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SeekrFlow Overview

  • What is SeekrFlow
  • Components
    • Agents
      • Models
      • Tools
    • Data engine
      • Storage
      • AI-ready data
    • Fine-tuning
      • Instruction fine-tuning
      • Context-grounded fine-tuning
      • Reinforcement tuning (GRPO)
      • Preference tuning (DPO)
      • Low-rank adaptation (LoRA)
      • Vision language tuning
      • Choose a fine-tuning method
    • Deployments
    • Explainability
      • Context attribution
      • Training data attribution
    • Content moderation
  • Use cases

SeekrFlow User Guide

  • Access SeekrFlow
  • Dashboard
  • Agent Builder
    • Create an agent
    • Manage agents
  • Data Engine
    • AI-Ready Data
    • Storage
      • File Storage
      • Vector Stores
  • Fine-tuning
  • Deployments (UI)
  • Playground
    • Test a model
    • Chat with an agent
    • Understand a response

SeekrFlow Developer Guide

  • Getting started
  • Agents
    • Create and manage agents
    • Threads
    • Messages
    • Structured Outputs
    • Tools
      • File search
      • Web search
      • Code interpreter
      • Custom tools
      • Agent as tool
  • Data engine
    • Prepare and ingest files
    • Monitor ingestion
    • Create and populate a vector database
    • Create instruction fine-tuning data
    • Create context-grounded fine-tuning data
  • Fine-tuning
    • Create a Fine-Tuning Job
    • Reinforcement tuning
    • Preference tuning
    • Vision language tuning
    • Deploy a Fine-tuned Model for Inference
    • Dedicated Inference
  • Explainability
    • Context attribution
    • Training data attribution
  • Integrations
    • OpenAI
    • LangChain
  • Content Moderation SDK
    • Seekr Content-Guard model
    • Meta Llama-Guard model

SeekrFlow MCP Server

  • Connect to MCP Server
  1. SeekrFlow Developer Guide
  2. Explainability

Training data attribution

Updated about 2 hours ago


Context attribution
Integrations