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Code Issues Packages Projects Releases Wiki Activity

Support iframe chatbot. (#3961)

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### What problem does this PR solve?

#3909

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
2024-12-10 17:03:24 +08:00
committed by GitHub
parent 601d74160b
commit e9b8c30a38
9 changed files with 173 additions and 141 deletions
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119
api/db/services/dialog_service.py
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@ -18,6 +18,7 @@ import binascii
import os
import json
import re
from collections import defaultdict
from copy import deepcopy
from timeit import default_timer as timer
import datetime
@ -108,6 +109,32 @@ def llm_id2llm_type(llm_id):
return llm["model_type"].strip(",")[-1]
def kb_prompt(kbinfos, max_tokens):
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
used_token_count = 0
chunks_num = 0
for i, c in enumerate(knowledges):
used_token_count += num_tokens_from_string(c)
chunks_num += 1
if max_tokens * 0.97 < used_token_count:
knowledges = knowledges[:i]
break
doc2chunks = defaultdict(list)
for i, ck in enumerate(kbinfos["chunks"]):
if i >= chunks_num:
break
doc2chunks["docnm_kwd"].append(ck["content_with_weight"])
knowledges = []
for nm, chunks in doc2chunks.items():
txt = f"Document: {nm} \nContains the following relevant fragments:\n"
for i, chunk in enumerate(chunks, 1):
txt += f"{i}. {chunk}\n"
knowledges.append(txt)
return knowledges
def chat(dialog, messages, stream=True, **kwargs):
assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
st = timer()
@ -195,32 +222,7 @@ def chat(dialog, messages, stream=True, **kwargs):
dialog.vector_similarity_weight,
doc_ids=attachments,
top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl)
# Group chunks by document ID
doc_chunks = {}
for ck in kbinfos["chunks"]:
doc_id = ck["doc_id"]
if doc_id not in doc_chunks:
doc_chunks[doc_id] = []
doc_chunks[doc_id].append(ck["content_with_weight"])
# Create knowledges list with grouped chunks
knowledges = []
for doc_id, chunks in doc_chunks.items():
# Find the corresponding document name
doc_name = next((d["doc_name"] for d in kbinfos.get("doc_aggs", []) if d["doc_id"] == doc_id), doc_id)
# Create a header for the document
doc_knowledge = f"Document: {doc_name} \nContains the following relevant fragments:\n"
# Add numbered fragments
for i, chunk in enumerate(chunks, 1):
doc_knowledge += f"{i}. {chunk}\n"
knowledges.append(doc_knowledge)
knowledges = kb_prompt(kbinfos, max_tokens)
logging.debug(
"{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
retrieval_tm = timer()
@ -603,7 +605,6 @@ def tts(tts_mdl, text):
def ask(question, kb_ids, tenant_id):
kbs = KnowledgebaseService.get_by_ids(kb_ids)
tenant_ids = [kb.tenant_id for kb in kbs]
embd_nms = list(set([kb.embd_id for kb in kbs]))
is_kg = all([kb.parser_id == ParserType.KG for kb in kbs])
@ -612,45 +613,9 @@ def ask(question, kb_ids, tenant_id):
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_nms[0])
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 = retr.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False)
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
used_token_count = 0
chunks_num = 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]
chunks_num = chunks_num + 1
break
# Group chunks by document ID
doc_chunks = {}
counter_chunks = 0
for ck in kbinfos["chunks"]:
if counter_chunks < chunks_num:
counter_chunks = counter_chunks + 1
doc_id = ck["doc_id"]
if doc_id not in doc_chunks:
doc_chunks[doc_id] = []
doc_chunks[doc_id].append(ck["content_with_weight"])
# Create knowledges list with grouped chunks
knowledges = []
for doc_id, chunks in doc_chunks.items():
# Find the corresponding document name
doc_name = next((d["doc_name"] for d in kbinfos.get("doc_aggs", []) if d["doc_id"] == doc_id), doc_id)
# Create a header for the document
doc_knowledge = f"Document: {doc_name} \nContains the following relevant fragments:\n"
# Add numbered fragments
for i, chunk in enumerate(chunks, 1):
doc_knowledge += f"{i}. {chunk}\n"
knowledges.append(doc_knowledge)
knowledges = kb_prompt(kbinfos, max_tokens)
prompt = """
Role: You're a smart assistant. Your name is Miss R.
Task: Summarize the information from knowledge bases and answer user's question.
@ -660,25 +625,25 @@ def ask(question, kb_ids, tenant_id):
- 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)
""" % "\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)
[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]
@ -691,7 +656,7 @@ def ask(question, kb_ids, tenant_id):
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'"
answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
return {"answer": answer, "reference": refs}
answer = ""