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Refa: add more logs to KG. (#8889)
### What problem does this PR solve? ### Type of change - [x] Refactoring
This commit is contained in:
@ -55,12 +55,18 @@ class Extractor:
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if response:
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return response
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_, system_msg = message_fit_in([{"role": "system", "content": system}], int(self._llm.max_length * 0.92))
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response = self._llm.chat(system_msg[0]["content"], hist, conf)
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response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
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if response.find("**ERROR**") >= 0:
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logging.warning(f"Extractor._chat got error. response: {response}")
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return ""
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set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
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for attempt in range(3):
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try:
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response = self._llm.chat(system_msg[0]["content"], hist, conf)
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response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
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if response.find("**ERROR**") >= 0:
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raise Exception(response)
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set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
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except Exception as e:
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logging.exception(e)
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if attempt == 2:
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raise
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return response
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def _entities_and_relations(self, chunk_key: str, records: list, tuple_delimiter: str):
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@ -39,6 +39,14 @@ from rag.nlp import rag_tokenizer, search
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from rag.utils.redis_conn import RedisDistributedLock
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@timeout(30, 2)
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async def _is_strong_enough(chat_model, embedding_model):
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_ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"]))
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res = await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role":"user", "content": "Are you strong enough!?"}]))
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if res.find("**ERROR**") >= 0:
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raise Exception(res)
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async def run_graphrag(
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row: dict,
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language,
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@ -48,6 +56,11 @@ async def run_graphrag(
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embedding_model,
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callback,
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):
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# Pressure test for GraphRAG task
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async with trio.open_nursery() as nursery:
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for _ in range(12):
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nursery.start_soon(_is_strong_enough, chat_model, embedding_model)
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start = trio.current_time()
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tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
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chunks = []
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@ -65,7 +78,7 @@ async def run_graphrag(
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doc_id,
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chunks,
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language,
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row["kb_parser_config"]["graphrag"]["entity_types"],
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row["kb_parser_config"]["graphrag"].get("entity_types", []),
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chat_model,
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embedding_model,
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callback,
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