The AI Toolkit with LangChain FlowRunner Extension empowers users to build sophisticated, AI-driven automation workflows by integrating advanced language models, semantic search, and database connectivity. By combining intelligent AI responses with tool integrations, this extension unlocks new levels of workflow automation and data insight. Key use cases include content generation, AI-assisted chatbots, document processing, real-time research, database querying using natural language, semantic search across knowledge bases, and automated multi-step workflows that require tool chaining and structured AI output.
Applicable Use Cases:
- Automated content generation and copywriting with AI assistance for marketing, blogs, and reports.
- Deploy customer service chatbots capable of intelligent, contextual conversations.
- Extract insights and structured data from unstructured text documents.
- Integrate real-time web research for fact-checking and up-to-date information retrieval.
- Query databases with natural language for quick access to structured records.
- Conduct semantic document search and manage large, AI-powered knowledge bases.
- Build advanced, multi-step AI workflows combining multiple tools and actions.
- Automate structured data processing and form filling using AI models.
Available Actions:
- AI with Tool(s): Invoke one or more tools in conjunction with an AI language model, allowing for complex, multi-tool workflows and chaining outputs across steps for advanced AI-driven automation.
- Backendless Database Tool: Interact with Backendless databases through queries, updates, and data manipulation, integrated directly with AI workflows for intelligent data retrieval or actions.
- Get AI Response: Submit a prompt to a supported AI language model and receive a generated, conversational text response for tasks like content creation, chatbots, and more.
- Get Structured AI Response: Obtain an AI-generated response in a structured format (e.g., JSON), ideal for extracting entities, structured data, or filling forms from text.
- Open Vector Store: Initialize and connect to a vector store for managing and searching embedded representations of documents or data, enabling semantic search and similarity matching in workflows.
- Search in Vector Store: Perform semantic search queries within a connected vector store to retrieve relevant documents, passages, or data points based on similarity to a query.
- Tavily Search Tool: Integrate real-time web search capabilities into your workflows using Tavily, enabling up-to-date information retrieval, fact-checking, and research as part of automated flows.