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Feat: support tree structured deep-research policy. (#12559)

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### What problem does this PR solve?

#12558
### Type of change

- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
2026-01-13 09:41:35 +08:00
committed by GitHub
parent 867ec94258
commit 44bada64c9
15 changed files with 1166 additions and 1381 deletions
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61
rag/prompts/generator.py
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@ -38,7 +38,7 @@ def get_value(d, k1, k2):
def chunks_format(reference):
if not reference or (reference is not dict):
if not reference or not isinstance(reference, dict):
return []
return [
{
@ -485,20 +485,26 @@ async def gen_meta_filter(chat_mdl, meta_data: dict, query: str) -> dict:
return {"conditions": []}
async def gen_json(system_prompt: str, user_prompt: str, chat_mdl, gen_conf=None):
async def gen_json(system_prompt: str, user_prompt: str, chat_mdl, gen_conf={}, max_retry=2):
from graphrag.utils import get_llm_cache, set_llm_cache
cached = get_llm_cache(chat_mdl.llm_name, system_prompt, user_prompt, gen_conf)
if cached:
return json_repair.loads(cached)
_, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length)
ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:], gen_conf=gen_conf)
ans = re.sub(r"(^.*</think>|```json\n|```\n*$)", "", ans, flags=re.DOTALL)
try:
res = json_repair.loads(ans)
set_llm_cache(chat_mdl.llm_name, system_prompt, ans, user_prompt, gen_conf)
return res
except Exception:
logging.exception(f"Loading json failure: {ans}")
err = ""
ans = ""
for _ in range(max_retry):
if ans and err:
msg[-1]["content"] += f"\nGenerated JSON is as following:\n{ans}\nBut exception while loading:\n{err}\nPlease reconsider and correct it."
ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:], gen_conf=gen_conf)
ans = re.sub(r"(^.*</think>|```json\n|```\n*$)", "", ans, flags=re.DOTALL)
try:
res = json_repair.loads(ans)
set_llm_cache(chat_mdl.llm_name, system_prompt, ans, user_prompt, gen_conf)
return res
except Exception as e:
logging.exception(f"Loading json failure: {ans}")
err += str(e)
TOC_DETECTION = load_prompt("toc_detection")
@ -847,8 +853,6 @@ async def run_toc_from_text(chunks, chat_mdl, callback=None):
TOC_RELEVANCE_SYSTEM = load_prompt("toc_relevance_system")
TOC_RELEVANCE_USER = load_prompt("toc_relevance_user")
async def relevant_chunks_with_toc(query: str, toc: list[dict], chat_mdl, topn: int = 6):
import numpy as np
try:
@ -876,8 +880,6 @@ async def relevant_chunks_with_toc(query: str, toc: list[dict], chat_mdl, topn:
META_DATA = load_prompt("meta_data")
async def gen_metadata(chat_mdl, schema: dict, content: str):
template = PROMPT_JINJA_ENV.from_string(META_DATA)
for k, desc in schema["properties"].items():
@ -890,3 +892,34 @@ async def gen_metadata(chat_mdl, schema: dict, content: str):
_, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length)
ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:])
return re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
SUFFICIENCY_CHECK = load_prompt("sufficiency_check")
async def sufficiency_check(chat_mdl, question: str, ret_content: str):
try:
return await gen_json(
PROMPT_JINJA_ENV.from_string(SUFFICIENCY_CHECK).render(question=question, retrieved_docs=ret_content),
"Output:\n",
chat_mdl
)
except Exception as e:
logging.exception(e)
return {}
MULTI_QUERIES_GEN = load_prompt("multi_queries_gen")
async def multi_queries_gen(chat_mdl, question: str, query:str, missing_infos:list[str], ret_content: str):
try:
return await gen_json(
PROMPT_JINJA_ENV.from_string(MULTI_QUERIES_GEN).render(
original_question=question,
original_query=query,
missing_info="\n - ".join(missing_infos),
retrieved_docs=ret_content
),
"Output:\n",
chat_mdl
)
except Exception as e:
logging.exception(e)
return {}