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69c12a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # app/modules/build_prompt.py
from runtime.session_store import get_session_controller
def build_planning_prompt(message, rag_context=""):
"""
Planner prompt: Always include the RAG context.
- If rag_context is empty, an empty string is used.
- 1st: Unconditionally generate 1 to 5 queries.
- 2nd: Dynamically control 0 to 5 queries after FT.
"""
system_message = (
"You are a planning assistant. Your role is to generate search queries "
"based on the user message and RAG context."
)
return (
f"{system_message}\n"
f"User question (multilingual): {message}\n\n"
f"Context from RAG DB (may be empty):\n{rag_context}\n\n"
"Task:\n"
"- Generate 1 to 5 concise search queries.\n"
"- If the RAG context already fully answers the question, you may generate 0 queries.\n"
"- Prefer official documentation, standards, academic or authoritative sources.\n"
"- Prefer English queries for global/academic sources.\n"
"- If the user question is strongly local or language-specific, also generate queries in the same language.\n"
"- Avoid redundant queries; use broader queries if they cover multiple facets.\n"
"- Output only the queries, one per line."
)
def build_generation_prompt(message, docs, history=None, hf_user=None, session_id=None, top_k=5):
"""
Answer generation prompt with context from retrieved documents.
- Include up to top_k document snippets as context.
- Include recent conversation history if available.
- System message is fixed internally for answer generation role.
"""
system_message = (
"You are an answer generation assistant. Your role is to synthesize "
"information from context and produce a clear, concise, Markdown-formatted answer "
"with citations in (title)[url] format."
)
# Sort by score if available, then take Top-K
sorted_docs = sorted(docs or [], key=lambda d: d.get("score", 0), reverse=True)
top_docs = sorted_docs[:top_k]
# Build structured context string
context_entries = []
for d in top_docs:
q = d.get("query", "")
title = d.get("title", "")
url = d.get("url", "")
snippet = d.get("snippet") or d.get("summary") or d.get("content") or ""
score = d.get("score", 0)
entry = (
f"Query: {q}\n"
f"Title: {title}\n"
f"URL: {url}\n"
f"Snippet: {snippet}\n"
f"Score: {score}\n"
)
context_entries.append(entry.strip())
context = "\n---\n".join(context_entries)
# Conversation history (optional)
history_text = ""
if history:
if history == "history_ctr":
history_controller = get_session_controller(hf_user, session_id)
try:
ctx = history_controller.build_prompt_history(message, max_tokens=1024, top_k=10)
history_text = "\nConversation history (relevant with user message):\n" + ctx
except Exception:
history_text = ""
else:
try:
last_items = history[-4:] if isinstance(history, list) else [str(history)]
history_text = "\nConversation history (recent):\n" + "\n".join(f"{r}: {c}" for r, c in last_items)
except Exception:
history_text = ""
# Final prompt assembly
return (
f"{system_message}\n"
f"{history_text}\n"
"Context (structured search results):\n"
f"{context}\n\n"
"Instructions:\n"
"- Provide the answer to the user message below using the context above.\n"
"- Answer in the same language as the user message.\n"
"- Follow Markdown format strictly.\n"
"- Include citations using (title)[url] format whenever referencing sources.\n"
"- Use the score field to prioritize higher relevance results first.\n"
"- Cross-check and summarize multiple sources when possible.\n"
"- If context is insufficient, state limitations clearly.\n\n"
f"User message: {message}\n"
"Answer:"
)
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