'langchain' 검색 결과

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Write Python for n8n Code nodes to transform, filter, aggregate, and validate workflow data. Get guidance on reading input items, returning the required JSON structure, using standard library modules, and avoiding common errors within n8n's Python execution modes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transforming and filtering data in n8n with Python
• Calculating totals and basic statistics
• Extracting and validating text with regular expressions

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Search an existing repository OpenWiki to clarify architecture and dependencies, or create and update its documentation. Pages explain system behavior using source evidence, with a resumable workflow that tracks progress one page at a time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Look up architecture and dependency behavior in an existing wiki
• Create repository documentation grounded in source code
• Update wiki explanations after source changes

Add Mermaid diagrams to generated wiki pages to explain request flows, state changes, data relationships, and branching logic. Keep diagrams aligned with inspected source code and repair syntax problems in diagrams degraded during earlier runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Illustrate requests passing between components
• Show job lifecycles and state transitions
• Document relationships between data entities

Build a built-in connector that brings a new information source into OpenWiki. It guides the choice between an existing MCP server connection and custom code, covering authentication, incremental fetching, stored data, and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a new information source to OpenWiki
• Decide whether to use an existing read-only MCP server
• Implement incremental fetching and resumable pagination

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify the framework used by existing Python agent code
• Choose an executor for Claude SDK or OpenAI Agents SDK code
• Check whether a framework has native support

Configure coding agents, AI SDKs, and HTTP clients to send model requests through AntSeed’s local proxy. Find available providers and models, select a provider, set compatible API endpoints, and verify request usage and payment activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect Claude Code or Codex to AntSeed
• Configure a local endpoint for AI SDKs
• Compare providers’ models, protocols, and prices

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Write and troubleshoot native Python in n8n Code nodes. Covers data filtering and transformation, migration from older Python code, valid return formats, and restricted execution environments, including cases where errors pass unprocessed input downstream.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Filtering and aggregating data in Python Code nodes
• Migrating legacy Python (Beta) code
• Fixing input-variable and return-format errors

Instrument Python or TypeScript agents to record runs for Failproof AI, verify local event files, and troubleshoot missing sessions. The skill also guides building evaluator workers that score completed runs on your own infrastructure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning where an agent should record events
• Adding run logging to an existing agent
• Troubleshooting missing events or sessions

Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a RAG document collection from selected research papers
• Download and extract text from batches of paper PDFs
• Create a local vector database without paid embedding APIs

Try NeMo Relay through a CLI wrapper, a supported integration, or direct Python, Node.js, or Rust instrumentation. Choose a minimal first run and verify recorded tool and model activity before considering one additional plugin.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Try Relay with Codex or Claude Code through the CLI
• Choose a supported integration for an existing application
• Check recorded tool and LLM calls in an application

Choose and install the right NeMo Relay option for a command-line tool, a Python, Node.js, or Rust application, or a supported framework integration. Check basic availability and report the installation result before moving to runtime setup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trying NeMo Relay through a temporary CLI run
• Adding a Relay package to an existing application
• Installing Relay for a supported framework

Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Converts agent definitions between frameworks — exports to Claude Code, OpenAI, CrewAI, Lyzr, and GitHub Models formats, and imports from Claude, Cursor, and CrewAI projects. Use when the user wants to convert an agent, migrate to another framework, export to LangChain/AutoGen/CrewAI, or import from existing automation tools.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Migrate workloads from Google Cloud Platform to AWS — plus AI and agentic workloads from any provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform, app code, or billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. Do not use for: Azure migrations (see azure-to-aws), on-premises migrations, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, or GCP-to-GCP refactoring.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.