Add user team context to RAG queries

- RAGChat accepts teamContext prop with players, budget, transfers
- TeamManagement passes team data to RAGChat
- LLM service API accepts user_context in query request
- RAG pipeline includes team context in LLM prompt for personalized advice

Author: Alexander-Rees

frontend/src/components/RAGChat.js

@@ -2,7 +2,7 @@ import React, { useState, useRef, useEffect } from "react";
 import axios from "axios";
 import "./RAGChat.css";
 
-const RAGChat = () => {
+const RAGChat = ({ teamContext }) => {
   const [query, setQuery] = useState("");
   const [messages, setMessages] = useState([]);
   const [loading, setLoading] = useState(false);
@@ -33,10 +33,23 @@ const RAGChat = () => {
     try {
       // Call LLM service - use environment variable or default to localhost
       const llmApiUrl = process.env.REACT_APP_LLM_API_URL || "http://localhost:5002";
-      const response = await axios.post(`${llmApiUrl}/api/v1/query`, {
+      
+      // Build request with optional team context
+      const requestBody = {
         query: userMessage,
         include_sources: true,
-      });
+      };
+      
+      // Add team context if available
+      if (teamContext) {
+        requestBody.user_context = {
+          team_players: teamContext.players || [],
+          budget: teamContext.budget,
+          free_transfers: teamContext.freeTransfers,
+        };
+      }
+      
+      const response = await axios.post(`${llmApiUrl}/api/v1/query`, requestBody);
 
       // Add assistant response
       setMessages([

frontend/src/components/TeamManagement.js

@@ -180,8 +180,14 @@ const TeamManagement = () => {
         </button>
       </div>
 
-      {/* RAG Chat Component */}
-      <RAGChat />
+      {/* RAG Chat Component - pass team context */}
+      <RAGChat 
+        teamContext={{
+          players: teamData || [],
+          budget: optimizationSettings.budget,
+          freeTransfers: optimizationSettings.freeTransfers,
+        }}
+      />
 
       <div className="team-content">
         {/* Only show import section if no team data */}

llm-service/app/core/rag.py

@@ -65,7 +65,8 @@ def generate_response(
         self,
         query: str,
         context_chunks: list[dict],
-        system_prompt: str = None
+        system_prompt: str = None,
+        user_context: dict = None
     ) -> str:
         """
         Generate response using LLM with retrieved context
@@ -74,6 +75,7 @@ def generate_response(
             query: User query
             context_chunks: Retrieved context chunks
             system_prompt: Custom system prompt (optional)
+            user_context: User's team context (optional)
             
         Returns:
             Generated response
@@ -84,9 +86,40 @@ def generate_response(
             for chunk in context_chunks
         ])
 
-        # Default system prompt
+        # Build user team context if provided
+        team_context_text = ""
+        if user_context:
+            team_context_text = "\n\n--- USER'S TEAM CONTEXT ---\n"
+            
+            if user_context.get('team_players'):
+                players = user_context['team_players']
+                team_context_text += f"Current Squad ({len(players)} players):\n"
+                for p in players[:15]:
+                    name = p.get('name', 'Unknown')
+                    position = p.get('position', '?')
+                    team = p.get('team', '?')
+                    points = p.get('totalPoints', p.get('total_points', 0))
+                    value = p.get('value', 0)
+                    team_context_text += f"- {name} ({position}, {team}) - £{value}m, {points} pts\n"
+            
+            if user_context.get('budget'):
+                team_context_text += f"\nRemaining Budget: £{user_context['budget']}m\n"
+            
+            if user_context.get('free_transfers') is not None:
+                team_context_text += f"Free Transfers: {user_context['free_transfers']}\n"
+            
+            team_context_text += "--- END TEAM CONTEXT ---\n"
+        
+        # Default system prompt - updated to reference team context
         if system_prompt is None:
-            system_prompt = """You are an expert Fantasy Premier League (FPL) assistant. 
+            if user_context:
+                system_prompt = """You are an expert Fantasy Premier League (FPL) assistant. 
+You provide helpful, accurate advice based on expert analysis and data.
+Use the provided context to answer questions, but also apply your FPL knowledge.
+IMPORTANT: The user has provided their current team. Tailor your advice specifically to their squad, budget, and available transfers.
+Be concise and actionable in your recommendations."""
+            else:
+                system_prompt = """You are an expert Fantasy Premier League (FPL) assistant. 
 You provide helpful, accurate advice based on expert analysis and data.
 Use the provided context to answer questions, but also apply your FPL knowledge.
 Be concise and actionable in your recommendations."""
@@ -95,7 +128,7 @@ def generate_response(
         user_message = f"""Context from FPL experts:
 
 {context_text}
-
+{team_context_text}
 Question: {query}
 
 Please provide a helpful answer based on the expert context above."""
@@ -113,7 +146,8 @@ def answer_question(
         query: str,
         category: str = None,
         gameweek: int = None,
-        include_sources: bool = True
+        include_sources: bool = True,
+        user_context: dict = None
     ) -> dict:
         """
         Complete RAG pipeline: retrieve + generate
@@ -123,11 +157,15 @@ def answer_question(
             category: Filter by category
             gameweek: Filter by gameweek
             include_sources: Include source metadata in response
+            user_context: User's team context (optional)
             
         Returns:
             Dict with 'answer' and optionally 'sources'
         """
         logger.info(f"RAG query: {query}")
+        if user_context:
+            player_count = len(user_context.get('team_players', []))
+            logger.info(f"User context: {player_count} players, budget={user_context.get('budget')}, transfers={user_context.get('free_transfers')}")
 
         # Retrieve context
         context_chunks = self.retrieve_context(
@@ -144,8 +182,8 @@ def answer_question(
                 "sources": []
             }
 
-        # Generate response
-        answer = self.generate_response(query, context_chunks)
+        # Generate response with user context
+        answer = self.generate_response(query, context_chunks, user_context=user_context)
 
         result = {"answer": answer}
 

llm-service/app/main.py

@@ -75,11 +75,18 @@ async def service_info():
     }
 
 
+class UserContext(BaseModel):
+    team_players: Optional[list] = None
+    budget: Optional[float] = None
+    free_transfers: Optional[int] = None
+
+
 class QueryRequest(BaseModel):
     query: str
     category: Optional[str] = None
     gameweek: Optional[int] = None
     include_sources: bool = True
+    user_context: Optional[UserContext] = None
 
 
 class QueryResponse(BaseModel):
@@ -92,11 +99,22 @@ async def query_rag(request: QueryRequest):
     """Query the RAG system"""
     try:
         rag = get_rag_pipeline()
+        
+        # Convert user_context to dict if provided
+        user_context = None
+        if request.user_context:
+            user_context = {
+                'team_players': request.user_context.team_players,
+                'budget': request.user_context.budget,
+                'free_transfers': request.user_context.free_transfers,
+            }
+        
         result = rag.answer_question(
             query=request.query,
             category=request.category,
             gameweek=request.gameweek,
-            include_sources=request.include_sources
+            include_sources=request.include_sources,
+            user_context=user_context
         )
         return QueryResponse(**result)
     except Exception as e: