# Unstructured

## Docs

- [Overview](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/accessing-unstructured-api.md)
- [API Parameters](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/api-parameters.md): The Unstructured API provides parameters to customize the processing of documents. Below are the details for these parameters.
- [API Validation Errors](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/api-validation-errors.md): This section details the structure of HTTP validation errors returned by the API.
- [Unstructured API on AWS](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/aws.md): his guide provides step-by-step instructions for deploying Unstructured API from AWS Marketplace.
- [Unstructured API on Azure](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/azure.md)
- [Document elements and metadata](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/document-elements.md)
- [Examples](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/examples.md): This page provides some examples of accessing Unstructured API via different methods
- [Free Unstructured API](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/free-api.md): This page describes how to obtain an API key to use with the free Unstructured API, the limitations of the free Unstructured API, and provides a quick start example.
- [Glossary](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/glossary.md)
- [Using JavaScript SDK](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/javascript-sdk.md): This documentation covers the usage of the JavaScript SDK for interacting with the Unstructured API.
- [Unstructured API Services](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/overview.md)
- [Using API from partition_via_api()](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/partition-via-api.md)
- [Partitioning strategies](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/partitioning.md)
- [Sending POST requests to Unstructured API](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/post-requests.md): This page provides some examples of accessing Unstructured API via direct calls.
- [Using Python SDK](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/python-sdk.md): This documentation covers the usage of the Python SDK for interacting with the Unstructured API.
- [SaaS Unstructured API](https://unstructured-53-kapa-ai.mintlify.app/api-reference/api-services/saas-api-development-guide.md): This page describes how to get started with the SaaS Unstructured API. Learn how to obtain an API key to use with the SaaS Unstructured API, and get started in no time.
- [Pipeline](https://unstructured-53-kapa-ai.mintlify.app/api-reference/general/pipeline-1.md)
- [Models](https://unstructured-53-kapa-ai.mintlify.app/open-source/best-practices/models.md): Depending on your need, `Unstructured` provides OCR-based and Transformer-based models to detect elements in the documents. The models are useful to detect the complex layout in the documents and predict the element types.
- [Best Practices](https://unstructured-53-kapa-ai.mintlify.app/open-source/best-practices/overview.md): Unstructured offers a few strategies and models to extract document information. These best practices are intended to provide guidelines to configure the `strategy` and `model` configurations to optimize document information extraction.
- [Table Extraction from PDF](https://unstructured-53-kapa-ai.mintlify.app/open-source/best-practices/table-extraction-from-pdf.md): This section describes two methods for extracting tables from PDF files.
- [Document elements and metadata](https://unstructured-53-kapa-ai.mintlify.app/open-source/concepts/document-elements.md)
- [Glossary](https://unstructured-53-kapa-ai.mintlify.app/open-source/concepts/glossary.md)
- [Partitioning strategies](https://unstructured-53-kapa-ai.mintlify.app/open-source/concepts/partitioning-strategies.md)
- [Chunking](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/chunking.md): Chunking functions in `unstructured` use metadata and document elements detected with `partition` functions to split a document into smaller parts for uses cases such as Retrieval Augmented Generation (RAG).
- [Cleaning](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/cleaning.md): As part of data preparation for an NLP model, it’s common to need to clean up your data prior to passing it into the model. If there’s unwanted content in your output, for example, it could impact the quality of your NLP model. To help with this, the `unstructured` library includes cleaning function…
- [Embedding](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/embedding.md): Embedding encoder classes in `unstructured` use document elements detected with `partition` or document elements grouped with `chunking` to obtain embeddings for each element, for uses cases such as Retrieval Augmented Generation (RAG).
- [Extracting](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/extracting.md)
- [Core Functionality](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/overview.md): The `unstructured` library includes functions to partition, chunk, clean, and stage raw source documents. These functions serve as the primary public interfaces within the library.
- [Partitioning](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/partitioning.md): Partitioning functions in `unstructured` allow users to extract structured content from a raw unstructured document. These functions break a document down into elements such as `Title`, `NarrativeText`, and `ListItem`, enabling users to decide what content they’d like to keep for their particular ap…
- [Staging](https://unstructured-53-kapa-ai.mintlify.app/open-source/core-functionality/staging.md)
- [Multi-files API Processing](https://unstructured-53-kapa-ai.mintlify.app/open-source/examples/multi-files-api-processing.md)
- [Examples](https://unstructured-53-kapa-ai.mintlify.app/open-source/examples/overview.md): The following are some examples of how to use the library to parse documents. You can find example documents in the example-docs, along with instructions on how to download additional documents that are too large to store in the repo.
- [Delta Table Source Connector](https://unstructured-53-kapa-ai.mintlify.app/open-source/examples/table-source-connector.md)
- [Data Processing into Vector Database](https://unstructured-53-kapa-ai.mintlify.app/open-source/examples/vector-database.md)
- [Astra](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/astra.md): Batch process all your records using `unstructured-ingest` to store structured outputs and embeddings locally on your filesystem and upload those to a Astra DB index.
- [Azure](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/azure.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an Azure bucket.
- [Azure Cognitive Search](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/azure-cognitive-search.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an Azure Cognitive Search index.
- [Box](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/box.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Box folder.
- [Chroma](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/chroma.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those to a Chroma database.
- [Clarifai](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/clarifai.md): Batch process all your records using `unstructured-ingest` to store unstructured outputs locally on your filesystem and upload those to Clarifai apps.
- [Databricks Volumes](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/databricks-volumes.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Databricks Volume.
- [Delta Table](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/delta-table.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Delta Table.
- [Dropbox](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/dropbox.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Dropbox bucket.
- [Elasticsearch](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/elasticsearch.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an Elasticsearch index.
- [Google Cloud Service](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/google-cloud-service.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Google Cloud Service bucket.
- [MongoDB](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/mongodb.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an MongoDB collection.
- [Opensearch](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/opensearch.md)
- [Destination Connectors](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/overview.md): Connect to your favorite data storage platforms for effortless batch processing of your files. We are constantly adding new data connectors and if you don’t see your favorite platform let us know in our community Slack.
- [Pinecone](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/pinecone.md): Batch process all your records using `unstructured-ingest` to store structured outputs and embeddings locally on your filesystem and upload those to a Pinecone index.
- [Qdrant](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/qdrant.md): Batch process all your records using `unstructured-ingest` to store structured outputs and embeddings locally on your filesystem and upload those to a Qdrant collection.
- [S3](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/s3.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an S3 bucket.
- [SQL](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/sql.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a PostgreSQL or SQLite schema.
- [Vectara](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/vectara.md): Process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those to a Vectara corpus. If you don’t yet have a Vectara account, \[sign up\]([https://vectara.com/integrations/unstructured/](https://vectara.com/integrations/unstructured/)) for…
- [Weaviate](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/destination-connectors/weaviate.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to a Weaviate collection.
- [Chunking Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/chunking-configuration.md): A common chunking configuration is a critical element in the data processing pipeline, particularly when creating embeddings and populating vector databases with the results. This configuration defines the parameters governing the segmentation of text into meaningful chunks, whether at the document,…
- [Embedding Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/embedding-configuration.md): A common embedding configuration is a critical component that allows for dynamic selection of embedders and their associated parameters to create vectors from data. This configuration provides the flexibility to choose from various embedding models and fine-tune parameters to optimize the quality an…
- [Fsspec Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/fsspec-configuration.md): A common fsspec configuration is a shared set of parameters and settings utilized by connectors responsible for managing cloud-based file-system content. These configurations enable connectors to interact with cloud storage systems consistently, specifying details such as authentication credentials,…
- [Ingest Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/overview.md): A comprehensive list of common configurations plays a pivotal role in structuring the data required to execute an ingest process effectively. These configurations encompass a range of parameters and settings that guide how data is collected, transformed, and stored. Common configurations may include…
- [Partition Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/partition-configuration.md): A standard partition configuration is a collection of parameters designed to oversee document partitioning, whether executed through API integration or by the unstructured library on a local system. These parameters serve a dual role, encompassing those passed to the partition method for the initial…
- [Permissions Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/permissions-configuration.md): A common permissions configuration is leveraged to extract user access data associated with the content from the source data provider. Currently supported for the Sharepoint connector.
- [Processor Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/processor-configuration.md): A common process configuration plays a pivotal role in overseeing the entire ingest process, encompassing various aspects to ensure efficiency and reliability. This configuration includes parameters for managing a pool of workers, which allows for parallelization of the ingest process to maximize th…
- [Read Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/read-configuration.md): A shared read configuration serves as a universal set of parameters that are consistent across all source connectors, providing a standardized way to access and retrieve documents from various sources. This configuration typically includes settings such as the download directory, which specifies the…
- [Retry Strategy Configuration](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/ingest-configuration/retry-strategy-configuration.md): A common retry strategy configuration is a critical element in enhancing the robustness and resiliency of a system, especially when dealing with temporary network issues. This configuration typically includes parameters that define how the system should respond to network-related errors, such as con…
- [Ingest](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/overview.md): The Ingest Library is a powerful tool designed to coordinate the process of pulling data from data providers, partitioning the content, and pushing that new content to a desired location. This technical documentation will provide an in-depth understanding of the Ingest Library, including its feature…
- [Airtable](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/airtable.md): Connect Airtable to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Azure](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/azure.md): Connect Azure to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Biomed](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/biomed.md): Connect Biomed to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Box](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/box.md): Connect Box to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Confluence](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/confluence.md): Connect Confluence to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Delta Table](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/delta-table.md): Connect delta tables to your preprocessing pipeline, and batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Discord](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/discord.md): Connect Discord to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Dropbox](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/dropbox.md): Connect Dropbox to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Elasticsearch](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/elastic-search.md): Connect Elasticsearch to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Github](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/github.md): Connect Github to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Gitlab](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/gitlab.md): Connect Gitlab to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Google Cloud Storage](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/google-cloud-storage.md): Connect Google Cloud Storage to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Google Drive](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/google-drive.md): Connect Google Drive to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Jira](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/jira.md): Connect Jira to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Local](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/local.md): Connect local files to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [MongoDB](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/mongodb.md): Batch process all your records using `unstructured-ingest` to store structured outputs locally on your filesystem and upload those local files to an MongoDB collection.
- [Notion](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/notion.md): Connect Airtable to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [One Drive](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/one-drive.md): Connect One Drive to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [OpenSearch](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/opensearch.md): Connect OpenSearch to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Outlook](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/outlook.md): Connect Outlook to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Source Connectors](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/overview.md): Connect to your favorite data storage platforms for effortless batch processing of your files. We are constantly adding new data connectors and if you don’t see your favorite platform let us know in our community Slack.
- [Reddit](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/reddit.md): Connect Reddit to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [S3](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/s3.md): Connect S3 to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Salesforce](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/salesforce.md): Connect Salesforce to your preprocessing pipeline, and batch process Salesforce data using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Sftp](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/sftp.md): Connect Sftp to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Sharepoint](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/sharepoint.md): Connect Sharepoint to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Slack](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/slack.md): Connect Slack to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Wikipedia](https://unstructured-53-kapa-ai.mintlify.app/open-source/ingest/source-connectors/wikipedia.md): Connect Wikipedia to your preprocessing pipeline, and batch process all your documents using `unstructured-ingest` to store structured outputs locally on your filesystem.
- [Docker Installation](https://unstructured-53-kapa-ai.mintlify.app/open-source/installation/docker-installation.md): The instructions below guide you on how to use the unstructured library inside a Docker container.
- [Full Installation](https://unstructured-53-kapa-ai.mintlify.app/open-source/installation/full-installation.md)
- [Overview](https://unstructured-53-kapa-ai.mintlify.app/open-source/installation/overview.md): Unstructured open source library offers flexible options for installation.
- [Integrations](https://unstructured-53-kapa-ai.mintlify.app/open-source/integrations.md): Integrate your model development pipeline with your favorite machine learning frameworks and libraries, and prepare your data for ingestion into downstream systems. Most of our integrations come in the form of [staging functions](/open-source/core-functionality/staging), which take a list of `Elemen…
- [Unstructured Open Source Library](https://unstructured-53-kapa-ai.mintlify.app/open-source/introduction/overview.md)
- [Quick Start](https://unstructured-53-kapa-ai.mintlify.app/open-source/introduction/quick-start.md): This guide offers concise steps to swiftly install and validate your `unstructured` installation. For more comprehensive installation guide, please refer to [this page](/open-source/installation/overview).
- [Jobs Scheduling](https://unstructured-53-kapa-ai.mintlify.app/platform/jobs-scheduling.md)
- [Unstructured Platform](https://unstructured-53-kapa-ai.mintlify.app/platform/overview.md)
- [Azure Cognitive Search](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/azure-cognitive-search.md): This page contains the information to store processed data to Azure Cognitive Search.
- [Chroma](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/chroma.md): This page contains the information to store processed data to a Chroma instance.
- [Databricks](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/databricks.md): This page contains the information to store processed data to Databricks.
- [Elasticsearch](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/elasticsearch.md): This page contains the information to store processed data to an Elasticsearch cluster.
- [Google Cloud Storage](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/google-cloud-storage.md): This page contains the information to store processed data to Google Cloud Storage.
- [MongoDB](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/mongodb.md): This page contains the information to store processed data to a MongoDB database.
- [OpenSearch](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/opensearch.md): This page contains the information to store processed data to an OpenSearch cluster.
- [Platform Destination Connectors](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/overview.md): Destination Connectors in the `Unstructured Platform` are designed to specify the endpoint for data processed within the platform. These connectors ensure that the transformed and analyzed data is securely and efficiently transferred to a storage system for future use, often to a vector database for…
- [Pinecone](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/pinecone.md): This page contains the information to store processed data to Pinecone vector database.
- [PostgreSQL](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/postgresql.md): This page contains the information to store processed data to a PostgreSQL database.
- [Amazon S3](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/s3.md): his page contains the information to store processed data to Amazon S3.
- [Weaviate](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-destination-connectors/weaviate.md): This page contains the information to store processed data to Weaviate.
- [Azure Blob Storage](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/azure-blob-storage.md): This page contains the information to ingest your documents from Azure Blob Storage.
- [Elasticsearch](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/elasticsearch.md): This page contains the information to ingest your data from Elasticsearch.
- [Google Cloud Storage](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/google-cloud.md): This page contains the information to ingest your data from Google Cloud Storage.
- [Google Drive](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/google-drive.md): This page contains the information to ingest your data from Google Drive.
- [OneDrive Cloud Storage](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/onedrive-cloud-storage.md): This page contains the information to ingest your data from OneDrive.
- [OpenSearch](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/opensearch.md): This page contains the information to ingest your data from OpenSearch.
- [Platform Destination Connectors](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/overview.md): Source connectors are essential components in data integration systems that establish a link between your files and the data ingestion process. They facilitate the batch processing of files, allowing for the systematic retrieval and ingestion of data stored in various file formats.
- [Amazon S3](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/s3.md): This page contains the information to ingest your documents from Amazon S3 buckets.
- [Salesforce](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/salesforce.md): This page contains the information to ingest your data from Salesforce.
- [SFTP Storage](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/sftp-storage.md): This page contains the information to ingest your data from an SFTP server.
- [Sharepoint](https://unstructured-53-kapa-ai.mintlify.app/platform/platform-source-connectors/sharepoint.md): This page contains the information to ingest your documents from Sharepoint sites.
- [Workflows Automation](https://unstructured-53-kapa-ai.mintlify.app/platform/workflows-automation.md)
- [Unstructured](https://unstructured-53-kapa-ai.mintlify.app/welcome.md): `Unstructured` offers tools designed to help preprocess unstructured documents for use in downstream machine learning tasks. This documentation covers three product lines: Unstructured API, Unstructured Enterprise Platform, and the Unstructured Open Source Library.

## OpenAPI Specs

- [openapi](https://unstructured-53-kapa-ai.mintlify.app/openapi.json)

## Optional

- [Blog](https://unstructured.io/blog)
- [Community](https://unstructuredw-kbe4326.slack.com/signup#/domain-signup)
- [Product](https://unstructured.io/product)