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Comprehensive Integration von Vektordatenbanken Tools for Every Need

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Integration von Vektordatenbanken

  • NeuralGPT

    Modular Python framework to build AI Agents with LLMs, RAG, memory, tool integration, and vector database support.
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    What is NeuralGPT?
    NeuralGPT is designed to simplify AI Agent development by offering modular components and standardized pipelines. At its core, it features customizable Agent classes, retrieval-augmented generation (RAG), and memory layers to maintain conversational context. Developers can integrate vector databases (e.g., Chroma, Pinecone, Qdrant) for semantic search and define tool agents to execute external commands or API calls. The framework supports multiple LLM backends such as OpenAI, Hugging Face, and Azure OpenAI. NeuralGPT includes a CLI for quick prototyping and a Python SDK for programmatic control. With built-in logging, error handling, and extensible plugin architecture, it accelerates deployment of intelligent assistants, chatbots, and automated workflows.
    NeuralGPT Core Features
    • Customizable Agent classes
    • Retrieval-augmented generation (RAG)
    • Conversational memory management
    • Vector DB integrations (Chroma, Pinecone, Qdrant)
    • Tool agent execution for external APIs/commands
    • Multi-backend LLM support (OpenAI, Hugging Face, Azure)
    • CLI and Python SDK
    • Plugin architecture with logging and error handling

  • Controllable RAG Agent

    An open-source Python framework to build Retrieval-Augmented Generation agents with customizable control over retrieval and response generation.
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    What is Controllable RAG Agent?
    The Controllable RAG Agent framework provides a modular approach to building Retrieval-Augmented Generation systems. It allows you to configure and chain retrieval components, memory modules, and generation strategies. Developers can plug in different LLMs, vector databases, and policy controllers to adjust how documents are fetched and processed before generation. Built on Python, it includes utilities for indexing, querying, conversation history tracking, and action-based control flows, making it ideal for chatbots, knowledge assistants, and research tools.

  • ragChatbot

    An open-source RAG chatbot framework using vector databases and LLMs to provide contextualized question-answering over custom documents.
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    What is ragChatbot?
    ragChatbot is a developer-centric framework designed to streamline the creation of Retrieval-Augmented Generation chatbots. It integrates LangChain pipelines with OpenAI or other LLM APIs to process queries against custom document corpora. Users can upload files in various formats (PDF, DOCX, TXT), automatically extract text, and compute embeddings using popular models. The framework supports multiple vector stores such as FAISS, Chroma, and Pinecone for efficient similarity search. It features a conversational memory layer for multi-turn interactions and a modular architecture for customizing prompt templates and retrieval strategies. With a simple CLI or web interface, you can ingest data, configure search parameters, and launch a chat server to answer user questions with contextual relevance and accuracy.
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