Переглянути джерело

Refa: add more logs to KG. (#8889)

### What problem does this PR solve?


### Type of change

- [x] Refactoring
tags/v0.20.0
Kevin Hu 3 місяці тому
джерело
коміт
729e6098f9
Аккаунт користувача з таким Email не знайдено
3 змінених файлів з 35 додано та 9 видалено
  1. 12
    6
      graphrag/general/extractor.py
  2. 14
    1
      graphrag/general/index.py
  3. 9
    2
      graphrag/utils.py

+ 12
- 6
graphrag/general/extractor.py Переглянути файл

@@ -55,12 +55,18 @@ class Extractor:
if response:
return response
_, system_msg = message_fit_in([{"role": "system", "content": system}], int(self._llm.max_length * 0.92))
response = self._llm.chat(system_msg[0]["content"], hist, conf)
response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
if response.find("**ERROR**") >= 0:
logging.warning(f"Extractor._chat got error. response: {response}")
return ""
set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
for attempt in range(3):
try:
response = self._llm.chat(system_msg[0]["content"], hist, conf)
response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
if response.find("**ERROR**") >= 0:
raise Exception(response)
set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
except Exception as e:
logging.exception(e)
if attempt == 2:
raise

return response

def _entities_and_relations(self, chunk_key: str, records: list, tuple_delimiter: str):

+ 14
- 1
graphrag/general/index.py Переглянути файл

@@ -39,6 +39,14 @@ from rag.nlp import rag_tokenizer, search
from rag.utils.redis_conn import RedisDistributedLock


@timeout(30, 2)
async def _is_strong_enough(chat_model, embedding_model):
_ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"]))
res = await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role":"user", "content": "Are you strong enough!?"}]))
if res.find("**ERROR**") >= 0:
raise Exception(res)


async def run_graphrag(
row: dict,
language,
@@ -48,6 +56,11 @@ async def run_graphrag(
embedding_model,
callback,
):
# Pressure test for GraphRAG task
async with trio.open_nursery() as nursery:
for _ in range(12):
nursery.start_soon(_is_strong_enough, chat_model, embedding_model)

start = trio.current_time()
tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
chunks = []
@@ -65,7 +78,7 @@ async def run_graphrag(
doc_id,
chunks,
language,
row["kb_parser_config"]["graphrag"]["entity_types"],
row["kb_parser_config"]["graphrag"].get("entity_types", []),
chat_model,
embedding_model,
callback,

+ 9
- 2
graphrag/utils.py Переглянути файл

@@ -484,16 +484,20 @@ async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, chang

semaphore = trio.Semaphore(5)
async with trio.open_nursery() as nursery:
for node in change.added_updated_nodes:
for ii, node in enumerate(change.added_updated_nodes):
node_attrs = graph.nodes[node]
async with semaphore:
if ii%100 == 9 and callback:
callback(msg=f"Get embedding of nodes: {ii}/{len(change.added_updated_nodes)}")
nursery.start_soon(graph_node_to_chunk, kb_id, embd_mdl, node, node_attrs, chunks)
for from_node, to_node in change.added_updated_edges:
for ii, (from_node, to_node) in enumerate(change.added_updated_edges):
edge_attrs = graph.get_edge_data(from_node, to_node)
if not edge_attrs:
# added_updated_edges could record a non-existing edge if both from_node and to_node participate in nodes merging.
continue
async with semaphore:
if ii%100 == 9 and callback:
callback(msg=f"Get embedding of edges: {ii}/{len(change.added_updated_edges)}")
nursery.start_soon(graph_edge_to_chunk, kb_id, embd_mdl, from_node, to_node, edge_attrs, chunks)
now = trio.current_time()
if callback:
@@ -502,6 +506,9 @@ async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, chang

es_bulk_size = 4
for b in range(0, len(chunks), es_bulk_size):
async with semaphore:
if b % 100 == es_bulk_size and callback:
callback(msg=f"Insert chunks: {b}/{len(chunks)}")
doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + es_bulk_size], search.index_name(tenant_id), kb_id))
if doc_store_result:
error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!"

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