afv-library/skills/developing-agentforce/assets/prompt-rag-search.agent

# Prompt Template with RAG Search Template
# =========================================
#
# This template demonstrates the pattern for combining Prompt Templates
# with Data Cloud RAG (Retrieval Augmented Generation) for grounded responses.
#
# Pattern: Knowledge search + grounded response generation
# Use when: FAQ bots, product knowledge agents, documentation assistants
#
# CREDIT CONSUMPTION:
# - Prompt Templates: 2-16 credits per invocation
# - Retriever actions: 20 credits per search
# - TIP: Cache retriever results in variables, reuse across subagents

system:
   messages:
      welcome: "Hello! I can help answer questions about our products and services."
      error: "I apologize, I couldn't find the information you need."
   instructions: "You are a helpful knowledge assistant. Always provide grounded answers based on retrieved knowledge."

config:
   agent_name: "RAGSearchAgent"
   agent_label: "Knowledge Assistant"
   description: "Agent demonstrating Prompt Template with Data Cloud RAG pattern"
   default_agent_user: "agent@yourorg.com"  # REQUIRED: Change to valid Einstein Agent User

variables:
   # Search state
   search_query: mutable string = ""
      description: "User's current search query"
   search_results: mutable string = ""
      description: "Retrieved knowledge chunks (cached for reuse)"
   has_results: mutable boolean = False
      description: "Whether search returned results"

   # Response tracking
   response_generated: mutable boolean = False
      description: "Whether a response has been generated for current query"

start_agent entry:
   description: "Entry point - welcome and route to knowledge search"
   reasoning:
      instructions: |
         Welcome the user and offer to help with questions.
      actions:
         go_search: @utils.transition to @subagent.knowledge_search
            description: "Start knowledge search"

# ============================================================
# KNOWLEDGE SEARCH SUBAGENT (RAG Pattern)
# ============================================================

subagent knowledge_search:
   description: "Search knowledge base and generate grounded responses"
   reasoning:
      instructions: ->
         # POST-ACTION: Generate response after retrieval
         if @variables.has_results == True and @variables.response_generated == False:
            # Use Prompt Template to generate grounded response
            run @actions.Generate_Grounded_Response
               with query = @variables.search_query
               with context = @variables.search_results
            set @variables.response_generated = True

         # NO RESULTS: Escalate or try different search
         if @variables.has_results == False and @variables.search_query != "":
            | I couldn't find information about that subject.
            | Would you like to try a different search, or speak with a human agent?

         # INITIAL STATE: Ask for question
         if @variables.search_query == "":
            | What would you like to know? I can help with:
            | - Product information
            | - Pricing and plans
            | - Technical specifications
            | - Troubleshooting guides

      actions:
         # Retriever action for RAG search
         # This searches Data Cloud knowledge base
         search_knowledge: @actions.Search_Knowledge_Base
            description: "Search for relevant information"
            with query = ...  # LLM extracts user's question
            include_in_progress_indicator: True
            progress_indicator_message: "Searching our knowledge base..."
            set @variables.search_query = @outputs.original_query
            set @variables.search_results = @outputs.retrieved_chunks
            set @variables.has_results = @outputs.has_results
            set @variables.response_generated = False  # Reset for new search

         # Prompt Template for grounded response
         # Configure in Agentforce Assets with:
         # - Template instructions referencing retrieved context
         # - Set output is_displayable: True (response shown to user)
         generate_answer: @actions.Generate_Grounded_Response
            description: "Generate answer from retrieved knowledge"
            available when @variables.has_results == True
            with query = @variables.search_query
            with context = @variables.search_results

         # New search
         new_search: @utils.setVariables
            description: "Search for something else"
            with search_query = ""
            with search_results = ""
            with has_results = False
            with response_generated = False

         escalate_now: @utils.escalate
            description: "Transfer to human agent"

# ============================================================
# FOLLOW-UP QUESTIONS SUBAGENT
# ============================================================

subagent follow_up:
   description: "Handle follow-up questions using cached context"
   reasoning:
      instructions: ->
         # COST OPTIMIZATION: Reuse cached search_results instead of re-searching
         if @variables.search_results != "":
            | Based on what we discussed:
            run @actions.Generate_Grounded_Response
               with query = ...  # Follow-up question extracted by LLM
               with context = @variables.search_results  # Reuse cached results!
         else:
            transition to @subagent.knowledge_search

      actions:
         back_to_search: @utils.transition to @subagent.knowledge_search
            description: "Start a new search"