🎯 Goal

By the end of Day 6 you will have:

• faster RAG retrieval
• smoother LLM response flow
• reduced lag in dashboard
• model routing (7B / 14B / 20B automatically)
• caching for repeated queries
• a system that feels “production stable” on your Mac

This is the final step that turns your build into a daily research tool instead of a prototype.

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⚙️ STEP 1 — ADD BASIC CACHING (RAG SPEED BOOST)

Edit analysis_engine.py

Add caching for retrieval:

from functools import lru_cache

---

Wrap retrieval:

@lru_cache(maxsize=128)
def retrieve_context(query, k=3):
    query_embedding = embedder.encode(query).tolist()

    results = collection.query(
        query_embeddings=[query_embedding],
        n_results=k
    )

    return "\n".join(results["documents"][0])

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🧠 STEP 2 — MODEL ROUTING LOGIC (IMPORTANT)

Instead of manually choosing models, automate it:

Add function:

def select_model(question):

    q = question.lower()

    if "compare" in q or "deep" in q or "why" in q:
        return "deepseek-r1:20b"

    if "summarize" in q or "quick" in q:
        return "llama3.1:8b"

    return "qwen2.5:14b"

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Update main function:

def run_analysis(question, model=None):

    if model is None:
        model = select_model(question)

    context = retrieve_context(question)
    prompt = build_prompt(question, context)

    response = ollama.chat(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )

    return response["message"]["content"]

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⚡ STEP 3 — SPEED OPTIMIZATION RULES

HARD RULES:

• never send full datasets to LLM
• always use top-K retrieval only
• keep context under control
• avoid repeated embeddings

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IMPROVEMENT:

Reduce retrieval size:

n_results=2  # instead of 5 or 10

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💾 STEP 4 — DISK + MEMORY OPTIMIZATION

Add cache folders:

CACHE/
EMBEDDINGS/
TEMP/

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Rules:

• store embeddings once
• reuse them (never recompute unless data changes)
• delete temp analysis files periodically

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🖥️ STEP 5 — STREAMLIT PERFORMANCE FIXES

Edit dashboard.py

---

Add caching for UI:

@st.cache_data
def cached_analysis(question, model):
    return run_analysis(question, model=model)

---

Replace:

result = run_analysis(question, model=model)

With:

result = cached_analysis(question, model)

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🧠 STEP 6 — REDUCE LAG IN UI

Add spinner + delay control:

with st.spinner("Running analysis engine..."):

---

Limit reruns:

st.session_state["last_query"] = question

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🔁 STEP 7 — FINAL SYSTEM FLOW (OPTIMIZED)

User Query
   ↓
Model Router (7B / 14B / 20B)
   ↓
Cached RAG Retrieval
   ↓
Compressed Context Builder
   ↓
Ollama LLM Call
   ↓
Cached Result (Streamlit)
   ↓
Dashboard Display

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🧠 WHAT YOU JUST COMPLETED

You now have:

✔ fast retrieval system

✔ smart model selection

✔ reduced compute waste

✔ cached responses

✔ responsive UI

✔ stable local AI system

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🚫 WHAT THIS IS NOT

• ❌ not real-time trading system
• ❌ not predictive engine
• ❌ not self-learning AI
• ❌ not market-connected system

It is:

a fast, optimized historical trading research engine

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🏁 FINAL RESULT (AFTER DAY 6)

Your Mac now runs:

• RAG-based trading memory
• multi-model reasoning system
• structured analysis engine
• ChatGPT-style dashboard
• optimized performance layer

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💡 ONE-LINE SUMMARY

Day 6 turns your AI trading system into a fast, stable, locally running research platform with caching, model routing, and performance optimization.

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