openai/chatgpt-retrieval-plugin

Features

• ChatGPT Plugin Development - Provides a complete pipeline for building retrieval-based ChatGPT plugins that query private or professional documents.
• Semantic Search - Implements a search engine that uses vector embeddings to retrieve content based on conceptual meaning.
• Document Indexing - Processes documents into smaller chunks and stores them in a database to support retrieval-augmented generation.
• Document Chunking Strategies - Segments large files into smaller text blocks to optimize retrieval accuracy and fit LLM context windows.
• RAG Pipelines - Implements a full retrieval-augmented generation workflow, from document chunking and indexing to context retrieval for queries.
• Personal Data Retrieval - Searches through user files and emails to find answers and retrieve relevant data using natural language.
• Retrieval Re-ranking - Applies a secondary scoring pass to initial search results to increase the quality of retrieved snippets.
• Semantic Search - Retrieves relevant document snippets from a vector database using natural language queries to provide model context.
• Vector Upsert Operations - Uploads text and files as embedded chunks into a vector database to maintain a current knowledge base.
• Vector Database Integrations - Connects applications to external vector stores and document databases to enable similarity search and contextual data retrieval.
• Vector Document Indexing - Implements automated workflows for updating and inserting document embeddings into vector databases to maintain an up-to-date knowledge base.
• Hybrid Search - Combines dense vector embeddings with sparse keyword matching to increase the precision of document search results.
• Hybrid Vector-Keyword Indexing - Combines dense vector embeddings with sparse keyword matching to increase the precision of search results.
• Multi-Format Document Ingestion - Populates vector databases by ingesting and normalizing large collections of JSON, JSONL, or ZIP files.
• Vector Similarity Search - Queries stored document embeddings to find relevant information based on the semantic proximity of a request.
• Multi-Format Document Parsing - Extracts searchable text from PDF, DOCX, and PPTX files to make content accessible to language models.
• Private Data Privacy Tools - Protects user data through PII screening of documents and secures private store access using OAuth authentication.
• Data Indexing - Creates searchable in-memory structures including trees and keyword tables to facilitate efficient semantic retrieval for language models.
• Archive-Based Indexing - Indexes documents stored within compressed ZIP archives into a vector database with PII screening.
• Document and Unstructured Extraction - Parses key information like authors and dates from unstructured text using a model to return structured JSON.
• Vector Databases - Deploys containerized search engines designed to index and retrieve high-dimensional embeddings for natural language queries.
• PostgreSQL Vector Stores - Utilizes PostgreSQL with the pgvector extension to persist and manage document embeddings for retrieval.
• Document Ingestion Pipelines - Processes JSON document dumps to store content and associated metadata in a vector database.
• Metadata Filtering - Limits returned document chunks by refining search queries using metadata criteria such as source, date, or author.
• OpenAPI-to-Tool Converters - Defines a machine-readable schema allowing external language models to discover and call retrieval endpoints.
• Vector Search Indexes - Implements indexes on embedding columns to accelerate nearest neighbor lookups and improve query performance.
• Search Result Filtering - Refines vector search results using structured attributes like date or source to limit the returned data set.
• Vector Storage - Indexes high-dimensional vectors in cloud-native databases to support fast similarity searches across large datasets.
• Cloud Deployment - Provides Docker-based packaging and environment variable configuration for hosting the application on cloud infrastructure.
• Containerized Application Deployments - Packages the retrieval service and its dependencies into portable container images for consistent cloud deployment.
• Containerized Deployments - Packages the retrieval service and vector database into Docker images for consistent cloud hosting.
• Plugin Manifests - Hosts plugins on remote platforms and connects them to chat interfaces using OpenAPI schemas.
• OAuth Authentication - Secures private document stores by verifying user identities and managing access scopes via OAuth 2.0.
• PII Detection and Screening - Scans text and documents for personally identifiable information using a language model to prevent sensitive data storage.
• Document Q&A - Plugin for semantic search and retrieval over personal documents.
• Infrastructure and Utilities - Enables semantic search over personal or organizational data.
• Natural Language Processing - Listed in the “Natural Language Processing” section of the FunNLP awesome list.
• Data and Integration Plugins - Enables AI to search and retrieve information from personal documents.

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