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add elapsed time of conversation (#2316)

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### What problem does this PR solve?

#2315

### Type of change


- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu 2024-09-09 12:08:50 +08:00 committed by GitHub
parent 63da2cb7d5
commit 9f7d187ab3
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GPG Key ID: B5690EEEBB952194
1 changed files with 80 additions and 15 deletions
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95
api/db/services/dialog_service.py
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@ -18,7 +18,7 @@ import os
import json
import re
from copy import deepcopy
from timeit import default_timer as timer
from api.db import LLMType, ParserType
from api.db.db_models import Dialog, Conversation
from api.db.services.common_service import CommonService
@ -88,6 +88,7 @@ def llm_id2llm_type(llm_id):
def chat(dialog, messages, stream=True, **kwargs):
assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
st = timer()
llm = LLMService.query(llm_name=dialog.llm_id)
if not llm:
llm = TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=dialog.llm_id)
@ -158,25 +159,16 @@ def chat(dialog, messages, stream=True, **kwargs):
doc_ids=attachments,
top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
#self-rag
if dialog.prompt_config.get("self_rag") and not relevant(dialog.tenant_id, dialog.llm_id, questions[-1], knowledges):
questions[-1] = rewrite(dialog.tenant_id, dialog.llm_id, questions[-1])
kbinfos = retr.retrieval(" ".join(questions), embd_mdl, dialog.tenant_id, dialog.kb_ids, 1, dialog.top_n,
dialog.similarity_threshold,
dialog.vector_similarity_weight,
doc_ids=attachments,
top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
chat_logger.info(
"{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
retrieval_tm = timer()
if not knowledges and prompt_config.get("empty_response"):
empty_res = prompt_config["empty_response"]
yield {"answer": empty_res, "reference": kbinfos, "audio_binary": tts(tts_mdl, empty_res)}
return {"answer": prompt_config["empty_response"], "reference": kbinfos}
kwargs["knowledge"] = "\n".join(knowledges)
kwargs["knowledge"] = "\n------\n".join(knowledges)
gen_conf = dialog.llm_setting
msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
@ -192,7 +184,7 @@ def chat(dialog, messages, stream=True, **kwargs):
max_tokens - used_token_count)
def decorate_answer(answer):
nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt
nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_tm
refs = []
if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
answer, idx = retr.insert_citations(answer,
@ -216,7 +208,9 @@ def chat(dialog, messages, stream=True, **kwargs):
if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
return {"answer": answer, "reference": refs, "prompt": prompt}
done_tm = timer()
prompt += "\n### Elapsed\n - Retrieval: %.1f ms\n - LLM: %.1f ms"%((retrieval_tm-st)*1000, (done_tm-st)*1000)
return {"answer": answer, "reference": refs, "prompt": re.sub(r"\n", "<br/>", prompt)}
if stream:
last_ans = ""
@ -415,4 +409,75 @@ def tts(tts_mdl, text):
bin = b""
for chunk in tts_mdl.tts(text):
bin += chunk
return binascii.hexlify(bin).decode("utf-8")
return binascii.hexlify(bin).decode("utf-8")
def ask(question, kb_ids, tenant_id):
kbs = KnowledgebaseService.get_by_ids(kb_ids)
embd_nms = list(set([kb.embd_id for kb in kbs]))
is_kg = all([kb.parser_id == ParserType.KG for kb in kbs])
retr = retrievaler if not is_kg else kg_retrievaler
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_nms[0])
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
max_tokens = chat_mdl.max_length
kbinfos = retr.retrieval(question, embd_mdl, tenant_id, kb_ids, 1, 12, 0.1, 0.3, aggs=False)
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
used_token_count = 0
for i, c in enumerate(knowledges):
used_token_count += num_tokens_from_string(c)
if max_tokens * 0.97 < used_token_count:
knowledges = knowledges[:i]
break
prompt = """
Role: You're a smart assistant. Your name is Miss R.
Task: Summarize the information from knowledge bases and answer user's question.
Requirements and restriction:
- DO NOT make things up, especially for numbers.
- If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided.
- Answer with markdown format text.
- Answer in language of user's question.
- DO NOT make things up, especially for numbers.
### Information from knowledge bases
%s
The above is information from knowledge bases.
"""%"\n".join(knowledges)
msg = [{"role": "user", "content": question}]
def decorate_answer(answer):
nonlocal knowledges, kbinfos, prompt
answer, idx = retr.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]
if not recall_docs: recall_docs = kbinfos["doc_aggs"]
kbinfos["doc_aggs"] = recall_docs
refs = deepcopy(kbinfos)
for c in refs["chunks"]:
if c.get("vector"):
del c["vector"]
if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
return {"answer": answer, "reference": refs}
answer = ""
for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
answer = ans
yield {"answer": answer, "reference": {}}
yield decorate_answer(answer)