# Neum AI

## Docs

- [CharacterChunker](https://docs.neum.ai/components/chunkers/CharacterChunker.md): This class is responsible for chunking text data into smaller pieces based on character count, with optional overlapping between chunks.
- [CustomChunker](https://docs.neum.ai/components/chunkers/CustomChunker.md): CustomChunker is a flexible class designed to chunk text according to user-provided code, allowing for personalized text segmentation strategies.
- [RecursiveChunker](https://docs.neum.ai/components/chunkers/RecursiveChunker.md): The RecursiveChunker class is tailored for segmenting text into smaller chunks with a recursive approach, allowing for different levels of text granularity.
- [Azure Blob Connector](https://docs.neum.ai/components/data-connectors/AzureBlobConnector.md): Retrieve data from Azure Blob storage
- [File Connector](https://docs.neum.ai/components/data-connectors/FileConnector.md): Process any public file
- [Postgres Connector](https://docs.neum.ai/components/data-connectors/PostgresConnector.md): Retrieve data from PostgreSQL
- [S3 Connector](https://docs.neum.ai/components/data-connectors/S3Connector.md): Retrieve data from S3
- [Sharepoint Connector](https://docs.neum.ai/components/data-connectors/SharepointConnector.md): Retrieve data from a Sharepoint site
- [SingleStore Connector](https://docs.neum.ai/components/data-connectors/SingleStoreConnector.md): Retrieve data from a SingleStore table
- [Supabase Storage Connector](https://docs.neum.ai/components/data-connectors/SupabaseStorageConnector.md): Retrieve data from a Supabase storage
- [Website Connector](https://docs.neum.ai/components/data-connectors/WebsiteConnector.md): Scrape the contents of a website
- [AzureOpenAIEmbed](https://docs.neum.ai/components/embed-connectors/AzureOpenAIEmbed.md): The AzureOpenAIEmbed connector is designed to generate embeddings for text data using Azure OpenAI’s models, providing a bridge between Neum AI documents and OpenAI’s powerful embedding capabilities.
- [HuggingFaceEmbed](https://docs.neum.ai/components/embed-connectors/HuggingFaceEmbed.md): The HuggingFaceEmbed connector is designed to generate embeddings for text data using Hugging Face hosted emedding models.
- [OpenAIEmbed](https://docs.neum.ai/components/embed-connectors/OpenAIEmbed.md): The OpenAIEmbed connector is designed to generate embeddings for text data using OpenAI’s models, providing a bridge between Neum AI documents and OpenAI’s powerful embedding capabilities.
- [ReplicateEmbed](https://docs.neum.ai/components/embed-connectors/ReplicateEmbed.md): ReplicateEmbed provides an interface to generate embeddings using models from the Replicate platform, integrated within the Neum AI framework for document processing.
- [AutoLoader](https://docs.neum.ai/components/loaders/AutoLoader.md): AutoLoader is a utility class in the Neum AI framework designed to automatically select and use the appropriate loader based on the type of file it encounters.
- [CSVLoader](https://docs.neum.ai/components/loaders/CSVLoader.md): The CSVLoader is dedicated to parsing CSV files and converting them into NeumDocument objects, with support for custom encoding and selective data extraction.
- [HTMLLoader](https://docs.neum.ai/components/loaders/HTMLLoader.md): HTMLLoader is designed to efficiently load and parse HTML files into NeumDocument objects for further processing within the Neum Ai ecosystem.
- [JSONLoader](https://docs.neum.ai/components/loaders/JSONLoader.md): JSONLoader is designed to parse JSON files and convert them into NeumDocument objects, catering to complex data structures and nested JSON.
- [MarkdownLoader](https://docs.neum.ai/components/loaders/MarkdownLoader.md): MarkdownLoader specializes in converting markdown files into NeumDocument objects, suitable for integration with Neumai’s document processing pipeline.
- [PDFLoader](https://docs.neum.ai/components/loaders/PDFLoader.md): The PDFLoader class is designed to load and parse PDF files, transforming them into NeumDocument objects for further processing within the Neum AI platform.
- [Pipeline](https://docs.neum.ai/components/pipeline.md): Pipeline object
- [PipelineCollection](https://docs.neum.ai/components/pipelineCollection.md): PipelineCollection object
- [LanceDBSink](https://docs.neum.ai/components/sink-connectors/LanceDBSink.md): LanceDBSink enables seamless integration with LanceDB vector database, supporting vector storage and similarity search for advanced data retrieval.
- [MarqoSink](https://docs.neum.ai/components/sink-connectors/MarqoSink.md): MarqoSink enables seamless integration with Marqo vector database, supporting vector storage and similarity search for advanced data retrieval.
- [PineconeSink](https://docs.neum.ai/components/sink-connectors/PineconeSink.md): PineconeSink enables seamless integration with Pinecone’s vector database, supporting vector storage and similarity search for advanced data retrieval.
- [QdrantSink](https://docs.neum.ai/components/sink-connectors/QdrantSink.md): QdrantSink integrates with Qdrant vector search engine to manage vector data, enabling powerful search capabilities and efficient data storage.
- [SingleStoreSink](https://docs.neum.ai/components/sink-connectors/SingleStoreSink.md): SingleStoreSink enables the storage, retrieval, and semantic search of vectorized data in SingleStore databases, suitable for high-performance data operations.
- [SupabaseSink](https://docs.neum.ai/components/sink-connectors/SupabaseSink.md): SupabaseSink is designed to store and search vectorized data within Supabase databases, enabling efficient similarity searches and data management.
- [WeaviateSink](https://docs.neum.ai/components/sink-connectors/WeaviateSink.md): WeaviateSink facilitates the storage and retrieval of vectorized data in the Weaviate vector database, allowing for efficient semantic search capabilities.
- [Source Connector](https://docs.neum.ai/components/sourceConnector.md): Connector to extract data from data sources
- [Neum Document](https://docs.neum.ai/components/utilities/neum-document.md): Data interface for `Neum Document`
- [Neum Search Result](https://docs.neum.ai/components/utilities/neum-search.md): Data interface for `Neum Search`
- [Neum Vector](https://docs.neum.ai/components/utilities/neum-vector.md): Data interface for `Neum Vector`
- [Selector](https://docs.neum.ai/components/utilities/selector.md): Metadata selector
- [Connect to chatbot](https://docs.neum.ai/get-started/chatbot.md): Connect Neum AI pipelines to your chatbot
- [Local vs Cloud](https://docs.neum.ai/get-started/cloud-vs-local.md): Comparing local vs cloud capabilities for Neum AI
- [Deploy pipelines](https://docs.neum.ai/get-started/deploy.md): Deploy your pipelines to Neum AI Cloud
- [Introduction](https://docs.neum.ai/get-started/introduction.md): Welcome to our documentation site!
- [Quickstart](https://docs.neum.ai/get-started/quickstart.md): Create your first pipeline
- [Langchain](https://docs.neum.ai/integrations/langchain.md): Leverage Langchain components with Neum AI
- [LlamaIndex](https://docs.neum.ai/integrations/llamaindex.md): Leverage LlamaIndex components with Neum AI
- [Data Pre-processing](https://docs.neum.ai/local-development/data-preprocessing.md): Learn about how data is extracted and pre-processed.
- [Data Flow](https://docs.neum.ai/local-development/dataflow.md): Learn about how data is handled and trasnformed within the Neum AI pipeline
- [Evaluation with dataset](https://docs.neum.ai/local-development/evaluation.md): Evaluate the performance of your pipelines against a dataset.
- [Pipeline Architecture](https://docs.neum.ai/local-development/pipeline-architecture.md): Learn the high level architecture for Neum AI pipelines
- [Search](https://docs.neum.ai/local-development/search.md): Learn about how data is organized and used for search
- [Logs and Analytics](https://docs.neum.ai/managed-platform/logs-and-analytics.md): Learn how the Neum AI platform provides logs and analytics to see the performance of your pipelines
- [Neum AI Cloud](https://docs.neum.ai/managed-platform/neumai-cloud.md): Introduction to the Neum AI Cloud Platform
- [Pipeline Management](https://docs.neum.ai/managed-platform/pipeline-management.md): Learn about how Neum AI lets you manage your pipelines.
- [Data retrieval](https://docs.neum.ai/managed-platform/retrieval.md): Support for data retrieval and feedback tracking
- [Cancel pipeline run](https://docs.neum.ai/platform-apis/endpoint/cancel-a-pipeline-run.md): Cancel a pipeline run
- [Create a pipeline](https://docs.neum.ai/platform-apis/endpoint/create-a-pipeline.md): Create a pipeline
- [Delete a pipeline](https://docs.neum.ai/platform-apis/endpoint/delete-a-pipeline.md): Delete a pipeline
- [Get pipeline configuration](https://docs.neum.ai/platform-apis/endpoint/get-pipeline.md): Get the information for a pipeline
- [Get pipeline retrievals](https://docs.neum.ai/platform-apis/endpoint/get-pipeline-retrievals.md): Get retrievals for a pipeline
- [Get pipeline run](https://docs.neum.ai/platform-apis/endpoint/get-pipeline-run.md): Get the information for a pipeline run
- [Get pipelines runs](https://docs.neum.ai/platform-apis/endpoint/get-pipeline-runs.md): Get the information for a all pipeline runs for a given pipeline
- [Get pipeline vector information](https://docs.neum.ai/platform-apis/endpoint/get-pipeline-vector-info.md): Get the information on the data stored in the sink of a pipeline
- [Get pipelines for a user](https://docs.neum.ai/platform-apis/endpoint/get-pipelines.md): Get the information for all pipelines for a user
- [Provide retrieval feedback](https://docs.neum.ai/platform-apis/endpoint/post-feedback-retrieval.md): For a given retrieval provide feedback on it.
- [Query pipeline](https://docs.neum.ai/platform-apis/endpoint/query-a-pipeline.md): Query the vector database attached to a pipeline
- [Trigger a pipeline](https://docs.neum.ai/platform-apis/endpoint/trigger-a-pipeline.md): Trigger a pipeline to run
- [Update a pipeline](https://docs.neum.ai/platform-apis/endpoint/update-a-pipeline.md)
- [Introduction](https://docs.neum.ai/platform-apis/introduction.md): APIs for the Neum AI Cloud platform
- [Neum Client](https://docs.neum.ai/platform-apis/neum-client.md): Interact with the Neum Cloud

## OpenAPI Specs

- [openapi](https://docs.neum.ai/openapi)

## Optional

- [Community](https://discord.gg/mJeNZYRz4m)
- [Blog](https://neum.ai/blog)