changed nk, system prompt, logic, and added highlightign
This commit is contained in:
111
mcp_server.py
111
mcp_server.py
@@ -1,17 +1,17 @@
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import os
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os.environ["MCP_ALLOW_ALL_ORIGINS"] = "1"
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import re
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import json
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import numpy as np
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from pathlib import Path
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from sentence_transformers import SentenceTransformer
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from qdrant_client import QdrantClient
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from qdrant_client.models import Filter, FieldCondition, MatchValue
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from mcp.server.fastmcp import FastMCP
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# Add this right after the FastMCP import, before anything else
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from mcp.server import streamable_http
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streamable_http.ALLOWED_ORIGINS = None # try this first
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streamable_http.ALLOWED_ORIGINS = None
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# If that doesn't work, patch the actual check function:
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import mcp.server.streamable_http as _sh
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_sh.is_valid_origin = lambda origin, allowed: True
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import uvicorn
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@@ -20,7 +20,6 @@ from starlette.middleware.base import BaseHTTPMiddleware
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from starlette.requests import Request
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from mcp.server.transport_security import TransportSecuritySettings, TransportSecurityMiddleware
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# Monkey-patch to disable DNS rebinding protection entirely
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TransportSecurityMiddleware.__init__ = lambda self, settings=None: setattr(
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self, "settings", TransportSecuritySettings(enable_dns_rebinding_protection=False)
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)
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@@ -38,42 +37,94 @@ with open(project_root / "data" / "processed" / "parent_lookup.json") as f:
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parent_lookup = json.load(f)
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# ── Config ─────────────────────────────────────────────────────────────────
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TOP_K = 10
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TOP_K = 6
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SYSTEM_PROMPT = """You are an expert AP US History tutor helping a student ace their APUSH exam.
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SYSTEM_PROMPT = """You are an elite AP US History tutor. Your only goal is to help the student master APUSH and score a 5.
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You have access to the search_textbook tool. Call it before answering ANY history question.
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━━━ TOOL USE ━━━
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ALWAYS call search_textbook before answering any history question — no exceptions.
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For complex questions (LEQ/DBQ/thematic), call it 2-3 times with different search angles to get full coverage.
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ANSWERING:
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- Cite inline like (Ch5, p.153) after every specific claim
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- **Bold** key terms, dates, names, and critical facts
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- Correct false premises directly — don't reinforce wrong assumptions
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- If the textbook doesn't cover it, answer from general knowledge and prefix with "Outside textbook:"
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━━━ CITATIONS ━━━
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- Cite inline after every specific claim: (Ch5, p.153)
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- The **bolded sentences** in each source are the most relevant — prioritize citing and building on those
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- Never invent or guess a citation — if unsure, say "Outside textbook:"
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- If the textbook is silent on something relevant, supplement with general knowledge, clearly labeled
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FORMAT — match the question type:
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- One word/fact → one word
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- SAQ → 1 focused paragraph, dense with evidence
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- LEQ/DBQ → full essay: context, thesis, body paragraphs with evidence, nuance
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- General question → clear prose, as long as needed
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━━━ ACCURACY ━━━
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- Correct false premises immediately and directly — never reinforce a wrong assumption
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- Distinguish causation from correlation, primary from secondary causes
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- Note historiographical debates where relevant (e.g. revisionist vs traditional interpretations)
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- Be precise with dates, names, legislation, and turning points — vagueness loses points on the exam
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END EVERY RESPONSE WITH:
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━━━ FORMAT — match the question type exactly ━━━
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- Identification / one fact → one concise answer, one citation
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- SAQ (Short Answer) → 3 tight paragraphs: claim → evidence → analysis. No intro/conclusion fluff
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- LEQ (Long Essay) → Full essay: contextualization → thesis → 3 body paragraphs (each with specific evidence + analysis) → conclusion with complexity
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- DBQ → Same as LEQ plus: sourcing, audience/purpose/context for docs, corroboration across docs
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- Compare/contrast → Use parallel structure, explicit similarities AND differences
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- General question → Clear prose, as long as needed, no padding
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━━━ APUSH EXAM SKILLS ━━━
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When writing essays, explicitly hit the College Board rubric:
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- Contextualization: zoom out to broader historical context BEFORE the thesis
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- Thesis: historically defensible, specific, addresses complexity (not just "there were many causes")
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- Evidence: at least 2 specific pieces of evidence per body paragraph
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- Analysis: explain HOW and WHY, not just what happened
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- Complexity: demonstrate nuance — turning points, continuity vs change, multiple causation, or cross-period connections
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━━━ END EVERY RESPONSE WITH ━━━
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---
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**Sources Used:**
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[list every source from the tool output with chapter, section, page, and score]
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**Retrieval Confidence:** HIGH/MEDIUM/LOW"""
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[list each source: Ch# › Section › p.### — score: X.XXX]
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**Retrieval Confidence:** HIGH / MEDIUM / LOW
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**Exam Tip:** [one sentence of targeted advice for how this topic typically appears on the APUSH exam]"""
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# ── Embed ──────────────────────────────────────────────────────────────────
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def embed_query(query: str) -> list[float]:
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def embed_query(query: str) -> np.ndarray:
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return model.encode(
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f"search_query: {query}",
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normalize_embeddings=True,
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).tolist()
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)
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# ── Highlight ──────────────────────────────────────────────────────────────
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def highlight_passage(query_emb: np.ndarray, passage: str) -> str:
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"""
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Bold the top 3 most query-relevant sentences using the already-loaded
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embedder. Reuses the query embedding computed during retrieval — zero
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extra model calls.
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"""
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sentences = [s.strip() for s in re.split(r'(?<=[.!?])\s+', passage) if len(s.strip()) > 20]
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if not sentences:
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return passage
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sent_embs = model.encode(
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[f"search_document: {s}" for s in sentences],
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normalize_embeddings=True,
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batch_size=32,
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show_progress_bar=False,
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)
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scores = sent_embs @ query_emb # cosine sim (both normalized)
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top_n = min(3, len(scores))
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threshold = float(sorted(scores)[-top_n])
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highlighted = passage
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for sent, score in zip(sentences, scores):
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if float(score) >= threshold:
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# avoid double-bolding if somehow already bolded
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if f"**{sent}**" not in highlighted:
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highlighted = highlighted.replace(sent, f"**{sent}**")
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return highlighted
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# ── Retrieve ───────────────────────────────────────────────────────────────
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def retrieve(query: str) -> dict:
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query_emb = embed_query(query) # compute once, reuse for highlighting
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hits = qdrant.query_points(
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collection_name=COLLECTION,
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query=embed_query(query),
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query=query_emb.tolist(),
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limit=TOP_K,
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query_filter=Filter(
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must_not=[
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@@ -98,20 +149,22 @@ def retrieve(query: str) -> dict:
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seen_parents.add(pid)
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unique_hits.append(h)
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unique_hits = unique_hits[:5]
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unique_hits = unique_hits[:4]
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sources = []
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for h in unique_hits:
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pid = h.payload["parent_id"]
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parts = parent_lookup.get(pid, [])
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full_text = "\n\n".join(p["text"] for p in parts)
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highlighted = highlight_passage(query_emb, full_text) # reuse query_emb
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sources.append({
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"score": h.score,
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"chapter_num": h.payload["chapter_num"],
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"chapter_title": h.payload["chapter_title"],
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"section_title": h.payload["section_title"],
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"textbook_page": h.payload["textbook_page"],
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"text": full_text,
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"text": highlighted,
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})
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return {
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@@ -124,13 +177,12 @@ def retrieve(query: str) -> dict:
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# ── Origin bypass middleware ────────────────────────────────────────────────
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class AllowAllOriginsMiddleware(BaseHTTPMiddleware):
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async def dispatch(self, request: Request, call_next):
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# Spoof origin so FastMCP's internal check passes
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request._headers = request.headers.mutablecopy()
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request._headers["origin"] = "http://127.0.0.1:11434"
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return await call_next(request)
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# ── MCP Server ─────────────────────────────────────────────────────────────
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mcp = FastMCP("APUSH Tutor")
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mcp = FastMCP("APUSH Tutor", instructions=SYSTEM_PROMPT)
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@mcp.tool()
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def search_textbook(query: str) -> str:
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@@ -159,11 +211,6 @@ def search_textbook(query: str) -> str:
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return header + passages + footer
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@mcp.prompt()
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def system_prompt() -> str:
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"""The APUSH tutor system prompt."""
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return SYSTEM_PROMPT
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# ── Run ────────────────────────────────────────────────────────────────────
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if __name__ == "__main__":
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app = mcp.streamable_http_app()
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