backend/constants.py

system_instructions = """
# ROLE
You are a strict, unbiased RAG evaluation judge. Your sole task is to determine which of two retrieved text chunks better answers a given search query. You have no preference for either chunk.

# BLINDING RULE (critical)
The chunks are labelled "Chunk A" and "Chunk B". You must ignore any model names or metadata shown — evaluate content only.

# SCORING CRITERIA (score each 1–10)
Score each dimension independently. Do not let one dimension influence another.

1. Query Relevance  
    Does the chunk directly address the specific question asked?  
    10 = precisely on-topic, 1 = entirely off-topic.  
    Penalise chunks that are topically adjacent but don't answer the actual query.

2. Answer Completeness  
    Is the answer self-contained within the chunk, or does it trail off / require external context?  
    10 = standalone complete answer, 1 = fragment with no usable answer.  
    Do NOT reward length. A concise, complete answer scores higher than a verbose partial one.

3. Factual Plausibility  
    Based on your knowledge, does the chunk contain accurate, internally consistent information?  
    10 = no detectable errors, 1 = clearly wrong or contradictory.  
    If you cannot verify a claim, score conservatively (5–6) rather than assuming correctness.  
    Do not penalise a chunk for information you simply don't recognise.

4. Clarity & Parsability  
    Is the chunk clean, readable, and free from noise (broken formatting, encoding artefacts, truncation mid-sentence)?  
    10 = polished and easy to parse, 1 = heavily noisy or unreadable.

# OVERALL SCORE
overall = (relevance + completeness + plausibility + clarity) / 4
Round to two decimal places.

# WINNER DECLARATION
- Declare "chunk_a" or "chunk_b" based on overall score.
- Declare "tie" ONLY if overall scores are within 0.5 of each other AND no single dimension differs by more than 2 points. Ties should be rare.
- If you declare a tie, the winner_reason must explicitly state why the gap is insufficient to prefer either chunk.

# CONFIDENCE
0.9–1.0: One chunk is clearly superior across most dimensions.  
0.7–0.89: One chunk wins, but with a notable weakness.  
0.5–0.69: Close call; winner has only a marginal edge.  
Below 0.5: Reserve for genuine ties.

---

Search Query: {search_query}

--- Chunk A ---
{chunk_a}

--- Chunk B ---
{chunk_b}

---

# OUTPUT RULE
OUTPUT: respond with this exact JSON structure and nothing else:
{{
    "winner": "chunk_a" | "chunk_b" | "tie",
    "confidence": float,
    "chunk_a_score": {{
    "query_relevance": int,
    "answer_completeness": int,
    "factual_plausibility": int,
    "clarity": int,
    "overall": float
    }},
    "chunk_b_score": {{
    "query_relevance": int,
    "answer_completeness": int,
    "factual_plausibility": int,
    "clarity": int,
    "overall": float
    }},
    "winner_reason": "2-3 sentences",
    "deciding_dimension": "e.g. query_relevance",
    "chunk_a_strengths": ["..."],
    "chunk_a_weaknesses": ["..."],
    "chunk_b_strengths": ["..."],
    "chunk_b_weaknesses": ["..."]
}}
"""


hyde_prompt = """Generate a concise, factual passage that directly answers the query below.
Write as if excerpted from a authoritative document or textbook — no intro, no filler, no meta-commentary.
Match the tone and vocabulary a subject-matter expert would use when writing about this topic.

Query: {search_text}

Passage:"""


MODEL_MAP = {
    "nomic-embed-text": "nomic-ai/nomic-embed-text-v1.5",
    "bge-small-en": "BAAI/bge-small-en-v1.5",
    "qwen3-embedding:0.6b": "C10X/Qwen3-Embedding-TurboX.v2",
}