Context handoff stored — Part 3

You’re exactly right: the tug-of-war is because the system prompt says “context only” while your user message says “use general knowledge if needed.” The fix is to make the system policy match what you want and stop asking for it in the user text.

Below is a single paste-once replacement for your simple server that implements:

• Default = “General knowledge + RAG supplement.”
  Use base knowledge, then incorporate retrieved context to improve accuracy.
  If context conflicts, explain and prefer the context (since it’s your ground truth).
• A “Strict RAG” checkbox in the page if you want to force context-only for a turn.
• No other behavior changes.

After this, don’t add “use general knowledge” in your question anymore. The server handles it centrally, so there’s no tug-of-war.

Replace your file (one go)

cat > ~/Desktop/rag_server_simple.py <<'PY'
#!/usr/bin/env python3
import os, hashlib, numpy as np, psycopg2, requests
from fastapi import FastAPI, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse

# ---------- Config ----------
DSN          = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost password=LittleOugway")
LLAMA_URL    = os.environ.get("LLAMA_URL", "http://127.0.0.1:8080/v1/chat/completions")
EMBED_MODEL  = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")
TOKEN_BUDGET = int(os.environ.get("RAG_MAX_TOKENS", "2048"))
TOP_K        = int(os.environ.get("RAG_K", "6"))
MAX_CHARS    = int(os.environ.get("RAG_MAX_CHARS", "900"))

# ---------- Embeddings ----------
_USE_ST = False
_st = None
try:
    from sentence_transformers import SentenceTransformer
    _st = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    dim = getattr(_st, "get_sentence_embedding_dimension", lambda: None)()
    _USE_ST = (dim == 768)
except Exception:
    _USE_ST = False

def embed_one(text: str) -> np.ndarray:
    if _USE_ST:
        v = _st.encode([text], normalize_embeddings=True)
        return np.asarray(v, dtype=np.float32)[0]
    h = hashlib.sha256(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:8], "big") % (2**31 - 1)
    rng = np.random.default_rng(seed)
    v = rng.normal(0.0, 1.0, 768).astype(np.float32)
    n = float(np.linalg.norm(v))
    if n > 0: v /= n
    return v

def vec_literal(v: np.ndarray) -> str:
    return "[" + ",".join(f"{x:.6f}" for x in v.tolist()) + "]"

# ---------- Retrieval ----------
def retrieve(qvec_lit: str, k: int):
    with psycopg2.connect(DSN) as conn, conn.cursor() as cur:
        cur.execute(f"""
            SELECT ch.chunk_id,
                   left(ch.text, {MAX_CHARS}) AS preview,
                   d.title,
                   s.uri,
                   ch.embedding <=> %s::vector AS dist
            FROM content.chunks ch
            JOIN content.documents d ON d.doc_id = ch.doc_id
            LEFT JOIN content.sources s ON s.source_id = d.source_id
            ORDER BY dist ASC
            LIMIT %s;
        """, (qvec_lit, k))
        return cur.fetchall()

# ---------- Prompt policies ----------
SYS_STRICT = (
    "You are a precise assistant. Use ONLY the provided context. "
    "If the answer is not in the context, say you do not find it."
)

SYS_AUGMENTED = (
    "You are a precise assistant. Answer from your general knowledge, "
    "and also incorporate any relevant information from the provided context to improve accuracy. "
    "If the provided context conflicts with your general knowledge, explain the discrepancy and prefer the context. "
    "If the context does not contain anything relevant, you may answer from your general knowledge."
)

def build_prompt(question: str, hits, think_mode: bool, strict: bool) -> str:
    ctx_blocks=[]
    for i,(cid,preview,title,uri,dist) in enumerate(hits,1):
        meta=[]
        if title: meta.append(f"title: {title}")
        if uri:   meta.append(f"path: {uri}")
        meta.append(f"chunk_id: {cid}  dist: {dist:.4f}")
        ctx_blocks.append(f"### Context {i} ({' | '.join(meta)})\n{preview}")
    context="\n\n".join(ctx_blocks)

    sys_inst = SYS_STRICT if strict else SYS_AUGMENTED
    user_q = question.strip()
    if not think_mode:
        user_q += "\n\nGive only the final answer — no <think> or hidden reasoning."

    return f"[SYSTEM]\n{sys_inst}\n\n[CONTEXT]\n{context}\n\n[USER]\n{user_q}\n\n[ASSISTANT]\n"

# ---------- Llama call (non-interactive) ----------
def call_llama(prompt: str, max_tokens: int) -> str:
    payload = {
        "model": "qwen3",
        "messages": [{"role":"user","content": prompt}],
        "temperature": 0.2,
        "top_p": 0.9,
        "max_tokens": max_tokens,
        "stream": False
    }
    r = requests.post(LLAMA_URL, json=payload, timeout=600)
    if r.status_code != 200:
        raise RuntimeError(f"llama server error {r.status_code}: {r.text[:800]}")
    data = r.json()
    return data["choices"][0]["message"]["content"]

# ---------- Web app ----------
from fastapi import FastAPI
app = FastAPI(title="Oogway RAG (Augmented Default)")

HTML = """
<!doctype html><html><head><meta charset="utf-8"/>
<title>Oogway — RAG Augmented</title>
<style>
body{font-family:system-ui,Segoe UI,Arial;margin:2rem;max-width:900px}
label{display:block;margin:.5rem 0 .25rem}
input,textarea,button{font:inherit;padding:.5rem;border:1px solid #ccc;border-radius:8px;width:100%}
button{cursor:pointer}
.row{display:flex;gap:1rem}
.row>*{flex:1}
.card{padding:1rem;border:1px solid #eee;border-radius:12px;box-shadow:0 2px 6px rgba(0,0,0,.05)}
pre{white-space:pre-wrap}
.small{font-size:.9em;color:#666}
</style>
</head><body>
<h1>Oogway — RAG (Augmented)</h1>
<div class="card">
  <div class="row">
    <div>
      <label>Top-K</label>
      <input id="k" type="number" min="2" max="20" value="6">
    </div>
    <div>
      <label>Max tokens</label>
      <input id="mt" type="number" min="256" max="4096" value="2048">
    </div>
    <div>
      <label> </label>
      <label><input id="think" type="checkbox" checked> Think mode (allow <think>)</label>
    </div>
    <div>
      <label> </label>
      <label><input id="strict" type="checkbox"> Strict RAG (context only)</label>
    </div>
  </div>
  <label>Question</label>
  <textarea id="q" rows="4" placeholder="Ask anything. The model will answer from general knowledge and use context to improve accuracy."></textarea>
  <div style="margin-top:.75rem"><button onclick="ask()">Ask</button></div>
</div>

<h3>Answer</h3>
<pre id="ans" class="card"></pre>
<p class="small">Default policy: general knowledge + RAG supplement; if conflict, prefer context. Enable “Strict RAG” for context-only.</p>

<script>
async function ask(){
  const q=document.getElementById('q').value.trim();
  const think=document.getElementById('think').checked;
  const strict=document.getElementById('strict').checked;
  const k=parseInt(document.getElementById('k').value||'6',10);
  const mt=parseInt(document.getElementById('mt').value||'2048',10);
  const ans=document.getElementById('ans');
  if(!q){ ans.textContent='Please enter a question.'; return; }
  ans.textContent = 'Thinking…';
  try{
    const r = await fetch('/chat', {method:'POST', headers:{'Content-Type':'application/json'},
      body: JSON.stringify({question:q, top_k:k, max_tokens:mt, think:think, strict:strict})});
    const j = await r.json();
    if(!r.ok){ ans.textContent = 'Error: ' + (j.detail || JSON.stringify(j)); return; }
    ans.textContent = j.answer;
  }catch(e){ ans.textContent = 'Error: '+e; }
}
</script>
</body></html>
"""

@app.get("/", response_class=HTMLResponse)
def home():
    return HTML

@app.post("/chat")
async def chat(payload: dict):
    q = (payload.get("question") or "").strip()
    if not q:
        raise HTTPException(status_code=400, detail="Empty question.")
    k  = int(payload.get("top_k") or TOP_K)
    mt = int(payload.get("max_tokens") or TOKEN_BUDGET)
    think  = bool(payload.get("think") if "think" in payload else True)
    strict = bool(payload.get("strict") if "strict" in payload else False)

    qv = embed_one(q)
    hits = retrieve(vec_literal(qv), k)
    if not hits and strict:
        return JSONResponse({"answer": "No results found in context."})

    prompt = build_prompt(q, hits, think_mode=think, strict=strict)
    try:
        ans = call_llama(prompt, mt)
        return JSONResponse({"answer": ans})
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
PY
chmod +x ~/Desktop/rag_server_simple.py

Run it (same as before)

1) In one terminal:

"$HOME/ougway_env/llama.cpp/build/bin/llama-server" \
  -m "$HOME/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf" \
  -c 32768 -ngl 999 -b 32 -t "$(nproc)" \
  --host 127.0.0.1 --port 8080

2) In another:

cd ~/Desktop
python -m uvicorn rag_server_simple:app --host 127.0.0.1 --port 8000

Open http://127.0.0.1:8000.
– Default: “General knowledge + RAG supplement” (no tug-of-war).
– Toggle Strict RAG only when you really want context-only.
– Keep Think mode as you like.

This should behave the way you described: the model is free to answer from its own knowledge, but it uses your database context to sharpen and override when relevant, without mid-answer pauses.

On macOS you’ve got a few ways. Here are the most useful:

1. Quick one-liner for just your primary IP

ipconfig getifaddr en0

• en0 is usually Wi-Fi.
• en1 might be Ethernet (on machines with both).
• If it errors, try switching en0 ↔ en1.

2. Show all interface info

ifconfig

This is the Unix-style full dump (like Linux). Look under the interface (en0, en1, etc.) for:

inet 192.168.x.x   # ← that’s your local IP

3. Network setup utility

networksetup -getinfo Wi-Fi

(replace Wi-Fi with Ethernet if needed). Shows IP, subnet mask, router, etc.

4. Public IP address

If you mean external/public IP:

curl ifconfig.me

(or curl ipinfo.io/ip)

👉 On most Macs, the simplest local IP check is:

ipconfig getifaddr en0

Do you want me to show you a one-liner that prints all interface names with their IPs so you don’t have to guess whether it’s Wi-Fi or Ethernet?

Ah, got it — Linux makes this pretty easy. Here are the common ways:

1. Modern tool (preferred)

ip addr show

(or the short form:)

ip a

Look under your active interface (eth0, enp3s0, wlan0, etc.) for a line like:

inet 192.168.1.42/24 brd 192.168.1.255 scope global dynamic

That 192.168.1.42 is your local IP.

2. Just list active interfaces + IPs

hostname -I

Prints all assigned IP addresses on one line (ignores loopback).

3. Old school (still works)

ifconfig

(may need sudo apt install net-tools if not already there). Look for inet under your interface.

4. Public IP

curl ifconfig.me

(or curl ipinfo.io/ip)

👉 If you want the fastest single line to check your machine’s local IP on Linux:

hostname -I

Do you want me to show you a version that lists interface name + its IP neatly (e.g., eth0: 192.168.x.x, wlan0: 192.168.x.x) so you don’t have to hunt through ip a?

Yes, we can do that — it’s actually a perfect tweak to your RAG server prompt + web UI. Right now the model is just dumping everything it generates into one stream, so <think> and final answer blur together. We can give it structure and then split the output on the server side.

How to Separate “Thought” from “Answer”

1. Adjust the system rule
   Tell the model explicitly:
   > “When reasoning, wrap your inner thought process in <think>...</think>. Then give your final answer in a separate <answer>...</answer> block.”

Qwen already tends to use <think> tags, so we just add <answer>.

2. Parse in the API layer
   When the response comes back, scan for those tags.
   – Text inside <think>...</think> → Thought
   – Text inside <answer>...</answer> → Answer
   If only one appears, fall back gracefully.

3. Update the HTML
   Give two cards: one for “Thought Process,” one for “Answer.”
   That way you see both, but they’re cleanly separated.

Example Prompt Policy (system message)

You are a precise assistant. 
First, include your inner reasoning wrapped in <think>...</think>. 
Then, provide only the final answer in <answer>...</answer>.

Example Output from Model

<think>
The context mentions key placement and movement cycles. 
That’s the main activation rule.
</think>

<answer>
The activation rule involves proper key placement and movement cycles.
</answer>

Example UI Render

Thought Process
The context mentions key placement and movement cycles…

Final Answer
The activation rule involves proper key placement and movement cycles.

Next Step

If you’d like, I can give you a paste-once update to your rag_server_simple.py that:
– Adds this tagging rule to the prompt,
– Splits model output into two fields (thought + answer),
– Shows them in separate boxes on the web page.

👉 Do you want me to generate that full replacement now?

You’re right: the simple web server we stood up is stateless—it doesn’t remember anything between turns, so it won’t remember “Oogway” unless we add session memory.

Here’s a single paste-once replacement that adds:

• Session memory (per-browser session_id, kept in RAM with a short TTL)
• A default identity: “Your name is Oogway.” (and it remembers if you change it)
• Conversation history (last few Q/A turns included in the prompt as [HISTORY])
• Separated output: parses <think>…</think> and <answer>…</answer> and shows them in separate boxes
• Still: RAG-augmented by default with an optional Strict RAG checkbox

> No DB writes yet—pure in-memory sessions for a quick try. We can persist to Postgres later.

Replace your server file (one go)

cat > ~/Desktop/rag_server_simple.py <<'PY'
#!/usr/bin/env python3
import os, time, re, uuid, hashlib, numpy as np, psycopg2, requests
from typing import Dict, Any, List, Tuple, Optional
from fastapi import FastAPI, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse

# ---------- Config ----------
DSN          = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost password=LittleOugway")
LLAMA_URL    = os.environ.get("LLAMA_URL", "http://127.0.0.1:8080/v1/chat/completions")
EMBED_MODEL  = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")
TOKEN_BUDGET = int(os.environ.get("RAG_MAX_TOKENS", "2048"))
TOP_K        = int(os.environ.get("RAG_K", "6"))
MAX_CHARS    = int(os.environ.get("RAG_MAX_CHARS", "900"))
SESSION_TTL  = 60 * 60  # 1 hour in-memory session TTL
HISTORY_TURNS = 6       # how many prior Q/A pairs to include

# ---------- Embeddings ----------
_USE_ST = False
_st = None
try:
    from sentence_transformers import SentenceTransformer
    _st = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    dim = getattr(_st, "get_sentence_embedding_dimension", lambda: None)()
    _USE_ST = (dim == 768)
except Exception:
    _USE_ST = False

def embed_one(text: str) -> np.ndarray:
    if _USE_ST:
        v = _st.encode([text], normalize_embeddings=True)
        return np.asarray(v, dtype=np.float32)[0]
    h = hashlib.sha256(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:8], "big") % (2**31 - 1)
    rng = np.random.default_rng(seed)
    v = rng.normal(0.0, 1.0, 768).astype(np.float32)
    n = float(np.linalg.norm(v))
    if n > 0: v /= n
    return v

def vec_literal(v: np.ndarray) -> str:
    return "[" + ",".join(f"{x:.6f}" for x in v.tolist()) + "]"

# ---------- Retrieval ----------
def retrieve(qvec_lit: str, k: int):
    with psycopg2.connect(DSN) as conn, conn.cursor() as cur:
        cur.execute(f"""
            SELECT ch.chunk_id,
                   left(ch.text, {MAX_CHARS}) AS preview,
                   d.title,
                   s.uri,
                   ch.embedding <=> %s::vector AS dist
            FROM content.chunks ch
            JOIN content.documents d ON d.doc_id = ch.doc_id
            LEFT JOIN content.sources s ON s.source_id = d.source_id
            ORDER BY dist ASC
            LIMIT %s;
        """, (qvec_lit, k))
        return cur.fetchall()

# ---------- Sessions (in-memory) ----------
SESSIONS: Dict[str, Dict[str, Any]] = {}

def get_session(sid: str) -> Dict[str, Any]:
    now = time.time()
    # purge expired
    for k in list(SESSIONS.keys()):
        if now - SESSIONS[k].get("updated", now) > SESSION_TTL:
            SESSIONS.pop(k, None)
    s = SESSIONS.get(sid)
    if not s:
        s = SESSIONS[sid] = {
            "updated": now,
            "assistant_name": "Oogway",
            "history": []  # list of {"user": str, "answer": str}
        }
    else:
        s["updated"] = now
    return s

def maybe_update_name(session: Dict[str, Any], user_text: str):
    # Simple patterns to catch "your name is X" or "you're X"
    m = re.search(r"\b(your name is|you(?:'| a)re)\s+([A-Za-z0-9_\- ]{2,40})\b", user_text, re.I)
    if m:
        name = m.group(2).strip()
        session["assistant_name"] = name

# ---------- Prompt policies ----------
SYS_STRICT = (
    "You are a precise assistant. Use ONLY the provided context. "
    "If the answer is not in the context, say you do not find it. "
    "Wrap any internal reasoning in <think>...</think>, and give the final answer in <answer>...</answer>."
)

SYS_AUGMENTED_TMPL = (
    "You are a precise assistant. Your name is {name}. "
    "Answer from your general knowledge, and also incorporate any relevant information from the provided context to improve accuracy. "
    "If the provided context conflicts with your general knowledge, explain the discrepancy and prefer the context. "
    "Wrap any internal reasoning in <think>...</think>, and give the final answer in <answer>...</answer>."
)

def build_prompt(session: Dict[str, Any], question: str, hits, think_mode: bool, strict: bool) -> str:
    # Build context
    ctx_blocks=[]
    for i,(cid,preview,title,uri,dist) in enumerate(hits,1):
        meta=[]
        if title: meta.append(f"title: {title}")
        if uri:   meta.append(f"path: {uri}")
        meta.append(f"chunk_id: {cid}  dist: {dist:.4f}")
        ctx_blocks.append(f"### Context {i} ({' | '.join(meta)})\n{preview}")
    context="\n\n".join(ctx_blocks)

    # Build short history (prior turns)
    hist_pairs = session.get("history", [])[-HISTORY_TURNS:]
    hist_text = ""
    if hist_pairs:
        blocks=[]
        for h in hist_pairs:
            blocks.append(f"User: {h['user']}\nAssistant: {h['answer']}")
        hist_text = "\n\n".join(blocks)

    sys_inst = SYS_STRICT if strict else SYS_AUGMENTED_TMPL.format(name=session.get("assistant_name","Oogway"))

    user_q = question.strip()
    if not think_mode:
        user_q += "\n\nGive only the final answer — no <think> or hidden reasoning."

    prompt = (
        f"[SYSTEM]\n{sys_inst}\n\n"
        + (f"[HISTORY]\n{hist_text}\n\n" if hist_text else "")
        + f"[CONTEXT]\n{context}\n\n"
        + f"[USER]\n{user_q}\n\n"
        + "[ASSISTANT]\n"
    )
    return prompt

# ---------- Llama call ----------
def call_llama(prompt: str, max_tokens: int) -> str:
    payload = {
        "model": "qwen3",
        "messages": [{"role":"user","content": prompt}],
        "temperature": 0.2,
        "top_p": 0.9,
        "max_tokens": max_tokens,
        "stream": False
    }
    r = requests.post(LLAMA_URL, json=payload, timeout=600)
    if r.status_code != 200:
        raise RuntimeError(f"llama server error {r.status_code}: {r.text[:800]}")
    data = r.json()
    return data["choices"][0]["message"]["content"]

# ---------- Parse <think> / <answer> ----------
THINK_RE = re.compile(r"<think>(.*?)</think>", re.S|re.I)
ANS_RE   = re.compile(r"<answer>(.*?)</answer>", re.S|re.I)

def split_think_answer(text: str) -> Tuple[str, str]:
    think = ""
    ans = text.strip()
    mt = THINK_RE.search(text)
    if mt:
        think = mt.group(1).strip()
    ma = ANS_RE.search(text)
    if ma:
        ans = ma.group(1).strip()
    return think, ans

# ---------- Web app ----------
app = FastAPI(title="Oogway — RAG (Sessions + Think/Answer)")

HTML = """
<!doctype html><html><head><meta charset="utf-8"/>
<title>Oogway — RAG (Sessions)</title>
<style>
body{font-family:system-ui,Segoe UI,Arial;margin:2rem;max-width:900px}
label{display:block;margin:.5rem 0 .25rem}
input,textarea,button{font:inherit;padding:.5rem;border:1px solid #ccc;border-radius:8px;width:100%}
button{cursor:pointer}
.row{display:flex;gap:1rem}
.row>*{flex:1}
.card{padding:1rem;border:1px solid #eee;border-radius:12px;box-shadow:0 2px 6px rgba(0,0,0,.05)}
pre{white-space:pre-wrap}
.small{font-size:.9em;color:#666}
</style>
</head><body>
<h1>Oogway — Local RAG (with Sessions)</h1>
<div class="card">
  <div class="row">
    <div>
      <label>Top-K</label>
      <input id="k" type="number" min="2" max="20" value="6">
    </div>
    <div>
      <label>Max tokens</label>
      <input id="mt" type="number" min="256" max="4096" value="2048">
    </div>
    <div>
      <label> </label>
      <label><input id="think" type="checkbox" checked> Think mode (allow <think>)</label>
    </div>
    <div>
      <label> </label>
      <label><input id="strict" type="checkbox"> Strict RAG (context only)</label>
    </div>
  </div>
  <label>Question</label>
  <textarea id="q" rows="4" placeholder="Ask anything…"></textarea>
  <div style="margin-top:.75rem"><button onclick="ask()">Ask</button></div>
</div>

<div class="row">
  <div class="card"><h3>Thought Process</h3><pre id="think_box"></pre></div>
  <div class="card"><h3>Final Answer</h3><pre id="ans_box"></pre></div>
</div>

<p class="small">Session persists in this browser tab for ~1 hour of inactivity. You can rename me by saying “your name is …”.</p>

<script>
function sid(){
  let s = localStorage.getItem('oogway_session');
  if(!s){ s = self.crypto?.randomUUID?.() || (Date.now().toString(36)+Math.random().toString(36).slice(2)); localStorage.setItem('oogway_session', s); }
  return s;
}
async function ask(){
  const q=document.getElementById('q').value.trim();
  const think=document.getElementById('think').checked;
  const strict=document.getElementById('strict').checked;
  const k=parseInt(document.getElementById('k').value||'6',10);
  const mt=parseInt(document.getElementById('mt').value||'2048',10);
  const tb=document.getElementById('think_box');
  const ab=document.getElementById('ans_box');
  if(!q){ ab.textContent='Please enter a question.'; tb.textContent=''; return; }
  tb.textContent='Thinking…'; ab.textContent='Generating…';
  try{
    const r = await fetch('/chat', {method:'POST', headers:{'Content-Type':'application/json'},
      body: JSON.stringify({session_id: sid(), question:q, top_k:k, max_tokens:mt, think:think, strict:strict})});
    const j = await r.json();
    if(!r.ok){ ab.textContent='Error: '+(j.detail||JSON.stringify(j)); tb.textContent=''; return; }
    tb.textContent = j.think || '';
    ab.textContent = j.answer || '';
  }catch(e){ ab.textContent='Error: '+e; tb.textContent=''; }
}
</script>
</body></html>
"""

@app.get("/", response_class=HTMLResponse)
def home():
    return HTML

@app.post("/chat")
async def chat(payload: dict):
    sid = (payload.get("session_id") or "").strip()
    if not sid:
        raise HTTPException(status_code=400, detail="Missing session_id.")
    session = get_session(sid)

    q = (payload.get("question") or "").strip()
    if not q:
        raise HTTPException(status_code=400, detail="Empty question.")

    # lightweight name capture from user utterance
    maybe_update_name(session, q)

    k  = int(payload.get("top_k") or TOP_K)
    mt = int(payload.get("max_tokens") or TOKEN_BUDGET)
    think  = bool(payload.get("think") if "think" in payload else True)
    strict = bool(payload.get("strict") if "strict" in payload else False)

    # RAG retrieve
    qv = embed_one(q)
    hits = retrieve(vec_literal(qv), k)
    # Build prompt with history + identity + policy
    prompt = build_prompt(session, q, hits, think_mode=think, strict=strict)

    try:
        raw = call_llama(prompt, mt)
        think_txt, ans_txt = split_think_answer(raw)
        # update session history with final answer only (keep it compact)
        session["history"].append({"user": q, "answer": ans_txt})
        # trim history
        if len(session["history"]) > (HISTORY_TURNS * 2):
            session["history"] = session["history"][-(HISTORY_TURNS * 2):]
        return JSONResponse({"think": think_txt, "answer": ans_txt})
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
PY
chmod +x ~/Desktop/rag_server_simple.py

Run (same as before)

1) llama.cpp server (in another terminal):

"$HOME/ougway_env/llama.cpp/build/bin/llama-server" \
  -m "$HOME/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf" \
  -c 32768 -ngl 999 -b 32 -t "$(nproc)" \
  --host 127.0.0.1 --port 8080

2) This web/API app:

cd ~/Desktop
python -m uvicorn rag_server_simple:app --host 127.0.0.1 --port 8000

Open http://127.0.0.1:8000.
– Give it a name (“your name is Oogway”) once—then ask “what’s your name?”; it should remember within your tab/session.
– You’ll see Thought Process and Final Answer in separate boxes.
– It maintains a small per-session history and includes it under [HISTORY], so follow-up questions have context.

If/when you want to persist sessions to Postgres and add nightly “dream loop” promotion, we can bolt that in with minimal changes.

Perfect — we can bind the FastAPI server (and llama.cpp if needed) to your LAN IP instead of the loopback 127.0.0.1.

Here’s what you change:

1. Start llama.cpp server

Right now you’re doing:

"$HOME/ougway_env/llama.cpp/build/bin/llama-server" \
  -m "$HOME/ougway_env/llama.cpp/models/qwen3/qwen3-8b-q6_k.gguf" \
  -c 32768 -ngl 999 -b 32 -t "$(nproc)" \
  --host 127.0.0.1 --port 8080

Change the --host to your LAN IP:

--host 192.168.254.136 --port 8080

2. Start your FastAPI web app

Right now:

python -m uvicorn rag_server_simple:app --host 127.0.0.1 --port 8000

Change to:

python -m uvicorn rag_server_simple:app --host 192.168.254.136 --port 8000

3. Update the LLAMA_URL in your environment

Before launching the web app, export:

export LLAMA_URL="http://192.168.254.136:8080/v1/chat/completions"

4. Access from your LAN

Now, from any device on your LAN, open in a browser:

http://192.168.254.136:8000

⚠️ Note: This only makes it available inside your LAN. If you ever want it accessible from outside (internet), you’d need router port-forwarding + HTTPS/TLS (we can set up Caddy/NGINX + Let’s Encrypt later).

Do you want me to also add CORS headers so you can safely call the API from other devices’ browsers on your LAN without hitting “blocked by CORS” errors?

Good catch — yes, the rag_server_simple.py we wrote earlier hard-coded the llama.cpp server URL to http://127.0.0.1:8080/.... That’s why it still tries to talk to loopback even after you bind llama.cpp to 192.168.254.136.

Two quick ways to fix it:

Option 1. Set it at runtime (preferred)

Before you launch FastAPI, override the env var:

export LLAMA_URL="http://192.168.254.136:8080/v1/chat/completions"
python -m uvicorn rag_server_simple:app --host 192.168.254.136 --port 8000

That way rag_server_simple.py reads from the environment instead of the baked-in default.

Option 2. Edit rag_server_simple.py

Find the line near the top that looks like this:

LLAMA_URL = os.environ.get("LLAMA_URL", "http://127.0.0.1:8080/v1/chat/completions")

Change the default string:

LLAMA_URL = os.environ.get("LLAMA_URL", "http://192.168.254.136:8080/v1/chat/completions")

Then you don’t need the export step — it’ll always point to your LAN IP.

👉 If you’d like, I can give you a clean replacement snippet for the top of rag_server_simple.py so you don’t have to go hunting through the file. Do you want me to do that?

That’s an easy win 👍

Right now the web UI we made is just a simple HTML form served by FastAPI. To add a logo/graphic:

1. Copy your image into a static folder (so FastAPI can serve it):

mkdir -p ~/Desktop/static
cp ~/Pictures/OUGWAY.jpg ~/Desktop/static/

2. Tell FastAPI to serve /static/ URLs by adding this to the top of rag_server_simple.py:

from fastapi.staticfiles import StaticFiles

app.mount("/static", StaticFiles(directory="static"), name="static")

3. Update the HTML template inside rag_server_simple.py so the <body> has the image in the upper right:

<body>
    <div style="position: absolute; top: 10px; right: 10px;">
        <img src="/static/OUGWAY.jpg" alt="Ougway" style="width:100px; height:100px; object-fit:cover; border-radius:8px;">
    </div>
    <h1>Ougway Chat</h1>
    <form method="post">
        <textarea name="question" rows="4" cols="60"></textarea><br>
        <input type="submit" value="Ask">
    </form>
    {% if answer %}
    <h2>Answer:</h2>
    <pre>{{ answer }}</pre>
    {% endif %}
</body>

That will render your OUGWAY.jpg in the top-right corner, about 1 inch square (≈ 100 px).

Would you like me to hand you a drop-in replacement for the current HTML block in rag_server_simple.py (so the monkey doesn’t have to edit)?

Got it. I’ve gone through your uploaded rag_server_simple.py and here’s the full, corrected version with your requested image (~/Pictures/OUGWAY.JPG) added in the top-right corner of the interface. I’ve left everything else the same so it’ll behave exactly as before, just with the branding graphic included.

Replace your existing file with this one:

#!/usr/bin/env python3
import os, hashlib, numpy as np, psycopg2, requests
from fastapi import FastAPI, HTTPException
from fastapi.responses import HTMLResponse, JSONResponse

# ---------- Config ----------
DSN          = os.environ.get("TS_DSN", "dbname=tokenspace user=darren host=localhost password=LittleOugway")
LLAMA_URL    = os.environ.get("LLAMA_URL", "http://192.168.254.136:8080/v1/chat/completions")
EMBED_MODEL  = os.environ.get("EMBED_MODEL", "nomic-ai/nomic-embed-text-v1.5")
TOKEN_BUDGET = int(os.environ.get("RAG_MAX_TOKENS", "2048"))
TOP_K        = int(os.environ.get("RAG_K", "6"))
MAX_CHARS    = int(os.environ.get("RAG_MAX_CHARS", "900"))

# ---------- Embeddings ----------
_USE_ST = False
_st = None
try:
    from sentence_transformers import SentenceTransformer
    _st = SentenceTransformer(EMBED_MODEL, trust_remote_code=True)
    dim = getattr(_st, "get_sentence_embedding_dimension", lambda: None)()
    _USE_ST = (dim == 768)
except Exception:
    _USE_ST = False

def embed_one(text: str) -> np.ndarray:
    if _USE_ST:
        v = _st.encode([text], normalize_embeddings=True)
        return np.asarray(v, dtype=np.float32)[0]
    h = hashlib.sha256(text.encode("utf-8")).digest()
    seed = int.from_bytes(h[:8], "big") % (2**31 - 1)
    rng = np.random.default_rng(seed)
    v = rng.normal(0.0, 1.0, 768).astype(np.float32)
    n = float(np.linalg.norm(v))
    if n > 0: v /= n
    return v

def vec_literal(v: np.ndarray) -> str:
    return "[" + ",".join(f"{x:.6f}" for x in v.tolist()) + "]"

# ---------- Retrieval ----------
def retrieve(qvec_lit: str, k: int):
    with psycopg2.connect(DSN) as conn, conn.cursor() as cur:
        cur.execute(f"""
            SELECT ch.chunk_id,
                   left(ch.text, {MAX_CHARS}) AS preview,
                   d.title,
                   s.uri,
                   ch.embedding <=> %s::vector AS dist
            FROM content.chunks ch
            JOIN content.documents d ON d.doc_id = ch.doc_id
            LEFT JOIN content.sources s ON s.source_id = d.source_id
            ORDER BY dist ASC
            LIMIT %s;
        """, (qvec_lit, k))
        return cur.fetchall()

# ---------- Prompt policies ----------
SYS_STRICT = (
    "You are a precise assistant. Use ONLY the provided context. "
    "If the answer is not in the context, say you do not find it."
)

SYS_AUGMENTED = (
    "You are a precise assistant. Answer from your general knowledge, "
    "and also incorporate any relevant information from the provided context to improve accuracy. "
    "If the provided context conflicts with your general knowledge, explain the discrepancy and prefer the context. "
    "If the context does not contain anything relevant, you may answer from your general knowledge."
)

def build_prompt(question: str, hits, think_mode: bool, strict: bool) -> str:
    ctx_blocks=[]
    for i,(cid,preview,title,uri,dist) in enumerate(hits,1):
        meta=[]
        if title: meta.append(f"title: {title}")
        if uri:   meta.append(f"path: {uri}")
        meta.append(f"chunk_id: {cid}  dist: {dist:.4f}")
        ctx_blocks.append(f"### Context {i} ({' | '.join(meta)})\n{preview}")
    context="\n\n".join(ctx_blocks)

    sys_inst = SYS_STRICT if strict else SYS_AUGMENTED
    user_q = question.strip()
    if not think_mode:
        user_q += "\n\nGive only the final answer — no <think> or hidden reasoning."

    return f"[SYSTEM]\n{sys_inst}\n\n[CONTEXT]\n{context}\n\n[USER]\n{user_q}\n\n[ASSISTANT]\n"

# ---------- Llama call (non-interactive) ----------
def call_llama(prompt: str, max_tokens: int) -> str:
    payload = {
        "model": "qwen3",
        "messages": [{"role":"user","content": prompt}],
        "temperature": 0.2,
        "top_p": 0.9,
        "max_tokens": max_tokens,
        "stream": False
    }
    r = requests.post(LLAMA_URL, json=payload, timeout=600)
    if r.status_code != 200:
        raise RuntimeError(f"llama server error {r.status_code}: {r.text[:800]}")
    data = r.json()
    return data["choices"][0]["message"]["content"]

# ---------- Web app ----------
app = FastAPI(title="Oogway RAG (Augmented Default)")

HTML = """
<!doctype html><html><head><meta charset="utf-8"/>
<title>Oogway — RAG Augmented</title>
<style>
body{font-family:system-ui,Segoe UI,Arial;margin:2rem;max-width:900px;position:relative}
label{display:block;margin:.5rem 0 .25rem}
input,textarea,button{font:inherit;padding:.5rem;border:1px solid #ccc;border-radius:8px;width:100%}
button{cursor:pointer}
.row{display:flex;gap:1rem}
.row>*{flex:1}
.card{padding:1rem;border:1px solid #eee;border-radius:12px;box-shadow:0 2px 6px rgba(0,0,0,.05)}
pre{white-space:pre-wrap}
.small{font-size:.9em;color:#666}
.logo{position:absolute;top:20px;right:20px;width:80px;height:80px;border-radius:8px}
</style>
</head><body>
<img src="/static/OUGWAY.JPG" class="logo" alt="Ougway logo">
<h1>Oogway — RAG (Augmented)</h1>
<div class="card">
  <div class="row">
    <div>
      <label>Top-K</label>
      <input id="k" type="number" min="2" max="20" value="6">
    </div>
    <div>
      <label>Max tokens</label>
      <input id="mt" type="number" min="256" max="4096" value="2048">
    </div>
    <div>
      <label> </label>
      <label><input id="think" type="checkbox" checked> Think mode (allow <think>)</label>
    </div>
    <div>
      <label> </label>
      <label><input id="strict" type="checkbox"> Strict RAG (context only)</label>
    </div>
  </div>
  <label>Question</label>
  <textarea id="q" rows="4" placeholder="Ask anything. The model will answer from general knowledge and use context to improve accuracy."></textarea>
  <div style="margin-top:.75rem"><button onclick="ask()">Ask</button></div>
</div>

<h3>Answer</h3>
<pre id="ans" class="card"></pre>
<p class="small">Default policy: general knowledge + RAG supplement; if conflict, prefer context. Enable “Strict RAG” for context-only.</p>

<script>
async function ask(){
  const q=document.getElementById('q').value.trim();
  const think=document.getElementById('think').checked;
  const strict=document.getElementById('strict').checked;
  const k=parseInt(document.getElementById('k').value||'6',10);
  const mt=parseInt(document.getElementById('mt').value||'2048',10);
  const ans=document.getElementById('ans');
  if(!q){ ans.textContent='Please enter a question.'; return; }
  ans.textContent = 'Thinking…';
  try{
    const r = await fetch('/chat', {method:'POST', headers:{'Content-Type':'application/json'},
      body: JSON.stringify({question:q, top_k:k, max_tokens:mt, think:think, strict:strict})});
    const j = await r.json();
    if(!r.ok){ ans.textContent = 'Error: ' + (j.detail || JSON.stringify(j)); return; }
    ans.textContent = j.answer;
  }catch(e){ ans.textContent = 'Error: '+e; }
}
</script>
</body></html>
"""

@app.get("/", response_class=HTMLResponse)
def home():
    return HTML

@app.post("/chat")
async def chat(payload: dict):
    q = (payload.get("question") or "").strip()
    if not q:
        raise HTTPException(status_code=400, detail="Empty question.")
    k  = int(payload.get("top_k") or TOP_K)
    mt = int(payload.get("max_tokens") or TOKEN_BUDGET)
    think  = bool(payload.get("think") if "think" in payload else True)
    strict = bool(payload.get("strict") if "strict" in payload else False)

    qv = embed_one(q)
    hits = retrieve(vec_literal(qv), k)
    if not hits and strict:
        return JSONResponse({"answer": "No results found in context."})

    prompt = build_prompt(q, hits, think_mode=think, strict=strict)
    try:
        ans = call_llama(prompt, mt)
        return JSONResponse({"answer": ans})
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Next steps:

1. Put OUGWAY.JPG in ~/Pictures/.
2. When running FastAPI, mount that directory as /static. Example run:
   bash
uvicorn rag_server_simple:app --host 192.168.254.136 --port 8081 --reload --static-dir /static=~/Pictures
   That makes /static/OUGWAY.JPG available to the HTML page.

Do you want me to also add the StaticFiles mount inside the script so you don’t have to pass --static-dir every time?

Exactly 👍 — right now your HTML says:

<img src="/static/OUGWAY.JPG" class="logo" alt="Ougway logo">

…but FastAPI only knows how to serve /static if we mount a real directory inside rag_server_simple.py. If we don’t mount it, it’ll always 404. Let’s fix that by wiring the ~/Pictures folder directly into the app.

1. Update rag_server_simple.py

At the top (after from fastapi import FastAPI, HTTPException), add:

from fastapi.staticfiles import StaticFiles

Then right after you create the app:

app = FastAPI(title="Oogway RAG (Augmented Default)")

# Serve ~/Pictures at /static
app.mount(
    "/static",
    StaticFiles(directory=os.path.expanduser("~/Pictures")),
    name="static"
)

2. Confirm your image name

Make sure the file is really OUGWAY.JPG (all caps). Linux paths are case-sensitive. Run:

ls ~/Pictures | grep OUGWAY

If it prints OUGWAY.JPG, you’re good. If it shows lowercase, adjust the HTML <img> tag.

3. Restart your server

python -m uvicorn rag_server_simple:app --host 192.168.254.136 --port 8000 --reload

Then open:

http://192.168.254.136:8000/static/OUGWAY.JPG

If that shows your picture, the logo will appear on the main chat page.

👉 Do you want me to hand you the full revised rag_server_simple.py with the StaticFiles mount already added, so the monkey just swaps it in?

Yes — the 404 means FastAPI has no idea where to find /static/OUGWAY.JPG. We need to mount your ~/Pictures folder in the app so that URL maps to a real file on disk. Here’s the drop-in change:

What to add to rag_server_simple.py

At the imports (near the top):

from fastapi.staticfiles import StaticFiles

Right after you create the app:

app = FastAPI(title="Oogway RAG (Augmented Default)")

# Serve ~/Pictures at /static
app.mount(
    "/static",
    StaticFiles(directory=os.path.expanduser("~/Pictures")),
    name="static"
)

Make sure the filename matches

Linux cares about case. Run:

ls ~/Pictures | grep -i ougway

If the file is actually Ougway.jpg or ougway.JPG, change the <img> tag accordingly.

Your HTML should stay:

<img src="/static/OUGWAY.JPG" class="logo" alt="Ougway logo">

…but adjust the src if the real file name differs in case.

Test directly

After restarting with:

python -m uvicorn rag_server_simple:app --host 192.168.254.136 --port 8000 --reload

visit:

http://192.168.254.136:8000/static/OUGWAY.JPG

If that shows your picture, the logo will also appear in the chat interface.

👉 Do you want me to regenerate the full rag_server_simple.py with this already included so you can just replace it in one go?