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This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,142 @@
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---
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title: "AmazonTextractConverter"
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id: amazontextractconverter
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slug: "/amazontextractconverter"
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description: "`AmazonTextractConverter` converts images and single-page PDFs to documents using AWS Textract. It supports plain text OCR, structured analysis of tables, forms, signatures, and layout, as well as natural-language queries over the document."
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---
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# AmazonTextractConverter
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`AmazonTextractConverter` converts images and single-page PDFs to documents using AWS Textract. It supports plain text OCR, structured analysis of tables, forms, signatures, and layout, as well as natural-language queries over the document.
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<div className="key-value-table">
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| | |
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| --- | --- |
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||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
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| **Mandatory init variables** | AWS credentials are resolved via `Secret` parameters or the default boto3 credential chain (environment variables, AWS config files, IAM roles). |
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| **Mandatory run variables** | `sources`: A list of file paths or `ByteStream` objects |
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| **Output variables** | `documents`: A list of documents <br /> <br />`raw_textract_response`: A list of raw responses from the Textract API |
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| **API reference** | [Amazon Textract](/reference/integrations-amazon_textract) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_textract |
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| **Package name** | `amazon-textract-haystack` |
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</div>
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## Overview
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`AmazonTextractConverter` takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and uses AWS Textract to extract text from images and single-page PDFs. Optionally, metadata can be attached to the documents through the `meta` input parameter. You need an active AWS account with access to the Textract service to use this integration. Refer to the [AWS Textract documentation](https://docs.aws.amazon.com/textract/latest/dg/getting-started.html) to set up your AWS credentials and ensure Textract is available in your selected region.
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Supported input formats: JPEG, PNG, TIFF, BMP, and single-page PDF (up to 10 MB).
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By default, the component uses the standard AWS environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`, `AWS_DEFAULT_REGION`, `AWS_PROFILE`) for authentication. You can also pass these as `Secret` objects at initialization. The component falls back to the default boto3 credential chain if no explicit credentials are provided, which makes it work with IAM roles when running on AWS infrastructure.
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### Operation modes
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The component switches between two Textract APIs depending on how you configure it:
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- **Plain text OCR (`DetectDocumentText`)** – Used when `feature_types` is not set. This is the fastest and cheapest option, extracting raw text from the document.
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- **Structured analysis (`AnalyzeDocument`)** – Used when `feature_types` is set. You can pass any combination of `"TABLES"`, `"FORMS"`, `"SIGNATURES"`, and `"LAYOUT"` to extract richer structural information from the document.
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### Natural-language queries
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You can pass a list of natural-language questions through the `queries` parameter on `run()`. When queries are provided, the `QUERIES` feature type is added automatically and Textract returns the extracted answers in the raw response. This is useful for pulling specific fields out of forms, invoices, or receipts without writing custom parsing logic.
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## Usage
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You need to install the `amazon-textract-haystack` integration to use `AmazonTextractConverter`:
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```shell
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pip install amazon-textract-haystack
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```
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### On its own
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Basic usage with plain text OCR:
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```python
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from haystack_integrations.components.converters.amazon_textract import (
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AmazonTextractConverter,
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)
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converter = AmazonTextractConverter()
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result = converter.run(sources=["document.png"])
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documents = result["documents"]
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```
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Extracting tables and forms with `AnalyzeDocument`:
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```python
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from haystack_integrations.components.converters.amazon_textract import (
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AmazonTextractConverter,
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)
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converter = AmazonTextractConverter(feature_types=["TABLES", "FORMS"])
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result = converter.run(sources=["invoice.pdf"])
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documents = result["documents"]
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raw_responses = result["raw_textract_response"]
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```
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Using natural-language queries to extract specific fields:
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```python
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from haystack_integrations.components.converters.amazon_textract import (
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AmazonTextractConverter,
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)
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converter = AmazonTextractConverter()
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result = converter.run(
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sources=["receipt.png"],
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queries=["What is the patient name?", "What is the total due?"],
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)
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documents = result["documents"]
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raw_responses = result["raw_textract_response"]
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```
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Passing AWS credentials explicitly:
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```python
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from haystack.utils import Secret
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from haystack_integrations.components.converters.amazon_textract import (
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AmazonTextractConverter,
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)
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converter = AmazonTextractConverter(
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aws_access_key_id=Secret.from_env_var("AWS_ACCESS_KEY_ID"),
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aws_secret_access_key=Secret.from_env_var("AWS_SECRET_ACCESS_KEY"),
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aws_region_name=Secret.from_token("us-east-1"),
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)
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result = converter.run(sources=["document.png"])
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```
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### In a pipeline
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Here's an example of an indexing pipeline that uses Textract to extract text from images and writes the resulting documents to a Document Store:
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```python
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from haystack import Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
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from haystack.components.writers import DocumentWriter
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from haystack_integrations.components.converters.amazon_textract import (
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AmazonTextractConverter,
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)
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document_store = InMemoryDocumentStore()
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pipeline = Pipeline()
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pipeline.add_component("converter", AmazonTextractConverter())
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pipeline.add_component("cleaner", DocumentCleaner())
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pipeline.add_component(
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"splitter",
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DocumentSplitter(split_by="sentence", split_length=5),
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)
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pipeline.add_component("writer", DocumentWriter(document_store=document_store))
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pipeline.connect("converter", "cleaner")
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pipeline.connect("cleaner", "splitter")
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pipeline.connect("splitter", "writer")
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file_names = ["document.png", "invoice.pdf"]
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pipeline.run({"converter": {"sources": file_names}})
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```
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+109
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---
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title: "AzureDocumentIntelligenceConverter"
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id: azuredocumentintelligenceconverter
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slug: "/azuredocumentintelligenceconverter"
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description: "`AzureDocumentIntelligenceConverter` converts files to Documents using Azure's Document Intelligence service with GitHub Flavored Markdown output for better LLM/RAG integration. It supports PDF, JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML."
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---
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# AzureDocumentIntelligenceConverter
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`AzureDocumentIntelligenceConverter` converts files to Documents using Azure's Document Intelligence service with GitHub Flavored Markdown output for better LLM/RAG integration. It supports the following file formats: PDF (both searchable and image-only), JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML.
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<div className="key-value-table">
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|
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| | |
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| --- | --- |
|
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| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
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| **Mandatory init variables** | `endpoint`: The endpoint URL of your Azure Document Intelligence resource <br /> <br />`api_key`: The API key for Azure authentication. Can be set with `AZURE_DI_API_KEY` environment variable. |
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| **Mandatory run variables** | `sources`: A list of file paths or ByteStream objects |
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| **Output variables** | `documents`: A list of documents <br /> <br />`raw_azure_response`: A list of raw responses from Azure |
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| **API reference** | [Azure Document Intelligence](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_doc_intelligence) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_doc_intelligence |
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| **Package name** | `azure-doc-intelligence-haystack` |
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</div>
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## Overview
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`AzureDocumentIntelligenceConverter` takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and uses Azure's Document Intelligence service to convert the files to a list of documents. Optionally, metadata can be attached to the documents through the `meta` input parameter. You need an active Azure account and a Document Intelligence or Cognitive Services resource to use this integration. Follow the steps described in the Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api) to set up your resource.
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The component uses an `AZURE_DI_API_KEY` environment variable by default. Otherwise, you can pass an `api_key` at initialization — see code examples below.
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This component uses the `azure-ai-documentintelligence` package (v1.0.0+) and outputs GitHub Flavored Markdown, preserving document structure such as headings, tables, and lists. Tables are rendered as inline markdown tables rather than being extracted as separate documents.
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When you initialize the component, you can optionally set the `model_id`, which refers to the model you want to use. Available options include:
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- `"prebuilt-document"`: General document analysis (default)
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- `"prebuilt-read"`: Fast OCR for text extraction
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- `"prebuilt-layout"`: Enhanced layout analysis with better table and structure detection
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- Custom model IDs from your Azure resource
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Refer to the [Azure documentation](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature) for a full list of available models.
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:::info
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This component replaces the legacy [`AzureOCRDocumentConverter`](azureocrdocumentconverter.mdx), which uses the older `azure-ai-formrecognizer` package. The `AzureDocumentIntelligenceConverter` uses the newer `azure-ai-documentintelligence` SDK and produces Markdown output instead of plain text, making it better suited for LLM and RAG applications.
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:::
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:::note
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This component returns Markdown content. Avoid piping it through `DocumentCleaner()` with its default settings because `remove_extra_whitespaces=True` and `remove_empty_lines=True` can collapse line breaks and flatten headings, tables, and lists. Connect the converter directly to your next component, or disable those options if you need custom cleanup.
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:::
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## Usage
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You need to install the `azure-doc-intelligence-haystack` integration to use the `AzureDocumentIntelligenceConverter`:
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```shell
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pip install azure-doc-intelligence-haystack
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```
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### On its own
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```python
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from pathlib import Path
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from haystack_integrations.components.converters.azure_doc_intelligence import (
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AzureDocumentIntelligenceConverter,
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)
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from haystack.utils import Secret
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converter = AzureDocumentIntelligenceConverter(
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endpoint="https://YOUR_RESOURCE.cognitiveservices.azure.com/",
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api_key=Secret.from_env_var("AZURE_DI_API_KEY"),
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)
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result = converter.run(sources=[Path("my_file.pdf")])
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documents = result["documents"]
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```
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### In a pipeline
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```python
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from haystack import Pipeline
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.preprocessors import DocumentSplitter
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from haystack.components.writers import DocumentWriter
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from haystack.utils import Secret
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from haystack_integrations.components.converters.azure_doc_intelligence import (
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AzureDocumentIntelligenceConverter,
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)
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document_store = InMemoryDocumentStore()
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pipeline = Pipeline()
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pipeline.add_component(
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"converter",
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AzureDocumentIntelligenceConverter(
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endpoint="https://YOUR_RESOURCE.cognitiveservices.azure.com/",
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api_key=Secret.from_env_var("AZURE_DI_API_KEY"),
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),
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)
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pipeline.add_component(
|
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"splitter",
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DocumentSplitter(split_by="sentence", split_length=5),
|
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)
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pipeline.add_component("writer", DocumentWriter(document_store=document_store))
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pipeline.connect("converter", "splitter")
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pipeline.connect("splitter", "writer")
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file_names = ["my_file.pdf"]
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pipeline.run({"converter": {"sources": file_names}})
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```
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---
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title: "AzureOCRDocumentConverter"
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id: azureocrdocumentconverter
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slug: "/azureocrdocumentconverter"
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description: "`AzureOCRDocumentConverter` converts files to documents using Azure's Document Intelligence service. It supports the following file formats: PDF (both searchable and image-only), JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML."
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---
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# AzureOCRDocumentConverter
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|
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`AzureOCRDocumentConverter` converts files to documents using Azure's Document Intelligence service. It supports the following file formats: PDF (both searchable and image-only), JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML.
|
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|
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<div className="key-value-table">
|
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|
||||
| | |
|
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| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory init variables** | `endpoint`: The endpoint of your Azure resource <br /> <br />`api_key`: The API key of your Azure resource. Can be set with `AZURE_AI_API_KEY` environment variable. |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths |
|
||||
| **Output variables** | `documents`: A list of documents <br /> <br />`raw_azure_response`: A list of raw responses from Azure |
|
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| **API reference** | [Azure Form Recognizer](/reference/integrations-azure_form_recognizer) |
|
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_form_recognizer |
|
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| **Package name** | `azure-form-recognizer-haystack` |
|
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|
||||
</div>
|
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|
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## Overview
|
||||
|
||||
`AzureOCRDocumentConverter` takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and uses Azure services to convert the files to a list of documents. Optionally, metadata can be attached to the documents through the `meta` input parameter. You need an active Azure account and a Document Intelligence or Cognitive Services resource to use this integration. Follow the steps described in the Azure [documentation](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api) to set up your resource.
|
||||
|
||||
The component uses an `AZURE_AI_API_KEY` environment variable by default. Otherwise, you can pass an `api_key` at initialization – see code examples below.
|
||||
|
||||
When you initialize the component, you can optionally set the `model_id`, which refers to the model you want to use. Please refer to [Azure documentation](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature) for a list of available models. The default model is `"prebuilt-read"`.
|
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|
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The `AzureOCRDocumentConverter` doesn’t extract the tables from a file as plain text but generates separate `Document` objects of type `table` that maintain the two-dimensional structure of the tables.
|
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|
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## Usage
|
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|
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The `AzureOCRDocumentConverter` is part of the `azure-form-recognizer-haystack` integration package. Install it with:
|
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|
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```shell
|
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pip install azure-form-recognizer-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
|
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from haystack_integrations.components.converters.azure_form_recognizer import (
|
||||
AzureOCRDocumentConverter,
|
||||
)
|
||||
from haystack.utils import Secret
|
||||
|
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converter = AzureOCRDocumentConverter(
|
||||
endpoint="azure_resource_url",
|
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api_key=Secret.from_token("<your-api-key>"),
|
||||
)
|
||||
|
||||
converter.run(sources=[Path("my_file.pdf")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.azure_form_recognizer import (
|
||||
AzureOCRDocumentConverter,
|
||||
)
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.utils import Secret
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component(
|
||||
"converter",
|
||||
AzureOCRDocumentConverter(
|
||||
endpoint="azure_resource_url",
|
||||
api_key=Secret.from_token("<your-api-key>"),
|
||||
),
|
||||
)
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
file_names = ["my_file.pdf"]
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,75 @@
|
||||
---
|
||||
title: "CSVToDocument"
|
||||
id: csvtodocument
|
||||
slug: "/csvtodocument"
|
||||
description: "Converts CSV files to documents."
|
||||
---
|
||||
|
||||
# CSVToDocument
|
||||
|
||||
Converts CSV files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/csv.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`CSVToDocument` converts one or more CSV files into a text document.
|
||||
|
||||
The component uses UTF-8 encoding by default, but you may specify a different encoding if needed during initialization.
|
||||
You can optionally attach metadata to each document with a `meta` parameter when running the component.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters.csv import CSVToDocument
|
||||
|
||||
converter = CSVToDocument()
|
||||
results = converter.run(
|
||||
sources=["sample.csv"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
|
||||
print(documents[0].content)
|
||||
# 'col1,col2\now1,row1\nrow2row2\n'
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import CSVToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", CSVToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,138 @@
|
||||
---
|
||||
title: "DoclingConverter"
|
||||
id: doclingconverter
|
||||
slug: "/doclingconverter"
|
||||
description: "`DoclingConverter` converts PDF, DOCX, HTML, and other document formats to Haystack Documents using Docling, with support for layout-aware chunking, Markdown, and JSON export."
|
||||
---
|
||||
|
||||
# DoclingConverter
|
||||
|
||||
`DoclingConverter` converts PDF, DOCX, HTML, and other document formats to Haystack Documents using [Docling](https://ds4sd.github.io/docling/), a document parsing library that understands document structure including layout, tables, and headings.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths, URLs, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Docling](/reference/integrations-docling) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/docling |
|
||||
| **Package name** | `docling-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `DoclingConverter` takes a list of file paths, URLs, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects and uses Docling to parse them into a rich document representation that captures layout, tables, headings, and other structural elements.
|
||||
|
||||
The component supports three export modes, controlled by the `export_type` parameter:
|
||||
|
||||
- **`ExportType.DOC_CHUNKS`** (default): Chunks each document using Docling's `HybridChunker` and returns one [`Document`](../../concepts/data-classes.mdx#document) per chunk. Chunk metadata includes structural context from Docling. Use this mode for indexing pipelines where downstream retrieval benefits from semantically coherent chunks.
|
||||
- **`ExportType.MARKDOWN`**: Exports each input document as a single Markdown string in one [`Document`](../../concepts/data-classes.mdx#document). Use this mode when you want to preserve the full document content as formatted text.
|
||||
- **`ExportType.JSON`**: Serializes the full Docling document to a JSON string in one [`Document`](../../concepts/data-classes.mdx#document). Use this mode when you need access to the complete structured representation.
|
||||
|
||||
You can customize parsing behavior by passing a pre-configured `DocumentConverter` instance via the `converter` parameter, and pass additional keyword arguments to Docling's conversion step via `convert_kwargs`. For `ExportType.MARKDOWN`, use `md_export_kwargs` to control Markdown rendering options (for example, image placeholder text). For `ExportType.DOC_CHUNKS`, provide a custom `BaseChunker` instance via the `chunker` parameter.
|
||||
|
||||
Document metadata is populated by a `MetaExtractor` instance. The default `MetaExtractor` adds Docling-specific metadata (chunk structure or document origin) under the `dl_meta` key. You can supply a custom `BaseMetaExtractor` implementation via the `meta_extractor` parameter. Additional metadata can be attached to all output Documents by passing a dictionary to the `meta` run parameter, or per source by passing a list of dictionaries.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the Docling integration:
|
||||
|
||||
```shell
|
||||
pip install docling-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.docling import (
|
||||
DoclingConverter,
|
||||
ExportType,
|
||||
)
|
||||
|
||||
# Default: chunk-based output
|
||||
converter = DoclingConverter()
|
||||
result = converter.run(sources=["report.pdf", "notes.docx"])
|
||||
documents = result["documents"]
|
||||
|
||||
# Full document as Markdown
|
||||
converter = DoclingConverter(export_type=ExportType.MARKDOWN)
|
||||
result = converter.run(sources=["report.pdf"])
|
||||
documents = result["documents"]
|
||||
print(documents[0].content)
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.docling import DoclingConverter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", DoclingConverter())
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": ["report.pdf", "manual.docx"]}})
|
||||
```
|
||||
|
||||
Because `DoclingConverter` with `ExportType.DOC_CHUNKS` already chunks the documents, you typically don't need a separate `DocumentSplitter` in the pipeline.
|
||||
|
||||
## Additional Features
|
||||
|
||||
### Custom chunking
|
||||
|
||||
Provide a custom Docling chunker to control how documents are split:
|
||||
|
||||
```python
|
||||
from docling.chunking import HybridChunker
|
||||
from haystack_integrations.components.converters.docling import DoclingConverter
|
||||
|
||||
chunker = HybridChunker(tokenizer="BAAI/bge-small-en-v1.5", max_tokens=256)
|
||||
converter = DoclingConverter(chunker=chunker)
|
||||
result = converter.run(sources=["report.pdf"])
|
||||
```
|
||||
|
||||
### Attaching metadata
|
||||
|
||||
Pass a single dictionary to apply metadata to all output Documents, or a list to set metadata per source:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.docling import DoclingConverter
|
||||
|
||||
converter = DoclingConverter()
|
||||
|
||||
# Same metadata for all sources
|
||||
result = converter.run(
|
||||
sources=["a.pdf", "b.pdf"],
|
||||
meta={"project": "research"},
|
||||
)
|
||||
|
||||
# Per-source metadata
|
||||
result = converter.run(
|
||||
sources=["a.pdf", "b.pdf"],
|
||||
meta=[{"title": "Report A"}, {"title": "Report B"}],
|
||||
)
|
||||
```
|
||||
|
||||
### Processing in-memory files
|
||||
|
||||
Pass [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects to convert files loaded into memory. Set `file_path` in the ByteStream metadata so Docling can detect the file format:
|
||||
|
||||
```python
|
||||
from haystack.dataclasses import ByteStream
|
||||
from haystack_integrations.components.converters.docling import DoclingConverter
|
||||
|
||||
with open("report.pdf", "rb") as f:
|
||||
data = f.read()
|
||||
|
||||
source = ByteStream(data=data, meta={"file_path": "report.pdf"})
|
||||
converter = DoclingConverter()
|
||||
result = converter.run(sources=[source])
|
||||
```
|
||||
@@ -0,0 +1,161 @@
|
||||
---
|
||||
title: "DoclingServeConverter"
|
||||
id: doclingserveconverter
|
||||
slug: "/doclingserveconverter"
|
||||
description: "`DoclingServeConverter` converts PDF, DOCX, HTML, and other document formats to Haystack Documents by calling a remote DoclingServe HTTP server, with no local ML dependencies."
|
||||
---
|
||||
|
||||
# DoclingServeConverter
|
||||
|
||||
`DoclingServeConverter` converts PDF, DOCX, HTML, and other document formats to Haystack Documents by calling a [DoclingServe](https://github.com/docling-project/docling-serve) HTTP server. Unlike the local [`DoclingConverter`](doclingconverter.mdx), this component has no heavy ML dependencies — all document parsing happens on the remote server.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths, URLs, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Docling Serve](/reference/integrations-docling_serve) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/docling_serve |
|
||||
| **Package name** | `docling-serve-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `DoclingServeConverter` takes a list of file paths, URLs, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects and sends them to a running DoclingServe instance for parsing. Local files and `ByteStream` objects are uploaded to the `/v1/convert/file` endpoint; URL strings are sent to `/v1/convert/source`.
|
||||
|
||||
The component supports three export modes, controlled by the `export_type` parameter:
|
||||
|
||||
- **`ExportType.MARKDOWN`** (default): Returns the document content as a Markdown string. Use this mode when you want well-structured text output with formatting preserved.
|
||||
- **`ExportType.TEXT`**: Returns plain text extracted from the document. Use this mode when you need clean, unformatted text.
|
||||
- **`ExportType.JSON`**: Returns the full Docling document representation as a JSON string. Use this mode when you need access to the complete structured representation.
|
||||
|
||||
Each source produces one [`Document`](../../concepts/data-classes.mdx#document) in the output. Sources that fail to convert are skipped with a warning logged.
|
||||
|
||||
You can pass additional conversion options to the DoclingServe API via the `convert_options` parameter (for example, `{"do_ocr": True, "ocr_engine": "tesseract"}`). If the DoclingServe instance requires authentication, pass the API key via the `api_key` parameter or set the `DOCLING_SERVE_API_KEY` environment variable.
|
||||
|
||||
The component supports both synchronous (`run`) and asynchronous (`run_async`) execution.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the Docling Serve integration:
|
||||
|
||||
```shell
|
||||
pip install docling-serve-haystack
|
||||
```
|
||||
|
||||
Start a DoclingServe instance locally (requires Docker):
|
||||
|
||||
```shell
|
||||
docker run -p 5001:5001 ghcr.io/docling-project/docling-serve-cpu:latest
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.docling_serve import (
|
||||
DoclingServeConverter,
|
||||
)
|
||||
|
||||
# Default: Markdown output
|
||||
converter = DoclingServeConverter(base_url="http://localhost:5001")
|
||||
result = converter.run(sources=["report.pdf", "notes.docx"])
|
||||
documents = result["documents"]
|
||||
print(documents[0].content[:200])
|
||||
|
||||
# Plain text output
|
||||
from haystack_integrations.components.converters.docling_serve import ExportType
|
||||
|
||||
converter = DoclingServeConverter(
|
||||
base_url="http://localhost:5001",
|
||||
export_type=ExportType.TEXT,
|
||||
)
|
||||
result = converter.run(sources=["report.pdf"])
|
||||
print(result["documents"][0].content)
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.docling_serve import (
|
||||
DoclingServeConverter,
|
||||
)
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component(
|
||||
"converter",
|
||||
DoclingServeConverter(base_url="http://localhost:5001"),
|
||||
)
|
||||
pipeline.add_component("splitter", DocumentSplitter())
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": ["report.pdf", "manual.docx"]}})
|
||||
```
|
||||
|
||||
## Additional Features
|
||||
|
||||
### Converting URLs directly
|
||||
|
||||
Pass URL strings to convert remote documents without downloading them first:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.docling_serve import (
|
||||
DoclingServeConverter,
|
||||
)
|
||||
|
||||
converter = DoclingServeConverter(base_url="http://localhost:5001")
|
||||
result = converter.run(sources=["https://arxiv.org/pdf/2602.17316"])
|
||||
print(result["documents"][0].content[:200])
|
||||
```
|
||||
|
||||
### Attaching metadata
|
||||
|
||||
Pass a single dictionary to apply metadata to all output Documents, or a list to set metadata per source:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.docling_serve import (
|
||||
DoclingServeConverter,
|
||||
)
|
||||
|
||||
converter = DoclingServeConverter(base_url="http://localhost:5001")
|
||||
|
||||
# Same metadata for all sources
|
||||
result = converter.run(
|
||||
sources=["a.pdf", "b.pdf"],
|
||||
meta={"project": "research"},
|
||||
)
|
||||
|
||||
# Per-source metadata
|
||||
result = converter.run(
|
||||
sources=["a.pdf", "b.pdf"],
|
||||
meta=[{"title": "Report A"}, {"title": "Report B"}],
|
||||
)
|
||||
```
|
||||
|
||||
### Processing in-memory files
|
||||
|
||||
Pass [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects to convert files loaded into memory. Set `file_path` in the ByteStream metadata so DoclingServe can detect the file format:
|
||||
|
||||
```python
|
||||
from haystack.dataclasses import ByteStream
|
||||
from haystack_integrations.components.converters.docling_serve import (
|
||||
DoclingServeConverter,
|
||||
)
|
||||
|
||||
with open("report.pdf", "rb") as f:
|
||||
data = f.read()
|
||||
|
||||
source = ByteStream(data=data, meta={"file_path": "report.pdf"})
|
||||
converter = DoclingServeConverter(base_url="http://localhost:5001")
|
||||
result = converter.run(sources=[source])
|
||||
```
|
||||
@@ -0,0 +1,154 @@
|
||||
---
|
||||
title: "DocumentToImageContent"
|
||||
id: documenttoimagecontent
|
||||
slug: "/documenttoimagecontent"
|
||||
description: "`DocumentToImageContent` extracts visual data from image or PDF file-based documents and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image question-answering and captioning."
|
||||
---
|
||||
|
||||
# DocumentToImageContent
|
||||
|
||||
`DocumentToImageContent` extracts visual data from image or PDF file-based documents and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image question-answering and captioning.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a `ChatPromptBuilder` in a query pipeline |
|
||||
| **Mandatory run variables** | `documents`: A list of documents to process. Each document should have metadata containing at minimum a 'file_path_meta_field' key. PDF documents additionally require a 'page_number' key to specify which page to convert. |
|
||||
| **Output variables** | `image_contents`: A list of `ImageContent` objects |
|
||||
| **API reference** | [Image Converters](/reference/image-converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/document_to_image.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`DocumentToImageContent` processes a list of documents containing image or PDF file paths and converts them into `ImageContent` objects.
|
||||
|
||||
- For images, it reads and encodes the file directly.
|
||||
- For PDFs, it extracts the specified page (through `page_number` in metadata) and converts it to an image.
|
||||
|
||||
By default, it looks for the file path in the `file_path` metadata field. You can customize this with the `file_path_meta_field` parameter. The `root_path` lets you specify a common base directory for file resolution.
|
||||
|
||||
This component is typically used in query pipelines right before a `ChatPromptBuilder` when you would like to add Images to your user prompt.
|
||||
|
||||
If `size` is provided, the images will be resized while maintaining aspect ratio. This reduces file size, memory usage, and processing time, which is beneficial when working with models that have resolution constraints or when transmitting images to remote services.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack import Document
|
||||
from haystack.components.converters.image.document_to_image import (
|
||||
DocumentToImageContent,
|
||||
)
|
||||
|
||||
converter = DocumentToImageContent(
|
||||
file_path_meta_field="file_path",
|
||||
root_path="/data/documents",
|
||||
detail="high",
|
||||
size=(800, 600),
|
||||
)
|
||||
|
||||
documents = [
|
||||
Document(content="Photo of a mountain", meta={"file_path": "mountain.jpg"}),
|
||||
Document(
|
||||
content="First page of a report",
|
||||
meta={"file_path": "report.pdf", "page_number": 1},
|
||||
),
|
||||
]
|
||||
|
||||
result = converter.run(documents)
|
||||
image_contents = result["image_contents"]
|
||||
print(image_contents)
|
||||
|
||||
# [
|
||||
# ImageContent(
|
||||
# base64_image="/9j/4A...", mime_type="image/jpeg", detail="high",
|
||||
# meta={"file_path": "mountain.jpg"}
|
||||
# ),
|
||||
# ImageContent(
|
||||
# base64_image="/9j/4A...", mime_type="image/jpeg", detail="high",
|
||||
# meta={"file_path": "report.pdf", "page_number": 1}
|
||||
# )
|
||||
# ]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
You can use `DocumentToImageContent` in multimodal indexing pipelines before passing to an Embedder or captioning model.
|
||||
|
||||
```python
|
||||
from haystack import Document, Pipeline
|
||||
from haystack.components.builders import ChatPromptBuilder
|
||||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||||
from haystack.components.converters.image.document_to_image import (
|
||||
DocumentToImageContent,
|
||||
)
|
||||
|
||||
# Query pipeline
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("image_converter", DocumentToImageContent(detail="auto"))
|
||||
pipeline.add_component(
|
||||
"chat_prompt_builder",
|
||||
ChatPromptBuilder(
|
||||
required_variables=["question"],
|
||||
template="""{% message role="system" %}
|
||||
You are a friendly assistant that answers questions based on provided images.
|
||||
{% endmessage %}
|
||||
|
||||
{%- message role="user" -%}
|
||||
Only provide an answer to the question using the images provided.
|
||||
|
||||
Question: {{ question }}
|
||||
Answer:
|
||||
|
||||
{%- for img in image_contents -%}
|
||||
{{ img | templatize_part }}
|
||||
{%- endfor -%}
|
||||
{%- endmessage -%}
|
||||
""",
|
||||
),
|
||||
)
|
||||
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
|
||||
|
||||
pipeline.connect("image_converter", "chat_prompt_builder.image_contents")
|
||||
pipeline.connect("chat_prompt_builder", "llm")
|
||||
|
||||
documents = [
|
||||
Document(content="Cat image", meta={"file_path": "cat.jpg"}),
|
||||
Document(content="Doc intro", meta={"file_path": "paper.pdf", "page_number": 1}),
|
||||
]
|
||||
|
||||
result = pipeline.run(
|
||||
data={
|
||||
"image_converter": {"documents": documents},
|
||||
"chat_prompt_builder": {"question": "What color is the cat?"},
|
||||
},
|
||||
)
|
||||
print(result)
|
||||
|
||||
# {
|
||||
# "llm": {
|
||||
# "replies": [
|
||||
# ChatMessage(
|
||||
# _role=<ChatRole.ASSISTANT: 'assistant'>,
|
||||
# _content=[TextContent(text="The cat is orange with some black.")],
|
||||
# _name=None,
|
||||
# _meta={
|
||||
# "model": "gpt-4o-mini-2024-07-18",
|
||||
# "index": 0,
|
||||
# "finish_reason": "stop",
|
||||
# "usage": {...},
|
||||
# },
|
||||
# )
|
||||
# ]
|
||||
# }
|
||||
# }
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
🧑🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
|
||||
@@ -0,0 +1,82 @@
|
||||
---
|
||||
title: "DOCXToDocument"
|
||||
id: docxtodocument
|
||||
slug: "/docxtodocument"
|
||||
description: "Convert DOCX files to documents."
|
||||
---
|
||||
|
||||
# DOCXToDocument
|
||||
|
||||
Convert DOCX files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: DOCX file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/docx.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `DOCXToDocument` component converts DOCX files into documents. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. By defining the table format (CSV or Markdown), you can use this component to extract tables in your DOCX files. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
## Usage
|
||||
|
||||
First, install the`python-docx` package to start using this converter:
|
||||
|
||||
```shell
|
||||
pip install python-docx
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters.docx import DOCXToDocument, DOCXTableFormat
|
||||
|
||||
converter = DOCXToDocument()
|
||||
# or define the table format
|
||||
converter = DOCXToDocument(table_format=DOCXTableFormat.CSV)
|
||||
|
||||
results = converter.run(
|
||||
sources=["sample.docx"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
|
||||
print(documents[0].content)
|
||||
|
||||
# 'This is the text from the DOCX file.'
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import DOCXToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", DOCXToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,106 @@
|
||||
---
|
||||
title: "FileToFileContent"
|
||||
id: filetofilecontent
|
||||
slug: "/filetofilecontent"
|
||||
description: "`FileToFileContent` reads local files and converts them into `FileContent` objects"
|
||||
---
|
||||
|
||||
# FileToFileContent
|
||||
|
||||
`FileToFileContent` reads local files and converts them into `FileContent` objects. These are ready for multimodal AI pipelines that need to pass PDFs and other file types to an LLM.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a `ChatPromptBuilder` in a query pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths or ByteStreams |
|
||||
| **Output variables** | `file_contents`: A list of `FileContent` objects |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/file_to_file_content.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`FileToFileContent` processes a list of file sources and converts them into `FileContent` objects that can be embedded
|
||||
into a `ChatMessage` and passed to a Language Model.
|
||||
|
||||
Each source can be:
|
||||
|
||||
- A file path (string or `Path`), or
|
||||
- A `ByteStream` object.
|
||||
|
||||
Optionally, you can provide extra provider-specific information using the `extra` parameter. This can be a single dictionary (applied to all files) or a list matching the length of `sources`.
|
||||
|
||||
Support for passing files to LLMs varies by provider. Some providers do not support file inputs, some restrict support
|
||||
to PDF files, and others accept a wider range of file types.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import FileToFileContent
|
||||
|
||||
converter = FileToFileContent()
|
||||
|
||||
sources = ["document.pdf", "recording.mp3"]
|
||||
|
||||
result = converter.run(sources=sources)
|
||||
file_contents = result["file_contents"]
|
||||
print(file_contents)
|
||||
|
||||
# [
|
||||
# FileContent(
|
||||
# base64_data='JVBERi0x...', mime_type='application/pdf',
|
||||
# filename='document.pdf', extra={}
|
||||
# ),
|
||||
# FileContent(
|
||||
# base64_data='SUQzBA...', mime_type='audio/mpeg',
|
||||
# filename='recording.mp3', extra={}
|
||||
# )
|
||||
# ]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Use `FileToFileContent` together with a `LinkContentFetcher` and a `ChatPromptBuilder` to build a pipeline that fetches a remote file, converts it, and passes it to an LLM.
|
||||
|
||||
```python
|
||||
from haystack.components.converters import FileToFileContent
|
||||
from haystack.components.fetchers import LinkContentFetcher
|
||||
from haystack.components.generators.chat.openai import OpenAIChatGenerator
|
||||
from haystack.components.builders import ChatPromptBuilder
|
||||
|
||||
from haystack import Pipeline
|
||||
|
||||
template = """
|
||||
{% message role="user"%}
|
||||
{% for file in files %}
|
||||
{{ file | templatize_part }}
|
||||
{% endfor %}
|
||||
What's the main takeaway of the following document? Just one sentence.
|
||||
{% endmessage %}
|
||||
"""
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("fetcher", LinkContentFetcher())
|
||||
pipeline.add_component("converter", FileToFileContent())
|
||||
pipeline.add_component("prompt_builder", ChatPromptBuilder(template=template))
|
||||
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4.1-mini"))
|
||||
|
||||
pipeline.connect("fetcher", "converter")
|
||||
pipeline.connect("converter", "prompt_builder")
|
||||
pipeline.connect("prompt_builder", "llm")
|
||||
|
||||
results = pipeline.run({"fetcher": {"urls": ["https://arxiv.org/pdf/2309.08632"]}})
|
||||
|
||||
print(results["llm"]["replies"][0].text)
|
||||
|
||||
# The document is a satirical paper humorously claiming that pretraining a
|
||||
# small language model exclusively on evaluation benchmark test sets can achieve
|
||||
# perfect performance, highlighting issues of data contamination in model
|
||||
# evaluation.
|
||||
```
|
||||
@@ -0,0 +1,71 @@
|
||||
---
|
||||
title: "HTMLToDocument"
|
||||
id: htmltodocument
|
||||
slug: "/htmltodocument"
|
||||
description: "A component that converts HTML files to documents."
|
||||
---
|
||||
|
||||
# HTMLToDocument
|
||||
|
||||
A component that converts HTML files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of HTML file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/html.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `HTMLToDocument` component converts HTML files into documents. It can be used in an indexing pipeline to index the contents of an HTML file into a Document Store or even in a querying pipeline after the [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx). The `HTMLToDocument` component takes a list of HTML file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and converts the files to a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
When you initialize the component, you can optionally set `extraction_kwargs`, a dictionary containing keyword arguments to customize the extraction process. These are passed to the underlying Trafilatura `extract` function. For the full list of available arguments, see the [Trafilatura documentation](https://trafilatura.readthedocs.io/en/latest/corefunctions.html#extract).
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.components.converters import HTMLToDocument
|
||||
|
||||
converter = HTMLToDocument()
|
||||
|
||||
docs = converter.run(sources=[Path("saved_page.html")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Here's an example of an indexing pipeline that writes the contents of an HTML file into an `InMemoryDocumentStore`:
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import HTMLToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", HTMLToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,111 @@
|
||||
---
|
||||
title: "ImageFileToDocument"
|
||||
id: imagefiletodocument
|
||||
slug: "/imagefiletodocument"
|
||||
description: "Converts image file references into empty `Document` objects with associated metadata."
|
||||
---
|
||||
|
||||
# ImageFileToDocument
|
||||
|
||||
Converts image file references into empty `Document` objects with associated metadata.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a component that processes images, like `SentenceTransformersImageDocumentEmbedder` or `LLMDocumentContentExtractor` |
|
||||
| **Mandatory run variables** | `sources`: A list of image file paths or ByteStreams |
|
||||
| **Output variables** | `documents`: A list of empty Document objects with associated metadata |
|
||||
| **API reference** | [Image Converters](/reference/image-converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/file_to_document.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`ImageFileToDocument` converts image file sources into empty `Document` objects with associated metadata.
|
||||
|
||||
This component is useful in pipelines where image file paths need to be wrapped in `Document` objects to be processed by downstream components such as `SentenceTransformersImageDocumentEmbedder` or `LLMDocumentContentExtractor`.
|
||||
|
||||
It _does not_ extract any content from the image files, but instead creates `Document` objects with `None` as their content and attaches metadata such as file path and any user-provided values.
|
||||
|
||||
Each source can be:
|
||||
|
||||
- A file path (string or `Path`), or
|
||||
- A `ByteStream` object.
|
||||
|
||||
Optionally, you can provide metadata using the `meta` parameter. This can be a single dictionary (applied to all documents) or a list matching the length of `sources`.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
This component is primarily meant to be used in pipelines.
|
||||
|
||||
```python
|
||||
|
||||
from haystack.components.converters.image import ImageFileToDocument
|
||||
|
||||
converter = ImageFileToDocument()
|
||||
|
||||
sources = ["image.jpg", "another_image.png"]
|
||||
|
||||
result = converter.run(sources=sources)
|
||||
documents = result["documents"]
|
||||
|
||||
print(documents)
|
||||
|
||||
# [Document(id=..., content=None, meta={'file_path': 'image.jpg'}),
|
||||
# Document(id=..., content=None, meta={'file_path': 'another_image.png'})]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
In the following Pipeline, image documents are created using the `ImageFileToDocument` component, then they are enriched with image embeddings and saved in the Document Store.
|
||||
|
||||
The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:
|
||||
|
||||
```shell
|
||||
pip install sentence-transformers-haystack
|
||||
```
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.converters.image import ImageFileToDocument
|
||||
from haystack_integrations.components.embedders.sentence_transformers import (
|
||||
SentenceTransformersDocumentImageEmbedder,
|
||||
)
|
||||
from haystack.components.writers.document_writer import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
|
||||
# Create our document store
|
||||
doc_store = InMemoryDocumentStore()
|
||||
|
||||
# Define pipeline with components
|
||||
indexing_pipe = Pipeline()
|
||||
indexing_pipe.add_component(
|
||||
"image_converter",
|
||||
ImageFileToDocument(store_full_path=True),
|
||||
)
|
||||
indexing_pipe.add_component(
|
||||
"image_doc_embedder",
|
||||
SentenceTransformersDocumentImageEmbedder(),
|
||||
)
|
||||
indexing_pipe.add_component("document_writer", DocumentWriter(doc_store))
|
||||
|
||||
indexing_pipe.connect("image_converter.documents", "image_doc_embedder.documents")
|
||||
indexing_pipe.connect("image_doc_embedder.documents", "document_writer.documents")
|
||||
|
||||
indexing_result = indexing_pipe.run(
|
||||
data={"image_converter": {"sources": ["apple.jpg", "kiwi.png"]}},
|
||||
)
|
||||
|
||||
indexed_documents = doc_store.filter_documents()
|
||||
print(f"Indexed {len(indexed_documents)} documents")
|
||||
# Indexed 2 documents
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
🧑🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
|
||||
@@ -0,0 +1,129 @@
|
||||
---
|
||||
title: "ImageFileToImageContent"
|
||||
id: imagefiletoimagecontent
|
||||
slug: "/imagefiletoimagecontent"
|
||||
description: "`ImageFileToImageContent` reads local image files and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image captioning, visual QA, or prompt-based generation."
|
||||
---
|
||||
|
||||
# ImageFileToImageContent
|
||||
|
||||
`ImageFileToImageContent` reads local image files and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image captioning, visual QA, or prompt-based generation.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a `ChatPromptBuilder` in a query pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of image file paths or ByteStreams |
|
||||
| **Output variables** | `image_contents`: A list of ImageContent objects |
|
||||
| **API reference** | [Image Converters](/reference/image-converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/file_to_image.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`ImageFileToImageContent` processes a list of image sources and converts them into `ImageContent` objects. These can be used in multimodal pipelines that require base64-encoded image input.
|
||||
|
||||
Each source can be:
|
||||
|
||||
- A file path (string or `Path`), or
|
||||
- A `ByteStream` object.
|
||||
|
||||
Optionally, you can provide metadata using the `meta` parameter. This can be a single dictionary (applied to all images) or a list matching the length of `sources`.
|
||||
|
||||
Use the `size` parameter to resize images while preserving aspect ratio. This reduces memory usage and transmission size, which is helpful when working with remote models or limited-resource environments.
|
||||
|
||||
This component is often used in query pipelines just before a `ChatPromptBuilder`.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
|
||||
from haystack.components.converters.image import ImageFileToImageContent
|
||||
|
||||
converter = ImageFileToImageContent(detail="high", size=(800, 600))
|
||||
|
||||
sources = ["cat.jpg", "scenery.png"]
|
||||
|
||||
result = converter.run(sources=sources)
|
||||
image_contents = result["image_contents"]
|
||||
print(image_contents)
|
||||
|
||||
# [
|
||||
# ImageContent(
|
||||
# base64_image="/9j/4A...", mime_type="image/jpeg", detail="high",
|
||||
# meta={"file_path": "cat.jpg"}
|
||||
# ),
|
||||
# ImageContent(
|
||||
# base64_image="/9j/4A...", mime_type="image/png", detail="high",
|
||||
# meta={"file_path": "scenery.png"}
|
||||
# )
|
||||
# ]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Use `ImageFileToImageContent` to supply image data to a `ChatPromptBuilder` for multimodal QA or captioning with an LLM.
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.builders import ChatPromptBuilder
|
||||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||||
from haystack.components.converters.image import ImageFileToImageContent
|
||||
|
||||
# Query pipeline
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("image_converter", ImageFileToImageContent(detail="auto"))
|
||||
pipeline.add_component(
|
||||
"chat_prompt_builder",
|
||||
ChatPromptBuilder(
|
||||
required_variables=["question"],
|
||||
template="""{% message role="system" %}
|
||||
You are a helpful assistant that answers questions using the provided images.
|
||||
{% endmessage %}
|
||||
|
||||
{% message role="user" %}
|
||||
Question: {{ question }}
|
||||
|
||||
{% for img in image_contents %}
|
||||
{{ img | templatize_part }}
|
||||
{% endfor %}
|
||||
{% endmessage %}
|
||||
""",
|
||||
),
|
||||
)
|
||||
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
|
||||
|
||||
pipeline.connect("image_converter", "chat_prompt_builder.image_contents")
|
||||
pipeline.connect("chat_prompt_builder", "llm")
|
||||
|
||||
sources = ["apple.jpg", "haystack-logo.png"]
|
||||
|
||||
result = pipeline.run(
|
||||
data={
|
||||
"image_converter": {"sources": sources},
|
||||
"chat_prompt_builder": {"question": "Describe the Haystack logo."},
|
||||
},
|
||||
)
|
||||
print(result)
|
||||
|
||||
# {
|
||||
# "llm": {
|
||||
# "replies": [
|
||||
# ChatMessage(
|
||||
# _role=<ChatRole.ASSISTANT: 'assistant'>,
|
||||
# _content=[TextContent(text="The Haystack logo features...")],
|
||||
# ...
|
||||
# )
|
||||
# ]
|
||||
# }
|
||||
# }
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
🧑🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
|
||||
@@ -0,0 +1,119 @@
|
||||
---
|
||||
title: "JSONConverter"
|
||||
id: jsonconverter
|
||||
slug: "/jsonconverter"
|
||||
description: "Converts JSON files to text documents."
|
||||
---
|
||||
|
||||
# JSONConverter
|
||||
|
||||
Converts JSON files to text documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory init variables** | ONE OF, OR BOTH: <br /> <br />`jq_schema`: A jq filter string to extract content <br /> <br />`content_key`: A key string to extract document content |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/json.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`JSONConverter` converts one or more JSON files into a text document.
|
||||
|
||||
### Parameters Overview
|
||||
|
||||
To initialize `JSONConverter`, you must provide either `jq_schema`, or `content_key` parameter, or both.
|
||||
|
||||
`jq_schema` parameter filter extracts nested data from JSON files. Refer to the [jq documentation](https://jqlang.github.io/jq/) for filter syntax. If not set, the entire JSON file is used.
|
||||
|
||||
The `content_key` parameter lets you specify which key in the extracted data will be the document's content.
|
||||
|
||||
- If both `jq_schema` and `content_key` are set, the `content_key` is searched in the data extracted by `jq_schema`. Non-object data will be skipped.
|
||||
- If only `jq_schema` is set, the extracted value must be scalar; objects or arrays will be skipped.
|
||||
- If only `content_key` is set, the source must be a JSON object, or it will be skipped.
|
||||
|
||||
Check out the [API reference](../converters.mdx) for the full list of parameters.
|
||||
|
||||
## Usage
|
||||
|
||||
You need to install the `jq` package to use this Converter:
|
||||
|
||||
```shell
|
||||
pip install jq
|
||||
```
|
||||
|
||||
### Example
|
||||
|
||||
Here is an example of simple component usage:
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
from haystack.components.converters import JSONConverter
|
||||
from haystack.dataclasses import ByteStream
|
||||
|
||||
source = ByteStream.from_string(
|
||||
json.dumps({"text": "This is the content of my document"}),
|
||||
)
|
||||
|
||||
converter = JSONConverter(content_key="text")
|
||||
results = converter.run(sources=[source])
|
||||
documents = results["documents"]
|
||||
print(documents[0].content)
|
||||
# 'This is the content of my document'
|
||||
```
|
||||
|
||||
In the following more complex example, we provide a `jq_schema` string to filter the JSON source files and `extra_meta_fields` to extract from the filtered data:
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
from haystack.components.converters import JSONConverter
|
||||
from haystack.dataclasses import ByteStream
|
||||
|
||||
data = {
|
||||
"laureates": [
|
||||
{
|
||||
"firstname": "Enrico",
|
||||
"surname": "Fermi",
|
||||
"motivation": "for his demonstrations of the existence of new radioactive elements produced "
|
||||
"by neutron irradiation, and for his related discovery of nuclear reactions brought about by"
|
||||
" slow neutrons",
|
||||
},
|
||||
{
|
||||
"firstname": "Rita",
|
||||
"surname": "Levi-Montalcini",
|
||||
"motivation": "for their discoveries of growth factors",
|
||||
},
|
||||
],
|
||||
}
|
||||
source = ByteStream.from_string(json.dumps(data))
|
||||
converter = JSONConverter(
|
||||
jq_schema=".laureates[]",
|
||||
content_key="motivation",
|
||||
extra_meta_fields={"firstname", "surname"},
|
||||
)
|
||||
|
||||
results = converter.run(sources=[source])
|
||||
documents = results["documents"]
|
||||
print(documents[0].content)
|
||||
# 'for his demonstrations of the existence of new radioactive elements produced by
|
||||
# neutron irradiation, and for his related discovery of nuclear reactions brought
|
||||
# about by slow neutrons'
|
||||
|
||||
print(documents[0].meta)
|
||||
# {'firstname': 'Enrico', 'surname': 'Fermi'}
|
||||
|
||||
print(documents[1].content)
|
||||
# 'for their discoveries of growth factors'
|
||||
|
||||
print(documents[1].meta)
|
||||
# {'firstname': 'Rita', 'surname': 'Levi-Montalcini'}
|
||||
```
|
||||
@@ -0,0 +1,148 @@
|
||||
---
|
||||
title: "KreuzbergConverter"
|
||||
id: kreuzbergconverter
|
||||
slug: "/kreuzbergconverter"
|
||||
description: "`KreuzbergConverter` converts files to Haystack Documents using Kreuzberg, a document intelligence framework with a Rust core that extracts text from 91+ file formats entirely locally with no external API calls."
|
||||
---
|
||||
|
||||
# KreuzbergConverter
|
||||
|
||||
`KreuzbergConverter` converts files to Haystack Documents using [Kreuzberg](https://docs.kreuzberg.dev/), a document intelligence framework with a Rust core that extracts text from 91+ file formats entirely locally with no external API calls.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths, directory paths, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Kreuzberg](/reference/integrations-kreuzberg) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/kreuzberg |
|
||||
| **Package name** | `kreuzberg-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `KreuzbergConverter` takes a list of file paths, directory paths, or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects and uses Kreuzberg to extract text and metadata. All processing is performed locally with no external API calls.
|
||||
|
||||
**Supported format categories:**
|
||||
- **Documents**: PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, ODT, ODS, ODP, RTF, Pages, Keynote, Numbers, and more
|
||||
- **Images (via OCR)**: PNG, JPEG, TIFF, GIF, BMP, WebP, JPEG 2000, SVG
|
||||
- **Text/Markup**: Markdown, HTML, XML, LaTeX, Typst, JSON, YAML, reStructuredText, Jupyter notebooks
|
||||
- **Email**: EML, MSG (with attachment extraction)
|
||||
- **Archives**: ZIP, TAR, GZIP, 7Z (extracts and processes contents recursively)
|
||||
- **eBooks & Academic**: EPUB, BibTeX, DocBook, JATS
|
||||
|
||||
The component returns one Haystack [`Document`](../../concepts/data-classes.mdx#document) per source by default. When per-page extraction or chunking is enabled, it returns one Document per page or chunk instead. Documents include rich metadata such as quality scores, detected languages, extracted keywords, table data, and PDF annotations.
|
||||
|
||||
By default, batch processing is enabled, leveraging Rust's rayon thread pool for parallel extraction. Set `batch=False` for sequential processing.
|
||||
|
||||
You can customize extraction behavior with Kreuzberg's `ExtractionConfig`, either passed directly or loaded from a TOML, YAML, or JSON configuration file via `config_path`. See the [Kreuzberg documentation](https://docs.kreuzberg.dev/) for the full configuration reference.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the Kreuzberg integration:
|
||||
|
||||
```shell
|
||||
pip install kreuzberg-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
|
||||
converter = KreuzbergConverter()
|
||||
result = converter.run(sources=["report.pdf", "notes.docx"])
|
||||
documents = result["documents"]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", KreuzbergConverter())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
|
||||
pipeline.connect("converter", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": ["report.pdf", "presentation.pptx"]}})
|
||||
```
|
||||
|
||||
## Additional Features
|
||||
|
||||
### Markdown Output with OCR
|
||||
|
||||
Use `ExtractionConfig` to customize the output format and OCR settings:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
from kreuzberg import ExtractionConfig, OcrConfig
|
||||
|
||||
converter = KreuzbergConverter(
|
||||
config=ExtractionConfig(
|
||||
output_format="markdown",
|
||||
ocr=OcrConfig(backend="tesseract", language="eng"),
|
||||
),
|
||||
)
|
||||
result = converter.run(sources=["scanned_document.pdf"])
|
||||
documents = result["documents"]
|
||||
```
|
||||
|
||||
### Per-Page Extraction
|
||||
|
||||
Create one Document per page using `PageConfig`:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
from kreuzberg import ExtractionConfig, PageConfig
|
||||
|
||||
converter = KreuzbergConverter(
|
||||
config=ExtractionConfig(
|
||||
page=PageConfig(extract_pages=True),
|
||||
),
|
||||
)
|
||||
result = converter.run(sources=["multipage.pdf"])
|
||||
# One Document per page, each with page_number in metadata
|
||||
```
|
||||
|
||||
### Token Reduction
|
||||
|
||||
Reduce output size for LLM consumption with `TokenReductionConfig`. Token reduction uses TF-IDF-based extractive summarization to identify and preserve the most important terms and phrases, progressively removing less critical content such as extra whitespace, filler words, and redundant phrases. Five levels are available: `"off"` (no reduction), `"light"` (~15%), `"moderate"` (~30%), `"aggressive"` (~50%), and `"maximum"` (>50% reduction):
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
from kreuzberg import ExtractionConfig, TokenReductionConfig
|
||||
|
||||
converter = KreuzbergConverter(
|
||||
config=ExtractionConfig(
|
||||
token_reduction=TokenReductionConfig(mode="moderate"),
|
||||
),
|
||||
)
|
||||
```
|
||||
|
||||
### Config from File
|
||||
|
||||
Load extraction settings from a TOML, YAML, or JSON file:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.kreuzberg import KreuzbergConverter
|
||||
|
||||
converter = KreuzbergConverter(config_path="extraction_config.toml")
|
||||
```
|
||||
|
||||
For the full configuration reference and format support matrix, see the [Kreuzberg documentation](https://docs.kreuzberg.dev/).
|
||||
@@ -0,0 +1,96 @@
|
||||
---
|
||||
title: "LibreOfficeFileConverter"
|
||||
id: libreofficefileconverter
|
||||
slug: "/libreofficefileconverter"
|
||||
description: "A component that converts office files (documents, spreadsheets, presentations) between formats using LibreOffice's command line interface."
|
||||
---
|
||||
|
||||
# LibreOfficeFileConverter
|
||||
|
||||
A component that converts office files between formats using LibreOffice's command line interface (`soffice`).
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a document converter (e.g. [`DOCXToDocument`](./docxtodocument.mdx)) when the source files need to be converted to a format that the converter supports |
|
||||
| **Mandatory run variables** | `sources`: File paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects; `output_file_type`: The target file format |
|
||||
| **Output variables** | `output`: A list of [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **API reference** | [LibreOffice](/reference/integrations-libreoffice) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/libreoffice |
|
||||
| **Package name** | `libreoffice-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`LibreOfficeFileConverter` converts office files from one format to another using LibreOffice's `soffice` command line utility. It supports a wide range of document, spreadsheet, and presentation formats and is useful when your pipeline receives files in a format that downstream converters don't support.
|
||||
|
||||
Unlike most converters, `LibreOfficeFileConverter` outputs [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects rather than Haystack Documents. This means it's typically chained with a document converter (such as [`DOCXToDocument`](./docxtodocument.mdx) or [`PyPDFToDocument`](./pypdftodocument.mdx)) to produce the final Documents.
|
||||
|
||||
**Requires LibreOffice to be installed** and available in `PATH` as `soffice`. See the [LibreOffice installation guide](https://www.libreoffice.org/get-help/install-howto/) for details.
|
||||
|
||||
### Supported conversions
|
||||
|
||||
| Category | Input formats | Possible output formats |
|
||||
| --- | --- | --- |
|
||||
| Documents | `doc`, `docx`, `odt`, `rtf`, `txt`, `html` | `pdf`, `docx`, `doc`, `odt`, `rtf`, `txt`, `html`, `epub` |
|
||||
| Spreadsheets | `xlsx`, `xls`, `ods`, `csv` | `pdf`, `xlsx`, `xls`, `ods`, `csv`, `html` |
|
||||
| Presentations | `pptx`, `ppt`, `odp` | `pdf`, `pptx`, `ppt`, `odp`, `html`, `png`, `jpg` |
|
||||
|
||||
This is a non-exhaustive list. See the [LibreOffice filter documentation](https://help.libreoffice.org/latest/en-GB/text/shared/guide/convertfilters.html) for all supported conversions.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the LibreOffice integration:
|
||||
|
||||
```shell
|
||||
pip install libreoffice-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack_integrations.components.converters.libreoffice import (
|
||||
LibreOfficeFileConverter,
|
||||
)
|
||||
|
||||
converter = LibreOfficeFileConverter()
|
||||
result = converter.run(sources=[Path("sample.doc")], output_file_type="docx")
|
||||
bytestreams = result["output"]
|
||||
```
|
||||
|
||||
You can also set `output_file_type` at initialization to avoid passing it on every `run()` call:
|
||||
|
||||
```python
|
||||
converter = LibreOfficeFileConverter(output_file_type="pdf")
|
||||
result = converter.run(sources=[Path("report.pptx")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
A common pattern is to chain `LibreOfficeFileConverter` with a document converter. The example below converts a legacy `.doc` file to `.docx` and then extracts it as a Haystack Document:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack import Pipeline
|
||||
from haystack.components.converters import DOCXToDocument
|
||||
from haystack_integrations.components.converters.libreoffice import (
|
||||
LibreOfficeFileConverter,
|
||||
)
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component(
|
||||
"libreoffice_converter",
|
||||
LibreOfficeFileConverter(output_file_type="docx"),
|
||||
)
|
||||
pipeline.add_component("docx_converter", DOCXToDocument())
|
||||
|
||||
pipeline.connect("libreoffice_converter.output", "docx_converter.sources")
|
||||
|
||||
result = pipeline.run(
|
||||
{"libreoffice_converter": {"sources": [Path("legacy_report.doc")]}},
|
||||
)
|
||||
documents = result["docx_converter"]["documents"]
|
||||
```
|
||||
@@ -0,0 +1,105 @@
|
||||
---
|
||||
title: "MarkdownToDocument"
|
||||
id: markdowntodocument
|
||||
slug: "/markdowntodocument"
|
||||
description: "A component that converts Markdown files to documents."
|
||||
---
|
||||
|
||||
# MarkdownToDocument
|
||||
|
||||
A component that converts Markdown files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: Markdown file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/markdown.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `MarkdownToDocument` component converts Markdown files into documents. You can use it in an indexing pipeline to index the contents of a Markdown file into a Document Store. It takes a list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
When you initialize the component, you can optionally turn off progress bars by setting `progress_bar` to `False`. If you want to convert the contents of tables into a single line, you can enable that through the `table_to_single_line` parameter.
|
||||
|
||||
If your Markdown files start with YAML frontmatter, set `extract_frontmatter=True` to move that data into `Document.meta` and remove it from the converted document content. Metadata passed through the `meta` input takes precedence over frontmatter keys.
|
||||
|
||||
## Usage
|
||||
|
||||
You need to install `markdown-it-py` and `mdit_plain packages` to use the `MarkdownToDocument` component:
|
||||
|
||||
```shell
|
||||
pip install markdown-it-py mdit_plain
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import MarkdownToDocument
|
||||
|
||||
converter = MarkdownToDocument()
|
||||
|
||||
docs = converter.run(sources=Path("my_file.md"))
|
||||
```
|
||||
|
||||
### With YAML frontmatter
|
||||
|
||||
Given `equity_note.md`:
|
||||
|
||||
```markdown
|
||||
---
|
||||
ticker: AAPL
|
||||
source: earnings_call
|
||||
date: 2026-06-12
|
||||
---
|
||||
|
||||
# Thesis
|
||||
Revenue guidance improved.
|
||||
```
|
||||
|
||||
```python
|
||||
from haystack.components.converters import MarkdownToDocument
|
||||
|
||||
converter = MarkdownToDocument(extract_frontmatter=True)
|
||||
|
||||
docs = converter.run(sources=["equity_note.md"])["documents"]
|
||||
print(docs[0].meta["ticker"])
|
||||
print(docs[0].content)
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import MarkdownToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", MarkdownToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
:notebook: Tutorial: [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)
|
||||
@@ -0,0 +1,75 @@
|
||||
---
|
||||
title: "MarkItDownConverter"
|
||||
id: markitdownconverter
|
||||
slug: "/markitdownconverter"
|
||||
description: "A component that converts files (PDF, Word, PowerPoint, Excel, HTML, images, and more) to Documents using Microsoft's MarkItDown library."
|
||||
---
|
||||
|
||||
# MarkItDownConverter
|
||||
|
||||
A component that converts files to Documents using Microsoft's MarkItDown library.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: File paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [MarkItDown](/reference/integrations-markitdown) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/markitdown |
|
||||
| **Package name** | `markitdown-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`MarkItDownConverter` converts files into Haystack Documents using Microsoft's [MarkItDown](https://github.com/microsoft/markitdown) library. MarkItDown converts many file formats to Markdown, including PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx), HTML, and more. All processing is performed locally without relying on external APIs.
|
||||
|
||||
The converter accepts file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of Documents. You can attach metadata to the Documents through the `meta` input parameter.
|
||||
|
||||
:::note
|
||||
This component returns Markdown content. Avoid piping it through `DocumentCleaner()` with its default settings because `remove_extra_whitespaces=True` and `remove_empty_lines=True` can collapse line breaks and flatten headings, tables, lists, and image tags. Connect the converter directly to your next component, or disable those options if you need custom cleanup.
|
||||
:::
|
||||
|
||||
## Usage
|
||||
|
||||
Install the MarkItDown integration:
|
||||
|
||||
```shell
|
||||
pip install markitdown-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.markitdown import MarkItDownConverter
|
||||
|
||||
converter = MarkItDownConverter()
|
||||
result = converter.run(sources=["document.pdf", "report.docx"])
|
||||
documents = result["documents"]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.markitdown import MarkItDownConverter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", MarkItDownConverter())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": ["document.pdf", "report.docx"]}})
|
||||
```
|
||||
@@ -0,0 +1,192 @@
|
||||
---
|
||||
title: "MistralOCRDocumentConverter"
|
||||
id: mistralocrdocumentconverter
|
||||
slug: "/mistralocrdocumentconverter"
|
||||
description: "`MistralOCRDocumentConverter` extracts text from documents using Mistral's OCR API, with optional structured annotations for both individual image regions and full documents. It supports various input formats including local files, URLs, and Mistral file IDs."
|
||||
---
|
||||
|
||||
# MistralOCRDocumentConverter
|
||||
|
||||
`MistralOCRDocumentConverter` extracts text from documents using Mistral's OCR API, with optional structured annotations for both individual image regions and full documents. It supports various input formats including local files, URLs, and Mistral file IDs.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory init variables** | `api_key`: The Mistral API key. Can be set with `MISTRAL_API_KEY` environment variable. |
|
||||
| **Mandatory run variables** | `sources`: A list of document sources (file paths, ByteStreams, URLs, or Mistral chunks) |
|
||||
| **Output variables** | `documents`: A list of documents <br /> <br />`raw_mistral_response`: A list of raw OCR responses from Mistral API |
|
||||
| **API reference** | [Mistral](/reference/integrations-mistral) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mistral |
|
||||
| **Package name** | `mistral-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `MistralOCRDocumentConverter` takes a list of document sources and uses Mistral's OCR API to extract text from images and PDFs. It supports multiple input formats:
|
||||
|
||||
- **Local files**: File paths (str or Path) or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects
|
||||
- **Remote resources**: Document URLs, image URLs using Mistral's `DocumentURLChunk` and `ImageURLChunk`
|
||||
- **Mistral storage**: File IDs using Mistral's `FileChunk` for files previously uploaded to Mistral
|
||||
|
||||
The component returns one Haystack [`Document`](../../concepts/data-classes.mdx#document) per source, with all pages concatenated using form feed characters (`\f`) as separators. This format ensures compatibility with Haystack's [`DocumentSplitter`](../preprocessors/documentsplitter.mdx) for accurate page-wise splitting and overlap handling. The content is returned in markdown format, with images represented as `` tags.
|
||||
|
||||
By default, the component uses the `MISTRAL_API_KEY` environment variable for authentication. You can also pass an `api_key` at initialization. Local files are automatically uploaded to Mistral's storage for processing and deleted afterward (configurable with `cleanup_uploaded_files`).
|
||||
|
||||
When you initialize the component, you can optionally specify which pages to process, set limits on image extraction, configure minimum image sizes, or include base64-encoded images in the response. The default model is `"mistral-ocr-2505"`. See the [Mistral models documentation](https://docs.mistral.ai/getting-started/models/models_overview/) for available models.
|
||||
|
||||
### Structured Annotations
|
||||
|
||||
A unique feature of `MistralOCRDocumentConverter` is its support for structured annotations using Pydantic schemas:
|
||||
|
||||
- **Bounding box annotations** (`bbox_annotation_schema`): Annotate individual image regions with structured data (for example, image type, description, summary). These annotations are inserted inline after the corresponding image tags in the markdown content.
|
||||
- **Document annotations** (`document_annotation_schema`): Annotate the full document with structured data (for example, language, chapter titles, URLs). These annotations are unpacked into the document's metadata with a `source_` prefix (for example, `source_language`, `source_chapter_titles`).
|
||||
|
||||
When annotation schemas are provided, the OCR model first extracts text and structure, then a Vision LLM analyzes the content and generates structured annotations according to your defined Pydantic schemas. Note that document annotation is limited to a maximum of 8 pages. For more details, see the [Mistral documentation on annotations](https://docs.mistral.ai/capabilities/document_ai/annotations/).
|
||||
|
||||
:::note
|
||||
This component returns Markdown content. Avoid piping it through `DocumentCleaner()` with its default settings because `remove_extra_whitespaces=True` and `remove_empty_lines=True` can collapse line breaks and flatten headings, tables, and image tags. For page-aware chunking, connect the converter directly to `DocumentSplitter`, or disable those options if you need custom cleanup.
|
||||
:::
|
||||
|
||||
## Usage
|
||||
|
||||
You need to install the `mistral-haystack` integration to use `MistralOCRDocumentConverter`:
|
||||
|
||||
```shell
|
||||
pip install mistral-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
Basic usage with a local file:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.mistral import (
|
||||
MistralOCRDocumentConverter,
|
||||
)
|
||||
|
||||
converter = MistralOCRDocumentConverter(
|
||||
api_key=Secret.from_env_var("MISTRAL_API_KEY"),
|
||||
model="mistral-ocr-2505",
|
||||
)
|
||||
|
||||
result = converter.run(sources=[Path("my_document.pdf")])
|
||||
documents = result["documents"]
|
||||
```
|
||||
|
||||
Processing multiple sources with different types:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.mistral import (
|
||||
MistralOCRDocumentConverter,
|
||||
)
|
||||
from mistralai.models import DocumentURLChunk, ImageURLChunk
|
||||
|
||||
converter = MistralOCRDocumentConverter(
|
||||
api_key=Secret.from_env_var("MISTRAL_API_KEY"),
|
||||
model="mistral-ocr-2505",
|
||||
)
|
||||
|
||||
sources = [
|
||||
Path("local_document.pdf"),
|
||||
DocumentURLChunk(document_url="https://example.com/document.pdf"),
|
||||
ImageURLChunk(image_url="https://example.com/receipt.jpg"),
|
||||
]
|
||||
|
||||
result = converter.run(sources=sources)
|
||||
documents = result["documents"] # List of 3 Documents
|
||||
raw_responses = result["raw_mistral_response"] # List of 3 raw responses
|
||||
```
|
||||
|
||||
Using structured annotations:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
from pydantic import BaseModel, Field
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.mistral import (
|
||||
MistralOCRDocumentConverter,
|
||||
)
|
||||
from mistralai.models import DocumentURLChunk
|
||||
|
||||
|
||||
# Define schema for image region annotations
|
||||
class ImageAnnotation(BaseModel):
|
||||
image_type: str = Field(..., description="The type of image content")
|
||||
short_description: str = Field(
|
||||
...,
|
||||
description="Short natural-language description",
|
||||
)
|
||||
summary: str = Field(..., description="Detailed summary of the image content")
|
||||
|
||||
|
||||
# Define schema for document-level annotations
|
||||
class DocumentAnnotation(BaseModel):
|
||||
language: str = Field(..., description="Primary language of the document")
|
||||
chapter_titles: List[str] = Field(
|
||||
...,
|
||||
description="Detected chapter or section titles",
|
||||
)
|
||||
urls: List[str] = Field(..., description="URLs found in the text")
|
||||
|
||||
|
||||
converter = MistralOCRDocumentConverter(
|
||||
api_key=Secret.from_env_var("MISTRAL_API_KEY"),
|
||||
model="mistral-ocr-2505",
|
||||
)
|
||||
|
||||
sources = [DocumentURLChunk(document_url="https://example.com/report.pdf")]
|
||||
result = converter.run(
|
||||
sources=sources,
|
||||
bbox_annotation_schema=ImageAnnotation,
|
||||
document_annotation_schema=DocumentAnnotation,
|
||||
)
|
||||
|
||||
documents = result["documents"]
|
||||
# Document metadata will include:
|
||||
# - source_language: extracted from DocumentAnnotation
|
||||
# - source_chapter_titles: extracted from DocumentAnnotation
|
||||
# - source_urls: extracted from DocumentAnnotation
|
||||
# Document content will include inline image annotations
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Here's an example of an indexing pipeline that processes PDFs with OCR and writes them to a Document Store:
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.mistral import (
|
||||
MistralOCRDocumentConverter,
|
||||
)
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component(
|
||||
"converter",
|
||||
MistralOCRDocumentConverter(
|
||||
api_key=Secret.from_env_var("MISTRAL_API_KEY"),
|
||||
model="mistral-ocr-2505",
|
||||
),
|
||||
)
|
||||
pipeline.add_component("splitter", DocumentSplitter(split_by="page", split_length=1))
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
|
||||
pipeline.connect("converter", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
file_paths = ["invoice.pdf", "receipt.jpg", "contract.pdf"]
|
||||
pipeline.run({"converter": {"sources": file_paths}})
|
||||
```
|
||||
@@ -0,0 +1,78 @@
|
||||
---
|
||||
title: "MSGToDocument"
|
||||
id: msgtodocument
|
||||
slug: "/msgtodocument"
|
||||
description: "Converts Microsoft Outlook .msg files to documents."
|
||||
---
|
||||
|
||||
# MSGToDocument
|
||||
|
||||
Converts Microsoft Outlook .msg files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of .msg file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents <br /> <br />`attachments`: A list of ByteStream objects representing file attachments |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/msg.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `MSGToDocument` component converts Microsoft Outlook `.msg` files into documents. This component extracts the email metadata (such as sender, recipients, CC, BCC, subject) and body content. Additionally, any file attachments within the `.msg` file are extracted as `ByteStream` objects.
|
||||
|
||||
## Usage
|
||||
|
||||
First, install the `python-oxmsg` package to start using this converter:
|
||||
|
||||
```
|
||||
pip install python-oxmsg
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters.msg import MSGToDocument
|
||||
from datetime import datetime
|
||||
|
||||
converter = MSGToDocument()
|
||||
results = converter.run(
|
||||
sources=["sample.msg"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
attachments = results["attachments"]
|
||||
|
||||
print(documents[0].content)
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
The following setup enables efficient extraction, preprocessing, and indexing of `.msg` email files within a Haystack pipeline:
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.routers import FileTypeRouter
|
||||
from haystack.components.converters import MSGToDocument
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
router = FileTypeRouter(mime_types=["application/vnd.ms-outlook"])
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("router", router)
|
||||
pipeline.add_component("converter", MSGToDocument())
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
|
||||
pipeline.connect("router.application/vnd.ms-outlook", "converter.sources")
|
||||
pipeline.connect("converter.documents", "writer.documents")
|
||||
|
||||
file_names = ["email1.msg", "email2.msg"]
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,80 @@
|
||||
---
|
||||
title: "MultiFileConverter"
|
||||
id: multifileconverter
|
||||
slug: "/multifileconverter"
|
||||
description: "Converts CSV, DOCX, HTML, JSON, MD, PPTX, PDF, TXT, and XSLX files to documents."
|
||||
---
|
||||
|
||||
# MultiFileConverter
|
||||
|
||||
Converts CSV, DOCX, HTML, JSON, MD, PPTX, PDF, TXT, and XSLX files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before PreProcessors , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of file paths or ByteStream objects |
|
||||
| **Output variables** | `documents`: A list of converted documents <br /> <br />`unclassified`: A list of uncategorized file paths or byte streams |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/multi_file_converter.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`MultiFileConverter` converts input files of various file types into documents.
|
||||
|
||||
It is a SuperComponent that combines a [`FileTypeRouter`](../routers/filetyperouter.mdx), nine converters and a [`DocumentJoiner`](../joiners/documentjoiner.mdx) into a single component.
|
||||
|
||||
### Parameters
|
||||
|
||||
To initialize `MultiFileConverter`, there are no mandatory parameters. Optionally, you can provide `encoding` and `json_content_key` parameters.
|
||||
|
||||
The `json_content_key` parameter lets you specify for the JSON files which key in the extracted data will be the document's content. The parameter is passed on to the underlying [`JSONConverter`](jsonconverter.mdx) component.
|
||||
|
||||
The `encoding` parameter lets you specify the default encoding of the TXT, CSV, and MD files. If you don't provide any value, the component uses `utf-8` by default. Note that if the encoding is specified in the metadata of an input ByteStream, it will override this parameter's setting. The parameter is passed on to the underlying [`TextFileToDocument`](textfiletodocument.mdx) and [`CSVToDocument`](csvtodocument.mdx) components.
|
||||
|
||||
## Usage
|
||||
|
||||
Install dependencies for all supported file types to use the `MultiFileConverter`:
|
||||
|
||||
```shell
|
||||
pip install pypdf markdown-it-py mdit_plain trafilatura python-pptx python-docx jq openpyxl tabulate pandas
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import MultiFileConverter
|
||||
|
||||
converter = MultiFileConverter()
|
||||
converter.run(sources=["test.txt", "test.pdf"], meta={})
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
You can also use `MultiFileConverter` in your indexing pipeline.
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.converters import MultiFileConverter
|
||||
from haystack.components.preprocessors import DocumentPreprocessor
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", MultiFileConverter())
|
||||
pipeline.add_component("preprocessor", DocumentPreprocessor())
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "preprocessor")
|
||||
pipeline.connect("preprocessor", "writer")
|
||||
|
||||
result = pipeline.run(data={"sources": ["test.txt", "test.pdf"]})
|
||||
|
||||
print(result)
|
||||
# {'writer': {'documents_written': 3}}
|
||||
```
|
||||
@@ -0,0 +1,148 @@
|
||||
---
|
||||
title: "OpenAPIServiceToFunctions"
|
||||
id: openapiservicetofunctions
|
||||
slug: "/openapiservicetofunctions"
|
||||
description: "`OpenAPIServiceToFunctions` is a component that transforms OpenAPI service specifications into a format compatible with LLM tool calling."
|
||||
---
|
||||
|
||||
# OpenAPIServiceToFunctions
|
||||
|
||||
`OpenAPIServiceToFunctions` is a component that transforms OpenAPI service specifications into a format compatible with LLM tool calling.
|
||||
|
||||
:::tip[Consider using MCP instead]
|
||||
|
||||
These OpenAPI components are a legacy way to connect Haystack to external APIs. For most use cases, we recommend the [`MCPTool`](../../tools/mcptool.mdx) instead: it is the modern, standardized way to give your pipelines and agents access to external tools and services.
|
||||
|
||||
:::
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Flexible |
|
||||
| **Mandatory run variables** | `sources`: A list of OpenAPI specification sources, which can be file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `functions`: A list of JSON function definitions objects. For each path definition in OpenAPI specification, a corresponding function definition is generated. <br /> <br />`openapi_specs`: A list of JSON/YAML objects with references resolved. Such OpenAPI spec (with references resolved) can, in turn, be used as input to OpenAPIServiceConnector. |
|
||||
| **API reference** | [OpenAPI](/reference/integrations-openapi) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openapi |
|
||||
| **Package name** | `openapi-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`OpenAPIServiceToFunctions` transforms OpenAPI service specifications into a function calling format suitable for LLM tool calling. It takes an OpenAPI specification, processes it to extract function definitions, and formats these definitions to be compatible with LLM tool calling.
|
||||
|
||||
`OpenAPIServiceToFunctions` is valuable when used together with [`OpenAPIServiceConnector`](../connectors/openapiserviceconnector.mdx) component. It converts OpenAPI specifications into function definitions, allowing `OpenAPIServiceConnector` to handle input parameters for the OpenAPI specification and facilitate their use in REST API calls through `OpenAPIServiceConnector`.
|
||||
|
||||
To use `OpenAPIServiceToFunctions`, you need to install the `openapi-haystack` package with:
|
||||
|
||||
```shell
|
||||
pip install openapi-haystack
|
||||
```
|
||||
|
||||
`OpenAPIServiceToFunctions` component doesn’t have any init parameters.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
This component is primarily meant to be used in pipelines. Using this component alone is useful when you want to convert OpenAPI specification into function definitions and then perhaps save them in a file and subsequently use them for tool calling.
|
||||
|
||||
### In a pipeline
|
||||
|
||||
In a pipeline context, `OpenAPIServiceToFunctions` is most valuable when used alongside `OpenAPIServiceConnector`. For instance, let’s consider integrating [serper.dev](http://serper.dev/) search engine bridge into a pipeline. `OpenAPIServiceToFunctions` retrieves the OpenAPI specification of Serper from https://bit.ly/serper_dev_spec, converts this specification into function definitions that an LLM with tool calling capabilities can understand, and then seamlessly passes these definitions as `generation_kwargs` to the Chat Generator component.
|
||||
|
||||
:::info
|
||||
To run the following code snippet, note that you have to have your own Serper and OpenAI API keys.
|
||||
:::
|
||||
|
||||
```python
|
||||
import json
|
||||
import requests
|
||||
|
||||
from typing import Any
|
||||
|
||||
from haystack import Pipeline
|
||||
from haystack.components.converters import OutputAdapter
|
||||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||||
from haystack.dataclasses import ChatMessage
|
||||
from haystack.dataclasses.byte_stream import ByteStream
|
||||
from haystack_integrations.components.connectors.openapi import OpenAPIServiceConnector
|
||||
from haystack_integrations.components.converters.openapi import (
|
||||
OpenAPIServiceToFunctions,
|
||||
)
|
||||
|
||||
|
||||
def prepare_fc_params(openai_functions_schema: dict[str, Any]) -> dict[str, Any]:
|
||||
return {
|
||||
"tools": [{"type": "function", "function": openai_functions_schema}],
|
||||
"tool_choice": {
|
||||
"type": "function",
|
||||
"function": {"name": openai_functions_schema["name"]},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
serperdev_spec = requests.get("https://bit.ly/serper_dev_spec").json()
|
||||
system_prompt = requests.get("https://bit.ly/serper_dev_system").text
|
||||
user_prompt = "Why was Sam Altman ousted from OpenAI?"
|
||||
|
||||
pipe = Pipeline()
|
||||
pipe.add_component("spec_to_functions", OpenAPIServiceToFunctions())
|
||||
pipe.add_component(
|
||||
"prepare_fc_adapter",
|
||||
OutputAdapter(
|
||||
"{{functions[0] | prepare_fc}}",
|
||||
dict[str, Any],
|
||||
{"prepare_fc": prepare_fc_params},
|
||||
),
|
||||
)
|
||||
pipe.add_component("functions_llm", OpenAIChatGenerator())
|
||||
pipe.add_component("openapi_connector", OpenAPIServiceConnector())
|
||||
pipe.add_component(
|
||||
"message_adapter",
|
||||
OutputAdapter(
|
||||
"{{system_message + service_response}}",
|
||||
list[ChatMessage],
|
||||
unsafe=True,
|
||||
),
|
||||
)
|
||||
pipe.add_component("llm", OpenAIChatGenerator())
|
||||
|
||||
pipe.connect("spec_to_functions.functions", "prepare_fc_adapter.functions")
|
||||
pipe.connect(
|
||||
"spec_to_functions.openapi_specs",
|
||||
"openapi_connector.service_openapi_spec",
|
||||
)
|
||||
pipe.connect("prepare_fc_adapter", "functions_llm.generation_kwargs")
|
||||
pipe.connect("functions_llm.replies", "openapi_connector.messages")
|
||||
pipe.connect("openapi_connector.service_response", "message_adapter.service_response")
|
||||
pipe.connect("message_adapter", "llm.messages")
|
||||
|
||||
result = pipe.run(
|
||||
data={
|
||||
"functions_llm": {
|
||||
"messages": [
|
||||
ChatMessage.from_system("Only do tool/function calling"),
|
||||
ChatMessage.from_user(user_prompt),
|
||||
],
|
||||
},
|
||||
"openapi_connector": {
|
||||
"service_credentials": serper_dev_key,
|
||||
},
|
||||
"spec_to_functions": {
|
||||
"sources": [ByteStream.from_string(json.dumps(serperdev_spec))],
|
||||
},
|
||||
"message_adapter": {
|
||||
"system_message": [ChatMessage.from_system(system_prompt)],
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
print(result["llm"]["replies"][0].text)
|
||||
|
||||
# Sam Altman was ousted from OpenAI on November 17, 2023, following
|
||||
# a "deliberative review process" by the board of directors. The board concluded
|
||||
# that he was not "consistently candid in his communications". However, he
|
||||
# returned as CEO just days after his ouster.
|
||||
```
|
||||
@@ -0,0 +1,135 @@
|
||||
---
|
||||
title: "OutputAdapter"
|
||||
id: outputadapter
|
||||
slug: "/outputadapter"
|
||||
description: "This component helps the output of one component fit smoothly into the input of another. It uses Jinja expressions to define how this adaptation occurs."
|
||||
---
|
||||
|
||||
# OutputAdapter
|
||||
|
||||
This component helps the output of one component fit smoothly into the input of another. It uses Jinja expressions to define how this adaptation occurs.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Flexible |
|
||||
| **Mandatory init variables** | `template`: A Jinja template string that defines how to adapt the data <br /> <br />`output_type`: Type alias that this instance will return |
|
||||
| **Mandatory run variables** | `**kwargs`: Input variables to be used in Jinja expression. See [Variables](#variables) section for more details. |
|
||||
| **Output variables** | The output is specified under the `output` key dictionary |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/output_adapter.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
To use `OutputAdapter`, you need to specify the adaptation rule that includes:
|
||||
|
||||
- `template`: A Jinja template string that defines how to adapt the input data.
|
||||
- `output_type`: The type of the output data (such as `str`, `List[int]`..). This doesn't change the actual output type and is only needed to validate connection with other components.
|
||||
- `custom_filters`: An optional dictionary of custom Jinja filters to be used in the template.
|
||||
|
||||
### Variables
|
||||
|
||||
The `OutputAdapter` requires all template variables to be present before running and raises an error if any template variable is missing at pipeline connect time.
|
||||
|
||||
```python
|
||||
from haystack.components.converters import OutputAdapter
|
||||
|
||||
adapter = OutputAdapter(template="Hello {{name}}!", output_type=str)
|
||||
```
|
||||
|
||||
### Unsafe behavior
|
||||
|
||||
The `OutputAdapter` internally renders the `template` using Jinja, and by default, this is safe behavior. However, it limits the output types to strings, bytes, numbers, tuples, lists, dicts, sets, booleans, `None`, and `Ellipsis` (`...`), as well as any combination of these structures.
|
||||
|
||||
If you want to use other types such as `ChatMessage`, `Document`, or `Answer`, you must enable unsafe template rendering by setting the `unsafe` init argument to `True`.
|
||||
|
||||
Be cautious, as enabling this can be unsafe and may lead to remote code execution if the `template` is a string customizable by the end user.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
This component is primarily meant to be used in pipelines.
|
||||
|
||||
In this example, `OutputAdapter` simply outputs the content field of the first document in the arrays of documents:
|
||||
|
||||
```python
|
||||
from haystack import Document
|
||||
from haystack.components.converters import OutputAdapter
|
||||
|
||||
adapter = OutputAdapter(template="{{ documents[0].content }}", output_type=str)
|
||||
input_data = {"documents": [Document(content="Test content")]}
|
||||
expected_output = {"output": "Test content"}
|
||||
assert adapter.run(**input_data) == expected_output
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
The example below demonstrates a straightforward pipeline that uses the `OutputAdapter` to capitalize the first document in the list. If needed, you can also utilize the predefined Jinja [filters](https://jinja.palletsprojects.com/en/3.1.x/templates/#builtin-filters).
|
||||
|
||||
```python
|
||||
from haystack import Pipeline, component, Document
|
||||
from haystack.components.converters import OutputAdapter
|
||||
|
||||
|
||||
@component
|
||||
class DocumentProducer:
|
||||
@component.output_types(documents=dict)
|
||||
def run(self):
|
||||
return {"documents": [Document(content="haystack")]}
|
||||
|
||||
|
||||
pipe = Pipeline()
|
||||
pipe.add_component(
|
||||
name="output_adapter",
|
||||
instance=OutputAdapter(
|
||||
template="{{ documents[0].content | capitalize}}",
|
||||
output_type=str,
|
||||
),
|
||||
)
|
||||
pipe.add_component(name="document_producer", instance=DocumentProducer())
|
||||
pipe.connect("document_producer", "output_adapter")
|
||||
result = pipe.run(data={})
|
||||
|
||||
assert result["output_adapter"]["output"] == "Haystack"
|
||||
```
|
||||
|
||||
You can also define your own custom filters, which can then be added to an `OutputAdapter` instance through its init method and used in templates. Here’s an example of this approach:
|
||||
|
||||
```python
|
||||
|
||||
from haystack import Pipeline, component, Document
|
||||
from haystack.components.converters import OutputAdapter
|
||||
|
||||
|
||||
def reverse_string(s):
|
||||
return s[::-1]
|
||||
|
||||
|
||||
@component
|
||||
class DocumentProducer:
|
||||
@component.output_types(documents=dict)
|
||||
def run(self):
|
||||
return {"documents": [Document(content="haystack")]}
|
||||
|
||||
|
||||
pipe = Pipeline()
|
||||
pipe.add_component(
|
||||
name="output_adapter",
|
||||
instance=OutputAdapter(
|
||||
template="{{ documents[0].content | reverse_string}}",
|
||||
output_type=str,
|
||||
custom_filters={"reverse_string": reverse_string},
|
||||
),
|
||||
)
|
||||
|
||||
pipe.add_component(name="document_producer", instance=DocumentProducer())
|
||||
pipe.connect("document_producer", "output_adapter")
|
||||
result = pipe.run(data={})
|
||||
|
||||
assert result["output_adapter"]["output"] == "kcatsyah"
|
||||
```
|
||||
@@ -0,0 +1,157 @@
|
||||
---
|
||||
title: "PaddleOCRVLDocumentConverter"
|
||||
id: paddleocrvldocumentconverter
|
||||
slug: "/paddleocrvldocumentconverter"
|
||||
description: "`PaddleOCRVLDocumentConverter` extracts text from documents using PaddleOCR's large model document parsing API."
|
||||
---
|
||||
|
||||
# PaddleOCRVLDocumentConverter
|
||||
|
||||
`PaddleOCRVLDocumentConverter` extracts text from documents using PaddleOCR's large model document parsing API. PaddleOCR-VL is used behind the scenes. For more information, please refer to the [PaddleOCR-VL documentation](https://www.paddleocr.ai/latest/en/version3.x/algorithm/PaddleOCR-VL/PaddleOCR-VL.html).
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx), or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory init variables** | `api_url`: The URL of the PaddleOCR-VL API. <br /> <br /> `access_token`: The AI Studio access token. Can be set with `AISTUDIO_ACCESS_TOKEN` environment variable. |
|
||||
| **Mandatory run variables** | `sources`: A list of image or PDF file paths or ByteStream objects. |
|
||||
| **Output variables** | `documents`: A list of documents. <br /> <br />`raw_paddleocr_responses`: A list of raw OCR responses from PaddleOCR API. |
|
||||
| **API reference** | [PaddleOCR](/reference/integrations-paddleocr) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/paddleocr |
|
||||
| **Package name** | `paddleocr-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `PaddleOCRVLDocumentConverter` takes a list of document sources and uses PaddleOCR's large model document parsing API to extract text from images and PDFs. It supports both images and PDF files.
|
||||
|
||||
The component returns one Haystack [`Document`](../../concepts/data-classes.mdx#document) per source, with all pages concatenated using form feed characters (`\f`) as separators. This format ensures compatibility with Haystack's [`DocumentSplitter`](../preprocessors/documentsplitter.mdx) for accurate page-wise splitting and overlap handling. The content is returned in markdown format, with images represented as `` tags.
|
||||
|
||||
The component takes `api_url` as a required parameter. To obtain the API URL, visit the [PaddleOCR official website](https://aistudio.baidu.com/paddleocr), click the **API** button, choose the example code for PaddleOCR-VL, and copy the `API_URL`.
|
||||
|
||||
By default, the component uses the `AISTUDIO_ACCESS_TOKEN` environment variable for authentication. You can also pass an `access_token` at initialization. The AI Studio access token can be obtained from [this page](https://aistudio.baidu.com/account/accessToken).
|
||||
|
||||
`raw_paddleocr_responses` can be useful while tuning layout thresholds, prompt settings, or Markdown post-processing options because it gives you access to the original API output alongside the converted Haystack documents.
|
||||
|
||||
:::note
|
||||
This component returns Markdown content. Avoid piping it through `DocumentCleaner()` with its default settings because `remove_extra_whitespaces=True` and `remove_empty_lines=True` can collapse line breaks and flatten headings, tables, and image tags. For page-aware chunking, connect the converter directly to `DocumentSplitter`, or disable those options if you need custom cleanup.
|
||||
:::
|
||||
|
||||
## When to use it
|
||||
|
||||
`PaddleOCRVLDocumentConverter` is a strong fit when you need more than plain OCR text:
|
||||
|
||||
- **Scanned PDFs and camera-captured documents** where page orientation and warped text can reduce extraction quality.
|
||||
- **Layout-sensitive documents** such as invoices, reports, forms, and multi-column PDFs where preserving structure matters for downstream chunking and retrieval.
|
||||
- **Tables, formulas, charts, or seals** where you want more targeted extraction behavior than plain text OCR.
|
||||
- **RAG ingestion pipelines** where Markdown output is useful because headings, lists, tables, and page breaks can be preserved for later splitting.
|
||||
|
||||
## Useful configuration areas
|
||||
|
||||
The full parameter list is available in the [API reference](/reference/integrations-paddleocr). In practice, the most useful options tend to fall into these groups:
|
||||
|
||||
- **Input handling and image cleanup**: `file_type`, `use_doc_orientation_classify`, and `use_doc_unwarping` help when you mix PDFs and images or work with skewed scans and mobile photos.
|
||||
- **Layout-aware extraction**: `use_layout_detection`, `layout_threshold`, `layout_nms`, `layout_unclip_ratio`, `layout_merge_bboxes_mode`, `layout_shape_mode`, and `merge_layout_blocks` help you tune how regions are detected and merged before Markdown is generated.
|
||||
- **Content focus**: `prompt_label`, `use_ocr_for_image_block`, `use_chart_recognition`, and `use_seal_recognition` let you bias extraction toward a particular type of content, such as plain OCR, formulas, tables, charts, or seals.
|
||||
- **Markdown output shaping**: `format_block_content`, `markdown_ignore_labels`, `prettify_markdown`, `show_formula_number`, `restructure_pages`, `merge_tables`, and `relevel_titles` help you control how much cleanup and restructuring happens before the result becomes a Haystack document.
|
||||
- **VLM generation controls**: `repetition_penalty`, `temperature`, `top_p`, `min_pixels`, `max_pixels`, `max_new_tokens`, `vlm_extra_args`, and `additional_params` are useful when you need to trade off output quality, determinism, and cost.
|
||||
- **Debugging and inspection**: `visualize=True` and the returned `raw_paddleocr_responses` are helpful when you are tuning extraction quality for a new document type.
|
||||
|
||||
## Typical scenarios
|
||||
|
||||
These settings are especially useful in a few common workflows:
|
||||
|
||||
- **Scanned contracts or receipts from phones**: start with `use_doc_orientation_classify=True` and `use_doc_unwarping=True`.
|
||||
- **Table-heavy financial or operations PDFs**: consider `use_layout_detection=True`, `merge_tables=True`, and `restructure_pages=True`.
|
||||
- **Formula-heavy documents**: use `prompt_label="formula"` together with `show_formula_number=True` if formula numbering matters in the final Markdown.
|
||||
- **Mixed business documents with figures or seals**: enable `use_chart_recognition=True`, `use_seal_recognition=True`, or `use_ocr_for_image_block=True` depending on the content you want to preserve.
|
||||
|
||||
## Usage
|
||||
|
||||
You need to install the `paddleocr-haystack` integration to use `PaddleOCRVLDocumentConverter`:
|
||||
|
||||
```shell
|
||||
pip install paddleocr-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
Basic usage with a local file:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.paddleocr import (
|
||||
PaddleOCRVLDocumentConverter,
|
||||
)
|
||||
|
||||
converter = PaddleOCRVLDocumentConverter(
|
||||
api_url="<your-api-url>",
|
||||
access_token=Secret.from_env_var("AISTUDIO_ACCESS_TOKEN"),
|
||||
)
|
||||
|
||||
result = converter.run(sources=[Path("my_document.pdf")])
|
||||
documents = result["documents"]
|
||||
```
|
||||
|
||||
Advanced configuration for structure-heavy PDFs:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.paddleocr import (
|
||||
PaddleOCRVLDocumentConverter,
|
||||
)
|
||||
|
||||
converter = PaddleOCRVLDocumentConverter(
|
||||
api_url="<your-api-url>",
|
||||
access_token=Secret.from_env_var("AISTUDIO_ACCESS_TOKEN"),
|
||||
use_doc_orientation_classify=True,
|
||||
use_doc_unwarping=True,
|
||||
use_layout_detection=True,
|
||||
use_ocr_for_image_block=True,
|
||||
merge_tables=True,
|
||||
restructure_pages=True,
|
||||
prettify_markdown=True,
|
||||
)
|
||||
|
||||
result = converter.run(sources=[Path("quarterly_report.pdf")])
|
||||
documents = result["documents"]
|
||||
raw_responses = result["raw_paddleocr_responses"]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Here's an example of an indexing pipeline that processes PDFs with OCR and writes them to a Document Store:
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.utils import Secret
|
||||
from haystack_integrations.components.converters.paddleocr import (
|
||||
PaddleOCRVLDocumentConverter,
|
||||
)
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component(
|
||||
"converter",
|
||||
PaddleOCRVLDocumentConverter(
|
||||
api_url="<your-api-url>",
|
||||
access_token=Secret.from_env_var("AISTUDIO_ACCESS_TOKEN"),
|
||||
),
|
||||
)
|
||||
pipeline.add_component("splitter", DocumentSplitter(split_by="page", split_length=1))
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
|
||||
pipeline.connect("converter", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
file_paths = ["invoice.pdf", "receipt.jpg", "contract.pdf"]
|
||||
pipeline.run({"converter": {"sources": file_paths}})
|
||||
```
|
||||
@@ -0,0 +1,83 @@
|
||||
---
|
||||
title: "PDFMinerToDocument"
|
||||
id: pdfminertodocument
|
||||
slug: "/pdfminertodocument"
|
||||
description: "A component that converts complex PDF files to documents using pdfminer arguments."
|
||||
---
|
||||
|
||||
# PDFMinerToDocument
|
||||
|
||||
A component that converts complex PDF files to documents using pdfminer arguments.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: PDF file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/pdfminer.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `PDFMinerToDocument` component converts PDF files into documents using [PDFMiner](https://pdfminersix.readthedocs.io/en/latest/) extraction tool arguments.
|
||||
|
||||
You can use it in an indexing pipeline to index the contents of a PDF file in a Document Store. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream)objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
When initializing the component, you can adjust several parameters to fit your PDF. See the full parameter list and descriptions in our [API reference](/reference/converters-api#pdfminertodocument).
|
||||
|
||||
## Usage
|
||||
|
||||
First, install `pdfminer` package to start using this converter:
|
||||
|
||||
```shell
|
||||
pip install pdfminer.six
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import PDFMinerToDocument
|
||||
|
||||
converter = PDFMinerToDocument()
|
||||
results = converter.run(
|
||||
sources=["sample.pdf"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
|
||||
print(documents[0].content)
|
||||
|
||||
# 'This is a text from the PDF file.'
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import PDFMinerToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", PDFMinerToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,117 @@
|
||||
---
|
||||
title: "PDFToImageContent"
|
||||
id: pdftoimagecontent
|
||||
slug: "/pdftoimagecontent"
|
||||
description: "`PDFToImageContent` reads local PDF files and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image captioning, visual QA, or prompt-based generation."
|
||||
---
|
||||
|
||||
# PDFToImageContent
|
||||
|
||||
`PDFToImageContent` reads local PDF files and converts them into `ImageContent` objects. These are ready for multimodal AI pipelines, including tasks like image captioning, visual QA, or prompt-based generation.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before a `ChatPromptBuilder` in a query pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of PDF file paths or ByteStreams |
|
||||
| **Output variables** | `image_contents`: A list of ImageContent objects |
|
||||
| **API reference** | [Image Converters](/reference/image-converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/image/pdf_to_image.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`PDFToImageContent` processes a list of PDF sources and converts them into `ImageContent` objects, one for each page of the PDF. These can be used in multimodal pipelines that require base64-encoded image input.
|
||||
|
||||
Each source can be:
|
||||
|
||||
- A file path (string or `Path`), or
|
||||
- A `ByteStream` object.
|
||||
|
||||
Optionally, you can provide metadata using the `meta` parameter. This can be a single dictionary (applied to all images) or a list matching the length of `sources`.
|
||||
|
||||
Use the `size` parameter to resize images while preserving aspect ratio. This reduces memory usage and transmission size, which is helpful when working with remote models or limited-resource environments.
|
||||
|
||||
This component is often used in query pipelines just before a `ChatPromptBuilder`.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters.image import PDFToImageContent
|
||||
|
||||
converter = PDFToImageContent()
|
||||
|
||||
sources = ["file.pdf", "another_file.pdf"]
|
||||
|
||||
image_contents = converter.run(sources=sources)["image_contents"]
|
||||
print(image_contents)
|
||||
|
||||
# [ImageContent(base64_image='...',
|
||||
# mime_type='application/pdf',
|
||||
# detail=None,
|
||||
# meta={'file_path': 'file.pdf', 'page_number': 1}),
|
||||
# ...]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
Use `ImageFileToImageContent` to supply image data to a `ChatPromptBuilder` for multimodal QA or captioning with an LLM.
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.builders import ChatPromptBuilder
|
||||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||||
from haystack.components.converters.image import PDFToImageContent
|
||||
|
||||
# Query pipeline
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("image_converter", PDFToImageContent(detail="auto"))
|
||||
pipeline.add_component(
|
||||
"chat_prompt_builder",
|
||||
ChatPromptBuilder(
|
||||
required_variables=["question"],
|
||||
template="""{% message role="system" %}
|
||||
You are a helpful assistant that answers questions using the provided images.
|
||||
{% endmessage %}
|
||||
|
||||
{% message role="user" %}
|
||||
Question: {{ question }}
|
||||
|
||||
{% for img in image_contents %}
|
||||
{{ img | templatize_part }}
|
||||
{% endfor %}
|
||||
{% endmessage %}
|
||||
""",
|
||||
),
|
||||
)
|
||||
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
|
||||
|
||||
pipeline.connect("image_converter", "chat_prompt_builder.image_contents")
|
||||
pipeline.connect("chat_prompt_builder", "llm")
|
||||
|
||||
sources = ["flan_paper.pdf"]
|
||||
|
||||
result = pipeline.run(
|
||||
data={
|
||||
"image_converter": {"sources": ["flan_paper.pdf"], "page_range": "9"},
|
||||
"chat_prompt_builder": {"question": "What is the main takeaway of Figure 6?"},
|
||||
},
|
||||
)
|
||||
print(result["replies"][0].text)
|
||||
|
||||
# ('The main takeaway of Figure 6 is that Flan-PaLM demonstrates improved '
|
||||
# 'performance in zero-shot reasoning tasks when utilizing chain-of-thought '
|
||||
# '(CoT) reasoning, as indicated by higher accuracy across different model '
|
||||
# 'sizes compared to PaLM without finetuning. This highlights the importance of '
|
||||
# 'instruction finetuning combined with CoT for enhancing reasoning '
|
||||
# 'capabilities in models.')
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
🧑🍳 Cookbook: [Introduction to Multimodality](https://haystack.deepset.ai/cookbook/multimodal_intro)
|
||||
@@ -0,0 +1,79 @@
|
||||
---
|
||||
title: "PPTXToDocument"
|
||||
id: pptxtodocument
|
||||
slug: "/pptxtodocument"
|
||||
description: "Convert PPTX files to documents."
|
||||
---
|
||||
|
||||
# PPTXToDocument
|
||||
|
||||
Convert PPTX files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: PPTX file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/pptx.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `PPTXToDocument` component converts PPTX files into documents. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
## Usage
|
||||
|
||||
First, install the`python-pptx` package to start using this converter:
|
||||
|
||||
```shell
|
||||
pip install python-pptx
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import PPTXToDocument
|
||||
|
||||
converter = PPTXToDocument()
|
||||
results = converter.run(
|
||||
sources=["sample.pptx"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
|
||||
print(documents[0].content)
|
||||
|
||||
# 'This is the text from the PPTX file.'
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import PPTXToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", PPTXToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
@@ -0,0 +1,79 @@
|
||||
---
|
||||
title: "PyPDFToDocument"
|
||||
id: pypdftodocument
|
||||
slug: "/pypdftodocument"
|
||||
description: "A component that converts PDF files to Documents."
|
||||
---
|
||||
|
||||
# PyPDFToDocument
|
||||
|
||||
A component that converts PDF files to Documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: PDF file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/pypdf.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `PyPDFToDocument` component converts PDF files into documents. You can use it in an indexing pipeline to index the contents of a PDF file into a Document Store. It takes a list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
## Usage
|
||||
|
||||
You need to install `pypdf` package to use the `PyPDFToDocument` converter:
|
||||
|
||||
```shell
|
||||
pip install pypdf
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.components.converters import PyPDFToDocument
|
||||
|
||||
converter = PyPDFToDocument()
|
||||
|
||||
docs = converter.run(sources=[Path("my_file.pdf")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import PyPDFToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", PyPDFToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
🧑🍳 Cookbook: [PDF-Based Question Answering with Amazon Bedrock and Haystack](https://haystack.deepset.ai/cookbook/amazon_bedrock_for_documentation_qa)
|
||||
|
||||
📓 Tutorial: [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)
|
||||
@@ -0,0 +1,73 @@
|
||||
---
|
||||
title: "TextFileToDocument"
|
||||
id: textfiletodocument
|
||||
slug: "/textfiletodocument"
|
||||
description: "Converts text files to documents."
|
||||
---
|
||||
|
||||
# TextFileToDocument
|
||||
|
||||
Converts text files to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: A list of paths to text files you want to convert |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/txt.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `TextFileToDocument` component converts text files into documents. You can use it in an indexing pipeline to index the contents of text files into a Document Store. It takes a list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
When you initialize the component, you can optionally set the default encoding of the text files through the `encoding` parameter. If you don't provide any value, the component uses `"utf-8"` by default. Note that if the encoding is specified in the metadata of an input ByteStream, it will override this parameter's setting.
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from haystack.components.converters import TextFileToDocument
|
||||
|
||||
converter = TextFileToDocument()
|
||||
|
||||
docs = converter.run(sources=[Path("my_file.txt")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import TextFileToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", TextFileToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
|
||||
## Additional References
|
||||
|
||||
:notebook: Tutorial: [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)
|
||||
@@ -0,0 +1,80 @@
|
||||
---
|
||||
title: "TikaDocumentConverter"
|
||||
id: tikadocumentconverter
|
||||
slug: "/tikadocumentconverter"
|
||||
description: "An integration for converting files of different types (PDF, DOCX, HTML, and more) to documents."
|
||||
---
|
||||
|
||||
# TikaDocumentConverter
|
||||
|
||||
An integration for converting files of different types (PDF, DOCX, HTML, and more) to documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: File paths |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Tika](/reference/integrations-tika) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/tika |
|
||||
| **Package name** | `tika-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `TikaDocumentConverter` component converts files of different types (pdf, docx, html, and others) into documents. You can use it in an indexing pipeline to index the contents of files into a Document Store. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
This integration uses [Apache Tika](https://tika.apache.org/) to parse the files and requires a running Tika server.
|
||||
|
||||
The easiest way to run Tika is by using Docker: `docker run -d -p 127.0.0.1:9998:9998 apache/tika:latest`.
|
||||
For more options on running Tika on Docker, see the [Tika documentation](https://github.com/apache/tika-docker/blob/main/README.md#usage).
|
||||
|
||||
When you initialize the `TikaDocumentConverter` component, you can specify a custom URL of the Tika server you are using through the parameter `tika_url`. The default URL is `"http://localhost:9998/tika"`.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the `tika-haystack` package to use the `TikaDocumentConverter` component:
|
||||
|
||||
```shell
|
||||
pip install tika-haystack
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.tika import TikaDocumentConverter
|
||||
from pathlib import Path
|
||||
|
||||
converter = TikaDocumentConverter()
|
||||
|
||||
converter.run(sources=[Path("my_file.pdf")])
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.tika import TikaDocumentConverter
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", TikaDocumentConverter())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_paths}})
|
||||
```
|
||||
@@ -0,0 +1,128 @@
|
||||
---
|
||||
title: "TwelveLabsVideoConverter"
|
||||
id: twelvelabsvideoconverter
|
||||
slug: "/twelvelabsvideoconverter"
|
||||
description: "`TwelveLabsVideoConverter` converts videos to Haystack Documents using the TwelveLabs Pegasus video-language model. Pegasus analyzes each video on the fly — its visuals and its own audio (via ASR) — and returns text, so each video becomes a Document whose content is the analysis."
|
||||
---
|
||||
|
||||
# TwelveLabsVideoConverter
|
||||
|
||||
`TwelveLabsVideoConverter` converts videos to Haystack Documents using the TwelveLabs Pegasus video-language model. Pegasus analyzes each video on the fly — its visuals **and** its own audio (via ASR) — and returns text, so each source video becomes one Document whose content is Pegasus's analysis (for example, a description plus a transcript). There is no frame extraction or separate transcription step.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | At the beginning of an indexing pipeline, before [PreProcessors](../preprocessors.mdx) or an embedder |
|
||||
| **Mandatory init variables** | `api_key`: The TwelveLabs API key. Can be set with `TWELVELABS_API_KEY` env var. |
|
||||
| **Mandatory run variables** | `sources`: A list of video URLs or local file paths |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [TwelveLabs](/reference/integrations-twelvelabs) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/twelvelabs |
|
||||
| **Package name** | `twelvelabs-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `TwelveLabsVideoConverter` takes a list of video sources and produces one [`Document`](../../concepts/data-classes.mdx#document) per source, with the Document content set to Pegasus's text analysis. Sources may be publicly accessible direct video URLs or local file paths (uploaded to TwelveLabs, up to 200 MB). Sources that fail to process are skipped with a warning logged, so one bad source does not fail the whole batch.
|
||||
|
||||
Each produced Document carries metadata about the request, including `source`, `asset_id`, `analysis_id`, `model`, and `provider`. The default model is `pegasus1.5`.
|
||||
|
||||
You can steer the analysis with a custom `prompt` and tune `temperature` and `max_tokens`.
|
||||
|
||||
To start using this integration with Haystack, install the package with:
|
||||
|
||||
```shell
|
||||
pip install twelvelabs-haystack
|
||||
```
|
||||
|
||||
The component uses a `TWELVELABS_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`. To get an API key, head to [playground.twelvelabs.io](https://playground.twelvelabs.io).
|
||||
|
||||
## Usage
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.twelvelabs import (
|
||||
TwelveLabsVideoConverter,
|
||||
)
|
||||
|
||||
converter = TwelveLabsVideoConverter()
|
||||
result = converter.run(sources=["https://example.com/clip.mp4"])
|
||||
|
||||
document = result["documents"][0]
|
||||
print(document.content) # Pegasus's description + transcript of the video
|
||||
print(document.meta) # includes source, asset_id, analysis_id, model, provider
|
||||
```
|
||||
|
||||
:::info
|
||||
We recommend setting `TWELVELABS_API_KEY` as an environment variable instead of setting it as a parameter.
|
||||
:::
|
||||
|
||||
### With a custom prompt
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.twelvelabs import (
|
||||
TwelveLabsVideoConverter,
|
||||
)
|
||||
|
||||
converter = TwelveLabsVideoConverter(
|
||||
prompt="Summarize this video in three bullet points and list any products shown.",
|
||||
temperature=0.2,
|
||||
max_tokens=1024,
|
||||
)
|
||||
result = converter.run(sources=["https://example.com/clip.mp4"])
|
||||
print(result["documents"][0].content)
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
This indexing pipeline analyzes videos with Pegasus, embeds the resulting analysis with the [`TwelveLabsDocumentEmbedder`](../embedders/twelvelabsdocumentembedder.mdx), and writes the documents to a document store:
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack_integrations.components.converters.twelvelabs import (
|
||||
TwelveLabsVideoConverter,
|
||||
)
|
||||
from haystack_integrations.components.embedders.twelvelabs import (
|
||||
TwelveLabsDocumentEmbedder,
|
||||
)
|
||||
|
||||
document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
|
||||
|
||||
indexing_pipeline = Pipeline()
|
||||
indexing_pipeline.add_component("converter", TwelveLabsVideoConverter())
|
||||
indexing_pipeline.add_component("embedder", TwelveLabsDocumentEmbedder())
|
||||
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
indexing_pipeline.connect("converter", "embedder")
|
||||
indexing_pipeline.connect("embedder", "writer")
|
||||
|
||||
indexing_pipeline.run({"converter": {"sources": ["https://example.com/clip.mp4"]}})
|
||||
```
|
||||
|
||||
### Attaching metadata
|
||||
|
||||
Pass a single dictionary to apply metadata to all output Documents, or a list to set metadata per source:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.twelvelabs import (
|
||||
TwelveLabsVideoConverter,
|
||||
)
|
||||
|
||||
converter = TwelveLabsVideoConverter()
|
||||
|
||||
# Same metadata for all sources
|
||||
result = converter.run(
|
||||
sources=["https://example.com/a.mp4", "https://example.com/b.mp4"],
|
||||
meta={"campaign": "demo"},
|
||||
)
|
||||
|
||||
# Per-source metadata
|
||||
result = converter.run(
|
||||
sources=["https://example.com/a.mp4", "https://example.com/b.mp4"],
|
||||
meta=[{"title": "Clip A"}, {"title": "Clip B"}],
|
||||
)
|
||||
```
|
||||
@@ -0,0 +1,116 @@
|
||||
---
|
||||
title: "UnstructuredFileConverter"
|
||||
id: unstructuredfileconverter
|
||||
slug: "/unstructuredfileconverter"
|
||||
description: "Use this component to convert text files and directories to a document."
|
||||
---
|
||||
|
||||
# UnstructuredFileConverter
|
||||
|
||||
Use this component to convert text files and directories to a document.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `paths`: A union of lists of paths |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Unstructured](/reference/integrations-unstructured) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/unstructured |
|
||||
| **Package name** | `unstructured-fileconverter-haystack` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
`UnstructuredFileConverter` converts files and directories into documents using the Unstructured API.
|
||||
|
||||
[Unstructured](https://docs.unstructured.io/) provides a series of tools to do ETL for LLMs. The `UnstructuredFileConverter` calls the Unstructured API that extracts text and other information from a vast range of file [formats](https://docs.unstructured.io/api-reference/api-services/overview#supported-file-types).
|
||||
|
||||
This Converter supports different modes for creating documents from the elements returned by Unstructured:
|
||||
|
||||
- `"one-doc-per-file"`: One Haystack document per file. All elements are concatenated into one text field.
|
||||
- `"one-doc-per-page"`: One Haystack document per page. All elements on a page are concatenated into one text field.
|
||||
- `"one-doc-per-element"`: One Haystack document per element. Each element is converted to a Haystack document.
|
||||
|
||||
## Usage
|
||||
|
||||
Install the Unstructured integration to use `UnstructuredFileConverter`component:
|
||||
|
||||
```shell
|
||||
pip install unstructured-fileconverter-haystack
|
||||
```
|
||||
|
||||
There are free and paid versions of Unstructured API: **Free Unstructured API** and **Unstructured Serverless API**.
|
||||
|
||||
1. **Free Unstructured API**:
|
||||
- API URL: `https://api.unstructured.io/general/v0/general`
|
||||
- This version is free, but comes with certain limitations.
|
||||
|
||||
2. **Unstructured Serverless API**:
|
||||
- You'll find your unique API URL in your Unstructured account after signing up for the paid version.
|
||||
- This is a full-tier paid version of Unstructured.
|
||||
|
||||
For more details about the two tiers refer to Unstructured [FAQ](https://docs.unstructured.io/faq/faq).
|
||||
|
||||
> ❗️ The API keys for the free and paid versions are different and cannot be used interchangeably.
|
||||
|
||||
Regardless of the chosen tier, we recommend to set the Unstructured API key as an environment variable `UNSTRUCTURED_API_KEY`:
|
||||
|
||||
```shell
|
||||
export UNSTRUCTURED_API_KEY=your_api_key
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
import os
|
||||
from haystack_integrations.components.converters.unstructured import (
|
||||
UnstructuredFileConverter,
|
||||
)
|
||||
|
||||
converter = UnstructuredFileConverter()
|
||||
documents = converter.run(paths=["a/file/path.pdf", "a/directory/path"])["documents"]
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
import os
|
||||
from haystack import Pipeline
|
||||
from haystack.components.writers import DocumentWriter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack_integrations.components.converters.unstructured import (
|
||||
UnstructuredFileConverter,
|
||||
)
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
indexing = Pipeline()
|
||||
indexing.add_component("converter", UnstructuredFileConverter())
|
||||
indexing.add_component("writer", DocumentWriter(document_store))
|
||||
indexing.connect("converter", "writer")
|
||||
|
||||
indexing.run({"converter": {"paths": ["a/file/path.pdf", "a/directory/path"]}})
|
||||
```
|
||||
|
||||
### With Docker
|
||||
|
||||
To use `UnstructuredFileConverter` through Docker, first, set up an Unstructured Docker container:
|
||||
|
||||
```
|
||||
docker run -p 8000:8000 -d --rm --name unstructured-api quay.io/unstructured-io/unstructured-api:latest --port 8000 --host 0.0.0.0
|
||||
```
|
||||
|
||||
When initializing the component, specify the localhost URL:
|
||||
|
||||
```python
|
||||
from haystack_integrations.components.converters.unstructured import (
|
||||
UnstructuredFileConverter,
|
||||
)
|
||||
|
||||
converter = UnstructuredFileConverter(
|
||||
api_url="http://localhost:8000/general/v0/general",
|
||||
)
|
||||
```
|
||||
@@ -0,0 +1,80 @@
|
||||
---
|
||||
title: "XLSXToDocument"
|
||||
id: xlsxtodocument
|
||||
slug: "/xlsxtodocument"
|
||||
description: "Converts Excel files into documents."
|
||||
---
|
||||
|
||||
# XLSXToDocument
|
||||
|
||||
Converts Excel files into documents.
|
||||
|
||||
<div className="key-value-table">
|
||||
|
||||
| | |
|
||||
| --- | --- |
|
||||
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) or right at the beginning of an indexing pipeline |
|
||||
| **Mandatory run variables** | `sources`: File paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects |
|
||||
| **Output variables** | `documents`: A list of documents |
|
||||
| **API reference** | [Converters](/reference/converters-api) |
|
||||
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/xlsx.py |
|
||||
| **Package name** | `haystack-ai` |
|
||||
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
|
||||
The `XLSXToDocument` component converts XLSX files into Haystack Documents with a CSV (default) or Markdown format. It takes a list of file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.
|
||||
|
||||
To see the additional parameters that you can specify with the component initialization, check out the [API Reference](/reference/converters-api#xlsxtodocument).
|
||||
|
||||
## Usage
|
||||
|
||||
First, install the openpyxl and tabulate packages to start using this converter:
|
||||
|
||||
```shell
|
||||
pip install pandas openpyxl
|
||||
pip install tabulate
|
||||
```
|
||||
|
||||
### On its own
|
||||
|
||||
```python
|
||||
from haystack.components.converters import XLSXToDocument
|
||||
|
||||
converter = XLSXToDocument()
|
||||
results = converter.run(
|
||||
sources=["sample.xlsx"],
|
||||
meta={"date_added": datetime.now().isoformat()},
|
||||
)
|
||||
documents = results["documents"]
|
||||
print(documents[0].content)
|
||||
# ",A,B\n1,col_a,col_b\n2,1.5,test\n"
|
||||
```
|
||||
|
||||
### In a pipeline
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import XLSXToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner
|
||||
from haystack.components.preprocessors import DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
pipeline = Pipeline()
|
||||
pipeline.add_component("converter", XLSXToDocument())
|
||||
pipeline.add_component("cleaner", DocumentCleaner())
|
||||
pipeline.add_component(
|
||||
"splitter",
|
||||
DocumentSplitter(split_by="sentence", split_length=5),
|
||||
)
|
||||
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
|
||||
pipeline.connect("converter", "cleaner")
|
||||
pipeline.connect("cleaner", "splitter")
|
||||
pipeline.connect("splitter", "writer")
|
||||
|
||||
pipeline.run({"converter": {"sources": file_names}})
|
||||
```
|
||||
Reference in New Issue
Block a user