync(
[
    new SystemChatMessage("You are a precise document summariser."),
    new UserChatMessage($"Summarise this document in five bullet points:\n\n{markdown}")
]);

Console.WriteLine(result.Value.Content[0].Text);
For RAG pipelines, embed per-page chunks using LlamaMarkdownReader and text-embedding-3-small, then retrieve by cosine similarity before passing context to a chat model. See the Azure OpenAI integration guide for full patterns including batched embedding, parallel OCR, multimodal image input, and Managed Identity auth.

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How do I use Managed Identity instead of an API key for Azure OpenAI?

Replace AzureKeyCredential with DefaultAzureCredential: 
using Azure.Identity;

AzureOpenAIClient client = new(
    new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
    new DefaultAzureCredential()
);
DefaultAzureCredential works in Azure App Service, Azure Functions, AKS, and other managed environments. For local development, run az login first, or use AzureCliCredential explicitly. Assign the Cognitive Services OpenAI User role to the managed identity in the Azure Portal or via the CLI: 
az role assignment create \
  --role "Cognitive Services OpenAI User" \
  --assignee <managed-identity-object-id> \
  --scope /subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<resource-name>