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Feat: add basic Langfuse support for LLM module (#6443)
### What problem does this PR solve? #6155 Add basic Langfuse support for LLM module. A trace example: <img width="755" alt="image" src="https://github.com/user-attachments/assets/25c1f852-5116-486c-a47f-6097187142ca" /> ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Yongteng Lei
@ -13,26 +13,29 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import binascii
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import time
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from functools import partial
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import logging
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import re
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import time
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from copy import deepcopy
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from functools import partial
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from timeit import default_timer as timer
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from langfuse import Langfuse
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from agentic_reasoning import DeepResearcher
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from api import settings
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from api.db import LLMType, ParserType, StatusEnum
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from api.db.db_models import Dialog, DB
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from api.db.db_models import DB, Dialog
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from api.db.services.common_service import CommonService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import TenantLLMService, LLMBundle
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from api import settings
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from api.db.services.langfuse_service import TenantLangfuseService
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from api.db.services.llm_service import LLMBundle, TenantLLMService
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from rag.app.resume import forbidden_select_fields4resume
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from rag.app.tag import label_question
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from rag.nlp.search import index_name
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from rag.prompts import kb_prompt, message_fit_in, llm_id2llm_type, keyword_extraction, full_question, chunks_format, \
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citation_prompt
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from rag.utils import rmSpace, num_tokens_from_string
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from rag.prompts import chunks_format, citation_prompt, full_question, kb_prompt, keyword_extraction, llm_id2llm_type, message_fit_in
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from rag.utils import num_tokens_from_string, rmSpace
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from rag.utils.tavily_conn import Tavily
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@ -41,17 +44,13 @@ class DialogService(CommonService):
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@classmethod
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@DB.connection_context()
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def get_list(cls, tenant_id,
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page_number, items_per_page, orderby, desc, id, name):
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def get_list(cls, tenant_id, page_number, items_per_page, orderby, desc, id, name):
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chats = cls.model.select()
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if id:
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chats = chats.where(cls.model.id == id)
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if name:
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chats = chats.where(cls.model.name == name)
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chats = chats.where(
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(cls.model.tenant_id == tenant_id)
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& (cls.model.status == StatusEnum.VALID.value)
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)
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chats = chats.where((cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value))
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if desc:
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chats = chats.order_by(cls.model.getter_by(orderby).desc())
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else:
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@ -72,13 +71,12 @@ def chat_solo(dialog, messages, stream=True):
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tts_mdl = None
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if prompt_config.get("tts"):
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tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
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msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
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for m in messages if m["role"] != "system"]
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msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]
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if stream:
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last_ans = ""
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for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting):
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answer = ans
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delta_ans = ans[len(last_ans):]
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delta_ans = ans[len(last_ans) :]
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if num_tokens_from_string(delta_ans) < 16:
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continue
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last_ans = answer
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@ -110,6 +108,16 @@ def chat(dialog, messages, stream=True, **kwargs):
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check_llm_ts = timer()
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langfuse_tracer = None
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langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=dialog.tenant_id)
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if langfuse_keys:
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langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host)
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if langfuse.auth_check():
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langfuse_tracer = langfuse
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langfuse.trace = langfuse_tracer.trace(name=f"{dialog.name}-{llm_model_config['llm_name']}")
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check_langfuse_tracer_ts = timer()
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kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
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embedding_list = list(set([kb.embd_id for kb in kbs]))
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if len(embedding_list) != 1:
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@ -159,8 +167,7 @@ def chat(dialog, messages, stream=True, **kwargs):
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if p["key"] not in kwargs and not p["optional"]:
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raise KeyError("Miss parameter: " + p["key"])
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if p["key"] not in kwargs:
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prompt_config["system"] = prompt_config["system"].replace(
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"{%s}" % p["key"], " ")
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prompt_config["system"] = prompt_config["system"].replace("{%s}" % p["key"], " ")
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if len(questions) > 1 and prompt_config.get("refine_multiturn"):
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questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)]
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@ -189,9 +196,11 @@ def chat(dialog, messages, stream=True, **kwargs):
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knowledges = []
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if prompt_config.get("reasoning", False):
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reasoner = DeepResearcher(chat_mdl,
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prompt_config,
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partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3))
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reasoner = DeepResearcher(
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chat_mdl,
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prompt_config,
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partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3),
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)
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for think in reasoner.thinking(kbinfos, " ".join(questions)):
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if isinstance(think, str):
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@ -200,31 +209,34 @@ def chat(dialog, messages, stream=True, **kwargs):
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elif stream:
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yield think
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else:
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kbinfos = retriever.retrieval(" ".join(questions), embd_mdl, tenant_ids, dialog.kb_ids, 1, dialog.top_n,
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dialog.similarity_threshold,
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dialog.vector_similarity_weight,
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doc_ids=attachments,
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top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl,
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rank_feature=label_question(" ".join(questions), kbs)
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)
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kbinfos = retriever.retrieval(
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" ".join(questions),
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embd_mdl,
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tenant_ids,
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dialog.kb_ids,
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1,
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dialog.top_n,
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dialog.similarity_threshold,
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dialog.vector_similarity_weight,
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doc_ids=attachments,
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top=dialog.top_k,
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aggs=False,
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rerank_mdl=rerank_mdl,
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rank_feature=label_question(" ".join(questions), kbs),
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)
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if prompt_config.get("tavily_api_key"):
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tav = Tavily(prompt_config["tavily_api_key"])
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tav_res = tav.retrieve_chunks(" ".join(questions))
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kbinfos["chunks"].extend(tav_res["chunks"])
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kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
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if prompt_config.get("use_kg"):
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ck = settings.kg_retrievaler.retrieval(" ".join(questions),
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tenant_ids,
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dialog.kb_ids,
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embd_mdl,
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LLMBundle(dialog.tenant_id, LLMType.CHAT))
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ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl, LLMBundle(dialog.tenant_id, LLMType.CHAT))
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if ck["content_with_weight"]:
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kbinfos["chunks"].insert(0, ck)
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knowledges = kb_prompt(kbinfos, max_tokens)
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logging.debug(
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"{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
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logging.debug("{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
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retrieval_ts = timer()
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if not knowledges and prompt_config.get("empty_response"):
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@ -239,16 +251,13 @@ def chat(dialog, messages, stream=True, **kwargs):
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prompt4citation = ""
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if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
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prompt4citation = citation_prompt()
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msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
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for m in messages if m["role"] != "system"])
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msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"])
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used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.95))
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assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
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prompt = msg[0]["content"]
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if "max_tokens" in gen_conf:
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gen_conf["max_tokens"] = min(
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gen_conf["max_tokens"],
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max_tokens - used_token_count)
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gen_conf["max_tokens"] = min(gen_conf["max_tokens"], max_tokens - used_token_count)
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def decorate_answer(answer):
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nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions
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@ -262,14 +271,14 @@ def chat(dialog, messages, stream=True, **kwargs):
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if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
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answer = re.sub(r"##[ij]\$\$", "", answer, flags=re.DOTALL)
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if not re.search(r"##[0-9]+\$\$", answer):
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answer, idx = retriever.insert_citations(answer,
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[ck["content_ltks"]
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for ck in kbinfos["chunks"]],
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[ck["vector"]
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for ck in kbinfos["chunks"]],
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embd_mdl,
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tkweight=1 - dialog.vector_similarity_weight,
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vtweight=dialog.vector_similarity_weight)
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answer, idx = retriever.insert_citations(
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answer,
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[ck["content_ltks"] for ck in kbinfos["chunks"]],
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[ck["vector"] for ck in kbinfos["chunks"]],
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embd_mdl,
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tkweight=1 - dialog.vector_similarity_weight,
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vtweight=dialog.vector_similarity_weight,
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)
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else:
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idx = set([])
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for r in re.finditer(r"##([0-9]+)\$\$", answer):
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@ -278,8 +287,7 @@ def chat(dialog, messages, stream=True, **kwargs):
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idx.add(i)
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idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
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recall_docs = [
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d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
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recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
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if not recall_docs:
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recall_docs = kbinfos["doc_aggs"]
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kbinfos["doc_aggs"] = recall_docs
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@ -295,7 +303,8 @@ def chat(dialog, messages, stream=True, **kwargs):
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total_time_cost = (finish_chat_ts - chat_start_ts) * 1000
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check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000
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create_retriever_time_cost = (create_retriever_ts - check_llm_ts) * 1000
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check_langfuse_tracer_cost = (check_langfuse_tracer_ts - check_llm_ts) * 1000
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create_retriever_time_cost = (create_retriever_ts - check_langfuse_tracer_ts) * 1000
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bind_embedding_time_cost = (bind_embedding_ts - create_retriever_ts) * 1000
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bind_llm_time_cost = (bind_llm_ts - bind_embedding_ts) * 1000
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refine_question_time_cost = (refine_question_ts - bind_llm_ts) * 1000
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@ -304,45 +313,54 @@ def chat(dialog, messages, stream=True, **kwargs):
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retrieval_time_cost = (retrieval_ts - generate_keyword_ts) * 1000
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generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000
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tk_num = num_tokens_from_string(think+answer)
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tk_num = num_tokens_from_string(think + answer)
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prompt += "\n\n### Query:\n%s" % " ".join(questions)
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prompt = (
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f"{prompt}\n\n"
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"## Time elapsed:\n"
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f" - Total: {total_time_cost:.1f}ms\n"
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f" - Check LLM: {check_llm_time_cost:.1f}ms\n"
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f" - Create retriever: {create_retriever_time_cost:.1f}ms\n"
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f" - Bind embedding: {bind_embedding_time_cost:.1f}ms\n"
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f" - Bind LLM: {bind_llm_time_cost:.1f}ms\n"
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f" - Tune question: {refine_question_time_cost:.1f}ms\n"
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f" - Bind reranker: {bind_reranker_time_cost:.1f}ms\n"
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f" - Generate keyword: {generate_keyword_time_cost:.1f}ms\n"
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f" - Retrieval: {retrieval_time_cost:.1f}ms\n"
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f" - Generate answer: {generate_result_time_cost:.1f}ms\n\n"
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"## Token usage:\n"
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f" - Generated tokens(approximately): {tk_num}\n"
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f" - Token speed: {int(tk_num/(generate_result_time_cost/1000.))}/s"
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f"{prompt}\n\n"
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"## Time elapsed:\n"
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f" - Total: {total_time_cost:.1f}ms\n"
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f" - Check LLM: {check_llm_time_cost:.1f}ms\n"
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f" - Check Langfuse tracer: {check_langfuse_tracer_cost:.1f}ms\n"
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f" - Create retriever: {create_retriever_time_cost:.1f}ms\n"
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f" - Bind embedding: {bind_embedding_time_cost:.1f}ms\n"
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f" - Bind LLM: {bind_llm_time_cost:.1f}ms\n"
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f" - Tune question: {refine_question_time_cost:.1f}ms\n"
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f" - Bind reranker: {bind_reranker_time_cost:.1f}ms\n"
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f" - Generate keyword: {generate_keyword_time_cost:.1f}ms\n"
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f" - Retrieval: {retrieval_time_cost:.1f}ms\n"
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f" - Generate answer: {generate_result_time_cost:.1f}ms\n\n"
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"## Token usage:\n"
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f" - Generated tokens(approximately): {tk_num}\n"
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f" - Token speed: {int(tk_num / (generate_result_time_cost / 1000.0))}/s"
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)
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return {"answer": think+answer, "reference": refs, "prompt": re.sub(r"\n", " \n", prompt), "created_at": time.time()}
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langfuse_output = "\n" + re.sub(r"^.*?(### Query:.*)", r"\1", prompt, flags=re.DOTALL)
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langfuse_output = {"time_elapsed:": re.sub(r"\n", " \n", langfuse_output), "created_at": time.time()}
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langfuse_generation.end(output=langfuse_output)
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return {"answer": think + answer, "reference": refs, "prompt": re.sub(r"\n", " \n", prompt), "created_at": time.time()}
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if langfuse_tracer:
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langfuse_generation = langfuse_tracer.trace.generation(name="chat", model=llm_model_config["llm_name"], input={"prompt": prompt, "prompt4citation": prompt4citation, "messages": msg})
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if stream:
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last_ans = ""
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answer = ""
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for ans in chat_mdl.chat_streamly(prompt+prompt4citation, msg[1:], gen_conf):
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for ans in chat_mdl.chat_streamly(prompt + prompt4citation, msg[1:], gen_conf):
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if thought:
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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answer = ans
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delta_ans = ans[len(last_ans):]
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delta_ans = ans[len(last_ans) :]
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if num_tokens_from_string(delta_ans) < 16:
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continue
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last_ans = answer
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yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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delta_ans = answer[len(last_ans):]
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yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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delta_ans = answer[len(last_ans) :]
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if delta_ans:
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yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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yield decorate_answer(thought+answer)
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yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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yield decorate_answer(thought + answer)
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else:
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answer = chat_mdl.chat(prompt+prompt4citation, msg[1:], gen_conf)
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answer = chat_mdl.chat(prompt + prompt4citation, msg[1:], gen_conf)
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user_content = msg[-1].get("content", "[content not available]")
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logging.debug("User: {}|Assistant: {}".format(user_content, answer))
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res = decorate_answer(answer)
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@ -360,27 +378,22 @@ Table of database fields are as follows:
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Question are as follows:
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{}
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Please write the SQL, only SQL, without any other explanations or text.
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""".format(
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index_name(tenant_id),
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"\n".join([f"{k}: {v}" for k, v in field_map.items()]),
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question
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)
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""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question)
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tried_times = 0
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def get_table():
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nonlocal sys_prompt, user_prompt, question, tried_times
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sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {
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"temperature": 0.06})
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sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {"temperature": 0.06})
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sql = re.sub(r"<think>.*</think>", "", sql, flags=re.DOTALL)
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logging.debug(f"{question} ==> {user_prompt} get SQL: {sql}")
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sql = re.sub(r"[\r\n]+", " ", sql.lower())
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sql = re.sub(r".*select ", "select ", sql.lower())
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sql = re.sub(r" +", " ", sql)
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sql = re.sub(r"([;；]|```).*", "", sql)
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if sql[:len("select ")] != "select ":
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if sql[: len("select ")] != "select ":
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return None, None
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if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
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if sql[:len("select *")] != "select *":
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if sql[: len("select *")] != "select *":
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sql = "select doc_id,docnm_kwd," + sql[6:]
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else:
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flds = []
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@ -417,11 +430,7 @@ Please write the SQL, only SQL, without any other explanations or text.
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{}
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|
||||
Please correct the error and write SQL again, only SQL, without any other explanations or text.
|
||||
""".format(
|
||||
index_name(tenant_id),
|
||||
"\n".join([f"{k}: {v}" for k, v in field_map.items()]),
|
||||
question, sql, tbl["error"]
|
||||
)
|
||||
""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"])
|
||||
tbl, sql = get_table()
|
||||
logging.debug("TRY it again: {}".format(sql))
|
||||
|
||||
@ -429,24 +438,18 @@ Please write the SQL, only SQL, without any other explanations or text.
|
||||
if tbl.get("error") or len(tbl["rows"]) == 0:
|
||||
return None
|
||||
|
||||
docid_idx = set([ii for ii, c in enumerate(
|
||||
tbl["columns"]) if c["name"] == "doc_id"])
|
||||
doc_name_idx = set([ii for ii, c in enumerate(
|
||||
tbl["columns"]) if c["name"] == "docnm_kwd"])
|
||||
column_idx = [ii for ii in range(
|
||||
len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
|
||||
docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"])
|
||||
doc_name_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "docnm_kwd"])
|
||||
column_idx = [ii for ii in range(len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
|
||||
|
||||
# compose Markdown table
|
||||
columns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
|
||||
tbl["columns"][i]["name"])) for i in
|
||||
column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
|
||||
columns = (
|
||||
"|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
|
||||
)
|
||||
|
||||
line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + \
|
||||
("|------|" if docid_idx and docid_idx else "")
|
||||
line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + ("|------|" if docid_idx and docid_idx else "")
|
||||
|
||||
rows = ["|" +
|
||||
"|".join([rmSpace(str(r[i])) for i in column_idx]).replace("None", " ") +
|
||||
"|" for r in tbl["rows"]]
|
||||
rows = ["|" + "|".join([rmSpace(str(r[i])) for i in column_idx]).replace("None", " ") + "|" for r in tbl["rows"]]
|
||||
rows = [r for r in rows if re.sub(r"[ |]+", "", r)]
|
||||
if quota:
|
||||
rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
|
||||
@ -456,11 +459,7 @@ Please write the SQL, only SQL, without any other explanations or text.
|
||||
|
||||
if not docid_idx or not doc_name_idx:
|
||||
logging.warning("SQL missing field: " + sql)
|
||||
return {
|
||||
"answer": "\n".join([columns, line, rows]),
|
||||
"reference": {"chunks": [], "doc_aggs": []},
|
||||
"prompt": sys_prompt
|
||||
}
|
||||
return {"answer": "\n".join([columns, line, rows]), "reference": {"chunks": [], "doc_aggs": []}, "prompt": sys_prompt}
|
||||
|
||||
docid_idx = list(docid_idx)[0]
|
||||
doc_name_idx = list(doc_name_idx)[0]
|
||||
@ -471,10 +470,11 @@ Please write the SQL, only SQL, without any other explanations or text.
|
||||
doc_aggs[r[docid_idx]]["count"] += 1
|
||||
return {
|
||||
"answer": "\n".join([columns, line, rows]),
|
||||
"reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]],
|
||||
"doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in
|
||||
doc_aggs.items()]},
|
||||
"prompt": sys_prompt
|
||||
"reference": {
|
||||
"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]],
|
||||
"doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()],
|
||||
},
|
||||
"prompt": sys_prompt,
|
||||
}
|
||||
|
||||
|
||||
@ -498,10 +498,7 @@ def ask(question, kb_ids, tenant_id):
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
|
||||
max_tokens = chat_mdl.max_length
|
||||
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
|
||||
kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids,
|
||||
1, 12, 0.1, 0.3, aggs=False,
|
||||
rank_feature=label_question(question, kbs)
|
||||
)
|
||||
kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False, rank_feature=label_question(question, kbs))
|
||||
knowledges = kb_prompt(kbinfos, max_tokens)
|
||||
prompt = """
|
||||
Role: You're a smart assistant. Your name is Miss R.
|
||||
@ -523,17 +520,9 @@ def ask(question, kb_ids, tenant_id):
|
||||
|
||||
def decorate_answer(answer):
|
||||
nonlocal knowledges, kbinfos, prompt
|
||||
answer, idx = retriever.insert_citations(answer,
|
||||
[ck["content_ltks"]
|
||||
for ck in kbinfos["chunks"]],
|
||||
[ck["vector"]
|
||||
for ck in kbinfos["chunks"]],
|
||||
embd_mdl,
|
||||
tkweight=0.7,
|
||||
vtweight=0.3)
|
||||
answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3)
|
||||
idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
|
||||
recall_docs = [
|
||||
d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
|
||||
recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
|
||||
if not recall_docs:
|
||||
recall_docs = kbinfos["doc_aggs"]
|
||||
kbinfos["doc_aggs"] = recall_docs
|
||||
|
||||
Reference in New Issue
Block a user