mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
更新0.2.x Agent，之后的Agent在0.3.x更新
This commit is contained in:
zR
parent
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269090ea66
@ -7,31 +7,35 @@
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保存的模型的位置位于原本嵌入模型的目录下，模型的名称为原模型名称+Merge_Keywords_时间戳
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'''
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import sys
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sys.path.append("..")
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import os
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import torch
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from datetime import datetime
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from configs import (
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MODEL_PATH,
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EMBEDDING_MODEL,
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EMBEDDING_KEYWORD_FILE,
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)
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import os
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import torch
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from safetensors.torch import save_model
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from sentence_transformers import SentenceTransformer
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from langchain_core._api import deprecated
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@deprecated(
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since="0.3.0",
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message="自定义关键词 Langchain-Chatchat 0.3.x 重写, 0.2.x中相关功能将废弃",
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removal="0.3.0"
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)
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def get_keyword_embedding(bert_model, tokenizer, key_words):
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tokenizer_output = tokenizer(key_words, return_tensors="pt", padding=True, truncation=True)
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# No need to manually convert to tensor as we've set return_tensors="pt"
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input_ids = tokenizer_output['input_ids']
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# Remove the first and last token for each sequence in the batch
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input_ids = input_ids[:, 1:-1]
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keyword_embedding = bert_model.embeddings.word_embeddings(input_ids)
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keyword_embedding = torch.mean(keyword_embedding, 1)
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return keyword_embedding
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@ -47,14 +51,11 @@ def add_keyword_to_model(model_name=EMBEDDING_MODEL, keyword_file: str = "", out
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bert_model = word_embedding_model.auto_model
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tokenizer = word_embedding_model.tokenizer
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key_words_embedding = get_keyword_embedding(bert_model, tokenizer, key_words)
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# key_words_embedding = st_model.encode(key_words)
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embedding_weight = bert_model.embeddings.word_embeddings.weight
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embedding_weight_len = len(embedding_weight)
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tokenizer.add_tokens(key_words)
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bert_model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=32)
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# key_words_embedding_tensor = torch.from_numpy(key_words_embedding)
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embedding_weight = bert_model.embeddings.word_embeddings.weight
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with torch.no_grad():
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embedding_weight[embedding_weight_len:embedding_weight_len + key_words_len, :] = key_words_embedding
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@ -76,46 +77,3 @@ def add_keyword_to_embedding_model(path: str = EMBEDDING_KEYWORD_FILE):
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output_model_name = "{}_Merge_Keywords_{}".format(EMBEDDING_MODEL, current_time)
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output_model_path = os.path.join(model_parent_directory, output_model_name)
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add_keyword_to_model(model_name, keyword_file, output_model_path)
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if __name__ == '__main__':
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add_keyword_to_embedding_model(EMBEDDING_KEYWORD_FILE)
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# input_model_name = ""
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# output_model_path = ""
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# # 以下为加入关键字前后tokenizer的测试用例对比
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# def print_token_ids(output, tokenizer, sentences):
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# for idx, ids in enumerate(output['input_ids']):
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# print(f'sentence={sentences[idx]}')
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# print(f'ids={ids}')
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# for id in ids:
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# decoded_id = tokenizer.decode(id)
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# print(f' {decoded_id}->{id}')
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#
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# sentences = [
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# '数据科学与大数据技术',
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# 'Langchain-Chatchat'
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# ]
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#
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# st_no_keywords = SentenceTransformer(input_model_name)
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# tokenizer_without_keywords = st_no_keywords.tokenizer
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# print("===== tokenizer with no keywords added =====")
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# output = tokenizer_without_keywords(sentences)
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# print_token_ids(output, tokenizer_without_keywords, sentences)
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# print(f'-------- embedding with no keywords added -----')
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# embeddings = st_no_keywords.encode(sentences)
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# print(embeddings)
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#
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# print("--------------------------------------------")
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# print("--------------------------------------------")
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# print("--------------------------------------------")
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#
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# st_with_keywords = SentenceTransformer(output_model_path)
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# tokenizer_with_keywords = st_with_keywords.tokenizer
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# print("===== tokenizer with keyword added =====")
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# output = tokenizer_with_keywords(sentences)
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# print_token_ids(output, tokenizer_with_keywords, sentences)
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#
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# print(f'-------- embedding with keywords added -----')
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# embeddings = st_with_keywords.encode(sentences)
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# print(embeddings)
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@ -38,13 +38,11 @@ numpy~=1.24.4
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pandas~=2.0.3
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einops>=0.7.0
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transformers_stream_generator==0.0.4
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vllm==0.2.6; sys_platform == "linux"
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httpx[brotli,http2,socks]==0.25.2
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llama-index
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vllm==0.2.7; sys_platform == "linux"
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# optional document loaders
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# rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
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#rapidocr_paddle[gpu]>=1.3.0.post5 # gpu accelleration for ocr of pdf and image files
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jq==1.6.0 # for .json and .jsonl files. suggest `conda install jq` on windows
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beautifulsoup4~=4.12.2 # for .mhtml files
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pysrt~=1.1.2
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@ -69,9 +67,11 @@ metaphor-python~=0.1.23
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# WebUI requirements
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streamlit~=1.29.0
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streamlit-option-menu>=0.3.6
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streamlit==1.30.0
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streamlit-option-menu==0.3.6
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streamlit-antd-components==0.3.1
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streamlit-chatbox==1.1.11
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streamlit-modal>=0.1.0
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streamlit-aggrid>=0.3.4.post3
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watchdog>=3.0.0
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streamlit-modal==0.1.0
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streamlit-aggrid==0.3.4.post3
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httpx==0.26.0
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watchdog==3.0.0
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@ -36,8 +36,8 @@ numpy~=1.24.4
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pandas~=2.0.3
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einops>=0.7.0
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transformers_stream_generator==0.0.4
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vllm==0.2.6; sys_platform == "linux"
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httpx[brotli,http2,socks]==0.25.2
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vllm==0.2.7; sys_platform == "linux"
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httpx==0.26.0
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llama-index
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# optional document loaders
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@ -27,7 +27,7 @@ numpy~=1.24.4
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pandas~=2.0.3
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einops>=0.7.0
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transformers_stream_generator==0.0.4
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vllm==0.2.6; sys_platform == "linux"
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vllm==0.2.7; sys_platform == "linux"
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httpx[brotli,http2,socks]==0.25.2
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requests
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pathlib
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@ -54,11 +54,11 @@ metaphor-python~=0.1.23
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# WebUI requirements
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streamlit>=1.29.0
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streamlit-option-menu>=0.3.6
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streamlit-antd-components>=0.3.0
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streamlit-chatbox>=1.1.11
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streamlit-modal>=0.1.0
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streamlit-aggrid>=0.3.4.post3
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httpx[brotli,http2,socks]>=0.25.2
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watchdog>=3.0.0
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streamlit==1.30.0
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streamlit-option-menu==0.3.6
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streamlit-antd-components==0.3.1
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streamlit-chatbox==1.1.11
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streamlit-modal==0.1.0
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streamlit-aggrid==0.3.4.post3
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httpx==0.26.0
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watchdog==3.0.0
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@ -1,10 +1,10 @@
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# WebUI requirements
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streamlit>=1.29.0
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streamlit-option-menu>=0.3.6
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streamlit-antd-components>=0.3.0
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streamlit-chatbox>=1.1.11
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streamlit-modal>=0.1.0
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streamlit-aggrid>=0.3.4.post3
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httpx[brotli,http2,socks]>=0.25.2
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watchdog>=3.0.0
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streamlit==1.30.0
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streamlit-option-menu==0.3.6
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streamlit-antd-components==0.3.1
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streamlit-chatbox==1.1.11
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streamlit-modal==0.1.0
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streamlit-aggrid==0.3.4.post3
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httpx==0.26.0
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watchdog==3.0.0
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@ -1,22 +1,19 @@
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"""
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This file is a modified version for ChatGLM3-6B the original ChatGLM3Agent.py file from the langchain repo.
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This file is a modified version for ChatGLM3-6B the original glm3_agent.py file from the langchain repo.
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"""
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from __future__ import annotations
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import yaml
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from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
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from langchain.memory import ConversationBufferWindowMemory
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from typing import Any, List, Sequence, Tuple, Optional, Union
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import os
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from langchain.agents.agent import Agent
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from langchain.chains.llm import LLMChain
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate, MessagesPlaceholder,
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)
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import json
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import logging
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from typing import Any, List, Sequence, Tuple, Optional, Union
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from pydantic.schema import model_schema
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from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
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from langchain.memory import ConversationBufferWindowMemory
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from langchain.agents.agent import Agent
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from langchain.chains.llm import LLMChain
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from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
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from langchain.agents.agent import AgentOutputParser
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from langchain.output_parsers import OutputFixingParser
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from langchain.pydantic_v1 import Field
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@ -43,12 +40,18 @@ class StructuredChatOutputParserWithRetries(AgentOutputParser):
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first_index = min([text.find(token) if token in text else len(text) for token in special_tokens])
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text = text[:first_index]
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if "tool_call" in text:
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tool_name_end = text.find("```")
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tool_name = text[:tool_name_end].strip()
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input_para = text.split("='")[-1].split("'")[0]
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action_end = text.find("```")
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action = text[:action_end].strip()
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params_str_start = text.find("(") + 1
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params_str_end = text.rfind(")")
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params_str = text[params_str_start:params_str_end]
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params_pairs = [param.split("=") for param in params_str.split(",") if "=" in param]
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params = {pair[0].strip(): pair[1].strip().strip("'\"") for pair in params_pairs}
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action_json = {
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"action": tool_name,
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"action_input": input_para
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"action": action,
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"action_input": params
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}
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else:
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action_json = {
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@ -109,10 +112,6 @@ class StructuredGLM3ChatAgent(Agent):
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else:
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return agent_scratchpad
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@classmethod
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def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
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pass
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@classmethod
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def _get_default_output_parser(
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cls, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
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@ -121,7 +120,7 @@ class StructuredGLM3ChatAgent(Agent):
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@property
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def _stop(self) -> List[str]:
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return ["```<observation>"]
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return ["<|observation|>"]
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@classmethod
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def create_prompt(
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@ -131,44 +130,25 @@ class StructuredGLM3ChatAgent(Agent):
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input_variables: Optional[List[str]] = None,
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memory_prompts: Optional[List[BasePromptTemplate]] = None,
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) -> BasePromptTemplate:
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def tool_config_from_file(tool_name, directory="server/agent/tools/"):
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"""search tool yaml and return simplified json format"""
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file_path = os.path.join(directory, f"{tool_name.lower()}.yaml")
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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tool_config = yaml.safe_load(file)
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# Simplify the structure if needed
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simplified_config = {
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"name": tool_config.get("name", ""),
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"description": tool_config.get("description", ""),
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"parameters": tool_config.get("parameters", {})
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}
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return simplified_config
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except FileNotFoundError:
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logger.error(f"File not found: {file_path}")
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return None
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except Exception as e:
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logger.error(f"An error occurred while reading {file_path}: {e}")
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return None
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tools_json = []
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tool_names = []
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for tool in tools:
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tool_config = tool_config_from_file(tool.name)
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if tool_config:
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tools_json.append(tool_config)
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tool_names.append(tool.name)
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# Format the tools for output
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tool_schema = model_schema(tool.args_schema) if tool.args_schema else {}
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simplified_config_langchain = {
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"name": tool.name,
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"description": tool.description,
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"parameters": tool_schema.get("properties", {})
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}
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tools_json.append(simplified_config_langchain)
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tool_names.append(tool.name)
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formatted_tools = "\n".join([
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f"{tool['name']}: {tool['description']}, args: {tool['parameters']}"
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for tool in tools_json
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])
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formatted_tools = formatted_tools.replace("'", "\\'").replace("{", "{{").replace("}", "}}")
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template = prompt.format(tool_names=tool_names,
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tools=formatted_tools,
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history="{history}",
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history="None",
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input="{input}",
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agent_scratchpad="{agent_scratchpad}")
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@ -225,7 +205,6 @@ def initialize_glm3_agent(
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tools: Sequence[BaseTool],
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llm: BaseLanguageModel,
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prompt: str = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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memory: Optional[ConversationBufferWindowMemory] = None,
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agent_kwargs: Optional[dict] = None,
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*,
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@ -238,14 +217,12 @@ def initialize_glm3_agent(
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llm=llm,
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tools=tools,
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prompt=prompt,
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callback_manager=callback_manager, **agent_kwargs
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**agent_kwargs
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)
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return AgentExecutor.from_agent_and_tools(
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agent=agent_obj,
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tools=tools,
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callback_manager=callback_manager,
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memory=memory,
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tags=tags_,
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**kwargs,
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)
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)
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@ -1,5 +1,3 @@
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## 由于工具类无法传参，所以使用全局变量来传递模型和对应的知识库介绍
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class ModelContainer:
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def __init__(self):
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self.MODEL = None
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@ -3,7 +3,7 @@ from .search_knowledgebase_simple import search_knowledgebase_simple
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from .search_knowledgebase_once import search_knowledgebase_once, KnowledgeSearchInput
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from .search_knowledgebase_complex import search_knowledgebase_complex, KnowledgeSearchInput
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from .calculate import calculate, CalculatorInput
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from .weather_check import weathercheck, WhetherSchema
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from .weather_check import weathercheck, WeatherInput
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from .shell import shell, ShellInput
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from .search_internet import search_internet, SearchInternetInput
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from .wolfram import wolfram, WolframInput
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@ -1,10 +0,0 @@
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name: arxiv
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description: A wrapper around Arxiv.org for searching and retrieving scientific articles in various fields.
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parameters:
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type: object
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properties:
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query:
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type: string
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description: The search query title
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required:
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- query
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@ -1,10 +0,0 @@
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name: calculate
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description: Useful for when you need to answer questions about simple calculations
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parameters:
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type: object
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properties:
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query:
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type: string
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description: The formula to be calculated
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required:
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- query
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@ -1,10 +0,0 @@
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name: search_internet
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description: Use this tool to surf internet and get information
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parameters:
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type: object
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properties:
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query:
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type: string
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description: Query for Internet search
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required:
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- query
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@ -1,10 +0,0 @@
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name: search_knowledgebase_complex
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description: Use this tool to search local knowledgebase and get information
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parameters:
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type: object
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properties:
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query:
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type: string
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description: The query to be searched
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required:
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- query
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@ -1,10 +0,0 @@
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name: search_youtube
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description: Use this tools to search youtube videos
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parameters:
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type: object
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properties:
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query:
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type: string
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description: Query for Videos search
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required:
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- query
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@ -1,10 +0,0 @@
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name: shell
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description: Use Linux Shell to execute Linux commands
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parameters:
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type: object
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properties:
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query:
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type: string
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description: The command to execute
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required:
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- query
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@ -1,338 +1,25 @@
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from __future__ import annotations
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## 单独运行的时候需要添加
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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import re
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import warnings
|
||||
from typing import Dict
|
||||
|
||||
from langchain.callbacks.manager import (
|
||||
AsyncCallbackManagerForChainRun,
|
||||
CallbackManagerForChainRun,
|
||||
)
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.pydantic_v1 import Extra, root_validator
|
||||
from langchain.schema import BasePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
import requests
|
||||
from typing import List, Any, Optional
|
||||
from datetime import datetime
|
||||
from langchain.prompts import PromptTemplate
|
||||
from server.agent import model_container
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
## 使用和风天气API查询天气
|
||||
KEY = "ac880e5a877042809ac7ffdd19d95b0d"
|
||||
# key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
|
||||
|
||||
|
||||
_PROMPT_TEMPLATE = """
|
||||
用户会提出一个关于天气的问题，你的目标是拆分出用户问题中的区，市 并按照我提供的工具回答。
|
||||
例如 用户提出的问题是: 上海浦东未来1小时天气情况？
|
||||
则 提取的市和区是: 上海 浦东
|
||||
如果用户提出的问题是: 上海未来1小时天气情况？
|
||||
则 提取的市和区是: 上海 None
|
||||
请注意以下内容:
|
||||
1. 如果你没有找到区的内容,则一定要使用 None 替代，否则程序无法运行
|
||||
2. 如果用户没有指定市 则直接返回缺少信息
|
||||
|
||||
问题: ${{用户的问题}}
|
||||
|
||||
你的回答格式应该按照下面的内容，请注意，格式内的```text 等标记都必须输出，这是我用来提取答案的标记。
|
||||
```text
|
||||
|
||||
${{拆分的市和区，中间用空格隔开}}
|
||||
```
|
||||
... weathercheck(市 区)...
|
||||
```output
|
||||
|
||||
${{提取后的答案}}
|
||||
```
|
||||
答案: ${{答案}}
|
||||
|
||||
|
||||
|
||||
这是一个例子：
|
||||
问题: 上海浦东未来1小时天气情况？
|
||||
|
||||
|
||||
```text
|
||||
上海 浦东
|
||||
```
|
||||
...weathercheck(上海 浦东)...
|
||||
|
||||
```output
|
||||
预报时间: 1小时后
|
||||
具体时间: 今天 18:00
|
||||
温度: 24°C
|
||||
天气: 多云
|
||||
风向: 西南风
|
||||
风速: 7级
|
||||
湿度: 88%
|
||||
降水概率: 16%
|
||||
|
||||
Answer: 上海浦东一小时后的天气是多云。
|
||||
|
||||
现在，这是我的问题：
|
||||
|
||||
问题: {question}
|
||||
"""
|
||||
PROMPT = PromptTemplate(
|
||||
input_variables=["question"],
|
||||
template=_PROMPT_TEMPLATE,
|
||||
)
|
||||
更简单的单参数输入工具实现，用于查询现在天气的情况
|
||||
"""
|
||||
from pydantic import BaseModel, Field
|
||||
import requests
|
||||
|
||||
def weather(location: str, api_key: str):
|
||||
url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c"
|
||||
response = requests.get(url)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
weather = {
|
||||
"temperature": data["results"][0]["now"]["temperature"],
|
||||
"description": data["results"][0]["now"]["text"],
|
||||
}
|
||||
return weather
|
||||
else:
|
||||
raise Exception(
|
||||
f"Failed to retrieve weather: {response.status_code}")
|
||||
|
||||
|
||||
def get_city_info(location, adm, key):
|
||||
base_url = 'https://geoapi.qweather.com/v2/city/lookup?'
|
||||
params = {'location': location, 'adm': adm, 'key': key}
|
||||
response = requests.get(base_url, params=params)
|
||||
data = response.json()
|
||||
return data
|
||||
|
||||
|
||||
def format_weather_data(data, place):
|
||||
hourly_forecast = data['hourly']
|
||||
formatted_data = f"\n 这是查询到的关于{place}未来24小时的天气信息: \n"
|
||||
for forecast in hourly_forecast:
|
||||
# 将预报时间转换为datetime对象
|
||||
forecast_time = datetime.strptime(forecast['fxTime'], '%Y-%m-%dT%H:%M%z')
|
||||
# 获取预报时间的时区
|
||||
forecast_tz = forecast_time.tzinfo
|
||||
# 获取当前时间（使用预报时间的时区）
|
||||
now = datetime.now(forecast_tz)
|
||||
# 计算预报日期与当前日期的差值
|
||||
days_diff = (forecast_time.date() - now.date()).days
|
||||
if days_diff == 0:
|
||||
forecast_date_str = '今天'
|
||||
elif days_diff == 1:
|
||||
forecast_date_str = '明天'
|
||||
elif days_diff == 2:
|
||||
forecast_date_str = '后天'
|
||||
else:
|
||||
forecast_date_str = str(days_diff) + '天后'
|
||||
forecast_time_str = forecast_date_str + ' ' + forecast_time.strftime('%H:%M')
|
||||
# 计算预报时间与当前时间的差值
|
||||
time_diff = forecast_time - now
|
||||
# 将差值转换为小时
|
||||
hours_diff = time_diff.total_seconds() // 3600
|
||||
if hours_diff < 1:
|
||||
hours_diff_str = '1小时后'
|
||||
elif hours_diff >= 24:
|
||||
# 如果超过24小时，转换为天数
|
||||
days_diff = hours_diff // 24
|
||||
hours_diff_str = str(int(days_diff)) + '天'
|
||||
else:
|
||||
hours_diff_str = str(int(hours_diff)) + '小时'
|
||||
# 将预报时间和当前时间的差值添加到输出中
|
||||
formatted_data += '预报时间: ' + forecast_time_str + ' 距离现在有: ' + hours_diff_str + '\n'
|
||||
formatted_data += '温度: ' + forecast['temp'] + '°C\n'
|
||||
formatted_data += '天气: ' + forecast['text'] + '\n'
|
||||
formatted_data += '风向: ' + forecast['windDir'] + '\n'
|
||||
formatted_data += '风速: ' + forecast['windSpeed'] + '级\n'
|
||||
formatted_data += '湿度: ' + forecast['humidity'] + '%\n'
|
||||
formatted_data += '降水概率: ' + forecast['pop'] + '%\n'
|
||||
# formatted_data += '降水量: ' + forecast['precip'] + 'mm\n'
|
||||
formatted_data += '\n'
|
||||
return formatted_data
|
||||
|
||||
|
||||
def get_weather(key, location_id, place):
|
||||
url = "https://devapi.qweather.com/v7/weather/24h?"
|
||||
params = {
|
||||
'location': location_id,
|
||||
'key': key,
|
||||
}
|
||||
response = requests.get(url, params=params)
|
||||
data = response.json()
|
||||
return format_weather_data(data, place)
|
||||
|
||||
|
||||
def split_query(query):
|
||||
parts = query.split()
|
||||
adm = parts[0]
|
||||
if len(parts) == 1:
|
||||
return adm, adm
|
||||
location = parts[1] if parts[1] != 'None' else adm
|
||||
return location, adm
|
||||
|
||||
|
||||
def weather(query):
|
||||
location, adm = split_query(query)
|
||||
key = KEY
|
||||
if key == "":
|
||||
return "请先在代码中填入和风天气API Key"
|
||||
try:
|
||||
city_info = get_city_info(location=location, adm=adm, key=key)
|
||||
location_id = city_info['location'][0]['id']
|
||||
place = adm + "市" + location + "区"
|
||||
|
||||
weather_data = get_weather(key=key, location_id=location_id, place=place)
|
||||
return weather_data + "以上是查询到的天气信息，请你查收\n"
|
||||
except KeyError:
|
||||
try:
|
||||
city_info = get_city_info(location=adm, adm=adm, key=key)
|
||||
location_id = city_info['location'][0]['id']
|
||||
place = adm + "市"
|
||||
weather_data = get_weather(key=key, location_id=location_id, place=place)
|
||||
return weather_data + "重要提醒：用户提供的市和区中，区的信息不存在，或者出现错别字，因此该信息是关于市的天气，请你查收\n"
|
||||
except KeyError:
|
||||
return "输入的地区不存在，无法提供天气预报"
|
||||
|
||||
|
||||
class LLMWeatherChain(Chain):
|
||||
llm_chain: LLMChain
|
||||
llm: Optional[BaseLanguageModel] = None
|
||||
"""[Deprecated] LLM wrapper to use."""
|
||||
prompt: BasePromptTemplate = PROMPT
|
||||
"""[Deprecated] Prompt to use to translate to python if necessary."""
|
||||
input_key: str = "question" #: :meta private:
|
||||
output_key: str = "answer" #: :meta private:
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def raise_deprecation(cls, values: Dict) -> Dict:
|
||||
if "llm" in values:
|
||||
warnings.warn(
|
||||
"Directly instantiating an LLMWeatherChain with an llm is deprecated. "
|
||||
"Please instantiate with llm_chain argument or using the from_llm "
|
||||
"class method."
|
||||
)
|
||||
if "llm_chain" not in values and values["llm"] is not None:
|
||||
prompt = values.get("prompt", PROMPT)
|
||||
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
|
||||
return values
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Expect input key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.input_key]
|
||||
|
||||
@property
|
||||
def output_keys(self) -> List[str]:
|
||||
"""Expect output key.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.output_key]
|
||||
|
||||
def _evaluate_expression(self, expression: str) -> str:
|
||||
try:
|
||||
output = weather(expression)
|
||||
except Exception as e:
|
||||
output = "输入的信息有误，请再次尝试"
|
||||
return output
|
||||
|
||||
def _process_llm_result(
|
||||
self, llm_output: str, run_manager: CallbackManagerForChainRun
|
||||
) -> Dict[str, str]:
|
||||
|
||||
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
||||
|
||||
llm_output = llm_output.strip()
|
||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
||||
if text_match:
|
||||
expression = text_match.group(1)
|
||||
output = self._evaluate_expression(expression)
|
||||
run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
||||
run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
||||
answer = "Answer: " + output
|
||||
elif llm_output.startswith("Answer:"):
|
||||
answer = llm_output
|
||||
elif "Answer:" in llm_output:
|
||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
||||
else:
|
||||
return {self.output_key: f"输入的格式不对: {llm_output},应该输入 (市 区)的组合"}
|
||||
return {self.output_key: answer}
|
||||
|
||||
async def _aprocess_llm_result(
|
||||
self,
|
||||
llm_output: str,
|
||||
run_manager: AsyncCallbackManagerForChainRun,
|
||||
) -> Dict[str, str]:
|
||||
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
||||
llm_output = llm_output.strip()
|
||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
||||
|
||||
if text_match:
|
||||
expression = text_match.group(1)
|
||||
output = self._evaluate_expression(expression)
|
||||
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
||||
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
||||
answer = "Answer: " + output
|
||||
elif llm_output.startswith("Answer:"):
|
||||
answer = llm_output
|
||||
elif "Answer:" in llm_output:
|
||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
||||
else:
|
||||
raise ValueError(f"unknown format from LLM: {llm_output}")
|
||||
return {self.output_key: answer}
|
||||
|
||||
def _call(
|
||||
self,
|
||||
inputs: Dict[str, str],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, str]:
|
||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||
_run_manager.on_text(inputs[self.input_key])
|
||||
llm_output = self.llm_chain.predict(
|
||||
question=inputs[self.input_key],
|
||||
stop=["```output"],
|
||||
callbacks=_run_manager.get_child(),
|
||||
)
|
||||
return self._process_llm_result(llm_output, _run_manager)
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
inputs: Dict[str, str],
|
||||
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, str]:
|
||||
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
|
||||
await _run_manager.on_text(inputs[self.input_key])
|
||||
llm_output = await self.llm_chain.apredict(
|
||||
question=inputs[self.input_key],
|
||||
stop=["```output"],
|
||||
callbacks=_run_manager.get_child(),
|
||||
)
|
||||
return await self._aprocess_llm_result(llm_output, _run_manager)
|
||||
|
||||
@property
|
||||
def _chain_type(self) -> str:
|
||||
return "llm_weather_chain"
|
||||
|
||||
@classmethod
|
||||
def from_llm(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
prompt: BasePromptTemplate = PROMPT,
|
||||
**kwargs: Any,
|
||||
) -> LLMWeatherChain:
|
||||
llm_chain = LLMChain(llm=llm, prompt=prompt)
|
||||
return cls(llm_chain=llm_chain, **kwargs)
|
||||
|
||||
|
||||
def weathercheck(query: str):
|
||||
model = model_container.MODEL
|
||||
llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
|
||||
ans = llm_weather.run(query)
|
||||
return ans
|
||||
|
||||
|
||||
class WhetherSchema(BaseModel):
|
||||
location: str = Field(description="应该是一个地区的名称，用空格隔开，例如：上海 浦东，如果没有区的信息，可以只输入上海")
|
||||
|
||||
if __name__ == '__main__':
|
||||
result = weathercheck("苏州姑苏区今晚热不热？")
|
||||
def weathercheck(location: str):
|
||||
return weather(location, "S8vrB4U_-c5mvAMiK")
|
||||
class WeatherInput(BaseModel):
|
||||
location: str = Field(description="City name,include city and county,like '厦门'")
|
||||
|
||||
@ -1,10 +0,0 @@
|
||||
name: weather_check
|
||||
description: Use Weather API to get weather information
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
query:
|
||||
type: string
|
||||
description: City name,include city and county,like "厦门市思明区"
|
||||
required:
|
||||
- query
|
||||
@ -1,10 +0,0 @@
|
||||
name: wolfram
|
||||
description: Useful for when you need to calculate difficult math formulas
|
||||
parameters:
|
||||
type: object
|
||||
properties:
|
||||
query:
|
||||
type: string
|
||||
description: The formula to be calculated
|
||||
required:
|
||||
- query
|
||||
@ -1,8 +1,6 @@
|
||||
from langchain.tools import Tool
|
||||
from server.agent.tools import *
|
||||
|
||||
## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本。
|
||||
|
||||
tools = [
|
||||
Tool.from_function(
|
||||
func=calculate,
|
||||
@ -20,7 +18,7 @@ tools = [
|
||||
func=weathercheck,
|
||||
name="weather_check",
|
||||
description="",
|
||||
args_schema=WhetherSchema,
|
||||
args_schema=WeatherInput,
|
||||
),
|
||||
Tool.from_function(
|
||||
func=shell,
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import Any, Dict, List, Union, Optional
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from langchain.callbacks.base import BaseCallbackHandler
|
||||
from langchain.schema import LLMResult
|
||||
|
||||
@ -1,23 +1,23 @@
|
||||
from langchain.memory import ConversationBufferWindowMemory
|
||||
import json
|
||||
import asyncio
|
||||
|
||||
from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
|
||||
from server.agent.tools_select import tools, tool_names
|
||||
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
|
||||
from langchain.agents import LLMSingleActionAgent, AgentExecutor
|
||||
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
|
||||
from fastapi import Body
|
||||
from sse_starlette.sse import EventSourceResponse
|
||||
from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
|
||||
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
|
||||
from langchain.chains import LLMChain
|
||||
from typing import AsyncIterable, Optional
|
||||
import asyncio
|
||||
from typing import List
|
||||
from server.chat.utils import History
|
||||
import json
|
||||
from server.agent import model_container
|
||||
from server.knowledge_base.kb_service.base import get_kb_details
|
||||
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.memory import ConversationBufferWindowMemory
|
||||
from langchain.agents import LLMSingleActionAgent, AgentExecutor
|
||||
from typing import AsyncIterable, Optional, List
|
||||
|
||||
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
|
||||
from server.knowledge_base.kb_service.base import get_kb_details
|
||||
from server.agent.custom_agent.ChatGLM3Agent import initialize_glm3_agent
|
||||
from server.agent.tools_select import tools, tool_names
|
||||
from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
|
||||
from server.chat.utils import History
|
||||
from server.agent import model_container
|
||||
from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
|
||||
|
||||
async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
|
||||
history: List[History] = Body([],
|
||||
@ -33,7 +33,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
|
||||
prompt_name: str = Body("default",
|
||||
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
|
||||
# top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
|
||||
):
|
||||
history = [History.from_data(h) for h in history]
|
||||
|
||||
@ -55,12 +54,10 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
callbacks=[callback],
|
||||
)
|
||||
|
||||
## 传入全局变量来实现agent调用
|
||||
kb_list = {x["kb_name"]: x for x in get_kb_details()}
|
||||
model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
|
||||
|
||||
if Agent_MODEL:
|
||||
## 如果有指定使用Agent模型来完成任务
|
||||
model_agent = get_ChatOpenAI(
|
||||
model_name=Agent_MODEL,
|
||||
temperature=temperature,
|
||||
@ -79,15 +76,11 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
)
|
||||
output_parser = CustomOutputParser()
|
||||
llm_chain = LLMChain(llm=model, prompt=prompt_template_agent)
|
||||
# 把history转成agent的memory
|
||||
memory = ConversationBufferWindowMemory(k=HISTORY_LEN * 2)
|
||||
for message in history:
|
||||
# 检查消息的角色
|
||||
if message.role == 'user':
|
||||
# 添加用户消息
|
||||
memory.chat_memory.add_user_message(message.content)
|
||||
else:
|
||||
# 添加AI消息
|
||||
memory.chat_memory.add_ai_message(message.content)
|
||||
|
||||
if "chatglm3" in model_container.MODEL.model_name:
|
||||
@ -95,7 +88,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
llm=model,
|
||||
tools=tools,
|
||||
callback_manager=None,
|
||||
# Langchain Prompt is not constructed directly here, it is constructed inside the GLM3 agent.
|
||||
prompt=prompt_template,
|
||||
input_variables=["input", "intermediate_steps", "history"],
|
||||
memory=memory,
|
||||
@ -155,7 +147,6 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
answer = ""
|
||||
final_answer = ""
|
||||
async for chunk in callback.aiter():
|
||||
# Use server-sent-events to stream the response
|
||||
data = json.loads(chunk)
|
||||
if data["status"] == Status.start or data["status"] == Status.complete:
|
||||
continue
|
||||
@ -181,7 +172,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
|
||||
await task
|
||||
|
||||
return EventSourceResponse(agent_chat_iterator(query=query,
|
||||
history=history,
|
||||
model_name=model_name,
|
||||
prompt_name=prompt_name),
|
||||
)
|
||||
history=history,
|
||||
model_name=model_name,
|
||||
prompt_name=prompt_name),
|
||||
)
|
||||
|
||||
@ -1,23 +1,23 @@
|
||||
from langchain.utilities.bing_search import BingSearchAPIWrapper
|
||||
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
|
||||
from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
|
||||
LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE,
|
||||
TEXT_SPLITTER_NAME, OVERLAP_SIZE)
|
||||
from fastapi import Body
|
||||
from sse_starlette import EventSourceResponse
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
from server.utils import wrap_done, get_ChatOpenAI
|
||||
from server.utils import BaseResponse, get_prompt_template
|
||||
LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE)
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.callbacks import AsyncIteratorCallbackHandler
|
||||
from typing import AsyncIterable
|
||||
import asyncio
|
||||
|
||||
from langchain.prompts.chat import ChatPromptTemplate
|
||||
from langchain.text_splitter import RecursiveCharacterTextSplitter
|
||||
from typing import List, Optional, Dict
|
||||
from server.chat.utils import History
|
||||
from langchain.docstore.document import Document
|
||||
from fastapi import Body
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
from sse_starlette import EventSourceResponse
|
||||
from server.utils import wrap_done, get_ChatOpenAI
|
||||
from server.utils import BaseResponse, get_prompt_template
|
||||
from server.chat.utils import History
|
||||
from typing import AsyncIterable
|
||||
import asyncio
|
||||
import json
|
||||
from typing import List, Optional, Dict
|
||||
from strsimpy.normalized_levenshtein import NormalizedLevenshtein
|
||||
from markdownify import markdownify
|
||||
|
||||
@ -38,11 +38,11 @@ def duckduckgo_search(text, result_len=SEARCH_ENGINE_TOP_K, **kwargs):
|
||||
|
||||
|
||||
def metaphor_search(
|
||||
text: str,
|
||||
result_len: int = SEARCH_ENGINE_TOP_K,
|
||||
split_result: bool = False,
|
||||
chunk_size: int = 500,
|
||||
chunk_overlap: int = OVERLAP_SIZE,
|
||||
text: str,
|
||||
result_len: int = SEARCH_ENGINE_TOP_K,
|
||||
split_result: bool = False,
|
||||
chunk_size: int = 500,
|
||||
chunk_overlap: int = OVERLAP_SIZE,
|
||||
) -> List[Dict]:
|
||||
from metaphor_python import Metaphor
|
||||
|
||||
@ -58,13 +58,13 @@ def metaphor_search(
|
||||
# metaphor 返回的内容都是长文本，需要分词再检索
|
||||
if split_result:
|
||||
docs = [Document(page_content=x.extract,
|
||||
metadata={"link": x.url, "title": x.title})
|
||||
metadata={"link": x.url, "title": x.title})
|
||||
for x in contents]
|
||||
text_splitter = RecursiveCharacterTextSplitter(["\n\n", "\n", ".", " "],
|
||||
chunk_size=chunk_size,
|
||||
chunk_overlap=chunk_overlap)
|
||||
splitted_docs = text_splitter.split_documents(docs)
|
||||
|
||||
|
||||
# 将切分好的文档放入临时向量库，重新筛选出TOP_K个文档
|
||||
if len(splitted_docs) > result_len:
|
||||
normal = NormalizedLevenshtein()
|
||||
@ -74,13 +74,13 @@ def metaphor_search(
|
||||
splitted_docs = splitted_docs[:result_len]
|
||||
|
||||
docs = [{"snippet": x.page_content,
|
||||
"link": x.metadata["link"],
|
||||
"title": x.metadata["title"]}
|
||||
"link": x.metadata["link"],
|
||||
"title": x.metadata["title"]}
|
||||
for x in splitted_docs]
|
||||
else:
|
||||
docs = [{"snippet": x.extract,
|
||||
"link": x.url,
|
||||
"title": x.title}
|
||||
"link": x.url,
|
||||
"title": x.title}
|
||||
for x in contents]
|
||||
|
||||
return docs
|
||||
@ -113,25 +113,27 @@ async def lookup_search_engine(
|
||||
docs = search_result2docs(results)
|
||||
return docs
|
||||
|
||||
|
||||
async def search_engine_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
|
||||
search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
|
||||
top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
|
||||
history: List[History] = Body([],
|
||||
description="历史对话",
|
||||
examples=[[
|
||||
{"role": "user",
|
||||
search_engine_name: str = Body(..., description="搜索引擎名称", examples=["duckduckgo"]),
|
||||
top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
|
||||
history: List[History] = Body([],
|
||||
description="历史对话",
|
||||
examples=[[
|
||||
{"role": "user",
|
||||
"content": "我们来玩成语接龙，我先来，生龙活虎"},
|
||||
{"role": "assistant",
|
||||
{"role": "assistant",
|
||||
"content": "虎头虎脑"}]]
|
||||
),
|
||||
stream: bool = Body(False, description="流式输出"),
|
||||
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
|
||||
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
|
||||
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
|
||||
prompt_name: str = Body("default",description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
|
||||
split_result: bool = Body(False, description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）")
|
||||
):
|
||||
),
|
||||
stream: bool = Body(False, description="流式输出"),
|
||||
model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
|
||||
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
|
||||
max_tokens: Optional[int] = Body(None,
|
||||
description="限制LLM生成Token数量，默认None代表模型最大值"),
|
||||
prompt_name: str = Body("default",
|
||||
description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
|
||||
split_result: bool = Body(False,
|
||||
description="是否对搜索结果进行拆分（主要用于metaphor搜索引擎）")
|
||||
):
|
||||
if search_engine_name not in SEARCH_ENGINES.keys():
|
||||
return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
|
||||
|
||||
@ -198,9 +200,9 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
|
||||
await task
|
||||
|
||||
return EventSourceResponse(search_engine_chat_iterator(query=query,
|
||||
search_engine_name=search_engine_name,
|
||||
top_k=top_k,
|
||||
history=history,
|
||||
model_name=model_name,
|
||||
prompt_name=prompt_name),
|
||||
)
|
||||
search_engine_name=search_engine_name,
|
||||
top_k=top_k,
|
||||
history=history,
|
||||
model_name=model_name,
|
||||
prompt_name=prompt_name),
|
||||
)
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
# 该文件封装了对api.py的请求，可以被不同的webui使用
|
||||
# 通过ApiRequest和AsyncApiRequest支持同步/异步调用
|
||||
|
||||
|
||||
from typing import *
|
||||
from pathlib import Path
|
||||
# 此处导入的配置为发起请求（如WEBUI）机器上的配置，主要用于为前端设置默认值。分布式部署时可以与服务器上的不同
|
||||
@ -27,7 +26,7 @@ from io import BytesIO
|
||||
from server.utils import set_httpx_config, api_address, get_httpx_client
|
||||
|
||||
from pprint import pprint
|
||||
|
||||
from langchain_core._api import deprecated
|
||||
|
||||
set_httpx_config()
|
||||
|
||||
@ -36,10 +35,11 @@ class ApiRequest:
|
||||
'''
|
||||
api.py调用的封装（同步模式）,简化api调用方式
|
||||
'''
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: str = api_address(),
|
||||
timeout: float = HTTPX_DEFAULT_TIMEOUT,
|
||||
self,
|
||||
base_url: str = api_address(),
|
||||
timeout: float = HTTPX_DEFAULT_TIMEOUT,
|
||||
):
|
||||
self.base_url = base_url
|
||||
self.timeout = timeout
|
||||
@ -55,12 +55,12 @@ class ApiRequest:
|
||||
return self._client
|
||||
|
||||
def get(
|
||||
self,
|
||||
url: str,
|
||||
params: Union[Dict, List[Tuple], bytes] = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
self,
|
||||
url: str,
|
||||
params: Union[Dict, List[Tuple], bytes] = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Union[httpx.Response, Iterator[httpx.Response], None]:
|
||||
while retry > 0:
|
||||
try:
|
||||
@ -75,13 +75,13 @@ class ApiRequest:
|
||||
retry -= 1
|
||||
|
||||
def post(
|
||||
self,
|
||||
url: str,
|
||||
data: Dict = None,
|
||||
json: Dict = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any
|
||||
self,
|
||||
url: str,
|
||||
data: Dict = None,
|
||||
json: Dict = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any
|
||||
) -> Union[httpx.Response, Iterator[httpx.Response], None]:
|
||||
while retry > 0:
|
||||
try:
|
||||
@ -97,13 +97,13 @@ class ApiRequest:
|
||||
retry -= 1
|
||||
|
||||
def delete(
|
||||
self,
|
||||
url: str,
|
||||
data: Dict = None,
|
||||
json: Dict = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any
|
||||
self,
|
||||
url: str,
|
||||
data: Dict = None,
|
||||
json: Dict = None,
|
||||
retry: int = 3,
|
||||
stream: bool = False,
|
||||
**kwargs: Any
|
||||
) -> Union[httpx.Response, Iterator[httpx.Response], None]:
|
||||
while retry > 0:
|
||||
try:
|
||||
@ -118,24 +118,25 @@ class ApiRequest:
|
||||
retry -= 1
|
||||
|
||||
def _httpx_stream2generator(
|
||||
self,
|
||||
response: contextlib._GeneratorContextManager,
|
||||
as_json: bool = False,
|
||||
self,
|
||||
response: contextlib._GeneratorContextManager,
|
||||
as_json: bool = False,
|
||||
):
|
||||
'''
|
||||
将httpx.stream返回的GeneratorContextManager转化为普通生成器
|
||||
'''
|
||||
|
||||
async def ret_async(response, as_json):
|
||||
try:
|
||||
async with response as r:
|
||||
async for chunk in r.aiter_text(None):
|
||||
if not chunk: # fastchat api yield empty bytes on start and end
|
||||
if not chunk: # fastchat api yield empty bytes on start and end
|
||||
continue
|
||||
if as_json:
|
||||
try:
|
||||
if chunk.startswith("data: "):
|
||||
data = json.loads(chunk[6:-2])
|
||||
elif chunk.startswith(":"): # skip sse comment line
|
||||
elif chunk.startswith(":"): # skip sse comment line
|
||||
continue
|
||||
else:
|
||||
data = json.loads(chunk)
|
||||
@ -143,7 +144,7 @@ class ApiRequest:
|
||||
except Exception as e:
|
||||
msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
exc_info=e if log_verbose else None)
|
||||
else:
|
||||
# print(chunk, end="", flush=True)
|
||||
yield chunk
|
||||
@ -158,20 +159,20 @@ class ApiRequest:
|
||||
except Exception as e:
|
||||
msg = f"API通信遇到错误：{e}"
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
exc_info=e if log_verbose else None)
|
||||
yield {"code": 500, "msg": msg}
|
||||
|
||||
def ret_sync(response, as_json):
|
||||
try:
|
||||
with response as r:
|
||||
for chunk in r.iter_text(None):
|
||||
if not chunk: # fastchat api yield empty bytes on start and end
|
||||
if not chunk: # fastchat api yield empty bytes on start and end
|
||||
continue
|
||||
if as_json:
|
||||
try:
|
||||
if chunk.startswith("data: "):
|
||||
data = json.loads(chunk[6:-2])
|
||||
elif chunk.startswith(":"): # skip sse comment line
|
||||
elif chunk.startswith(":"): # skip sse comment line
|
||||
continue
|
||||
else:
|
||||
data = json.loads(chunk)
|
||||
@ -179,7 +180,7 @@ class ApiRequest:
|
||||
except Exception as e:
|
||||
msg = f"接口返回json错误： ‘{chunk}’。错误信息是：{e}。"
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
exc_info=e if log_verbose else None)
|
||||
else:
|
||||
# print(chunk, end="", flush=True)
|
||||
yield chunk
|
||||
@ -194,7 +195,7 @@ class ApiRequest:
|
||||
except Exception as e:
|
||||
msg = f"API通信遇到错误：{e}"
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
exc_info=e if log_verbose else None)
|
||||
yield {"code": 500, "msg": msg}
|
||||
|
||||
if self._use_async:
|
||||
@ -203,16 +204,17 @@ class ApiRequest:
|
||||
return ret_sync(response, as_json)
|
||||
|
||||
def _get_response_value(
|
||||
self,
|
||||
response: httpx.Response,
|
||||
as_json: bool = False,
|
||||
value_func: Callable = None,
|
||||
self,
|
||||
response: httpx.Response,
|
||||
as_json: bool = False,
|
||||
value_func: Callable = None,
|
||||
):
|
||||
'''
|
||||
转换同步或异步请求返回的响应
|
||||
`as_json`: 返回json
|
||||
`value_func`: 用户可以自定义返回值，该函数接受response或json
|
||||
'''
|
||||
|
||||
def to_json(r):
|
||||
try:
|
||||
return r.json()
|
||||
@ -220,7 +222,7 @@ class ApiRequest:
|
||||
msg = "API未能返回正确的JSON。" + str(e)
|
||||
if log_verbose:
|
||||
logger.error(f'{e.__class__.__name__}: {msg}',
|
||||
exc_info=e if log_verbose else None)
|
||||
exc_info=e if log_verbose else None)
|
||||
return {"code": 500, "msg": msg, "data": None}
|
||||
|
||||
if value_func is None:
|
||||
@ -250,10 +252,10 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r: r["data"])
|
||||
|
||||
def get_prompt_template(
|
||||
self,
|
||||
type: str = "llm_chat",
|
||||
name: str = "default",
|
||||
**kwargs,
|
||||
self,
|
||||
type: str = "llm_chat",
|
||||
name: str = "default",
|
||||
**kwargs,
|
||||
) -> str:
|
||||
data = {
|
||||
"type": type,
|
||||
@ -297,15 +299,19 @@ class ApiRequest:
|
||||
response = self.post("/chat/chat", json=data, stream=True, **kwargs)
|
||||
return self._httpx_stream2generator(response, as_json=True)
|
||||
|
||||
@deprecated(
|
||||
since="0.3.0",
|
||||
message="自定义Agent问答将于 Langchain-Chatchat 0.3.x重写, 0.2.x中相关功能将废弃",
|
||||
removal="0.3.0")
|
||||
def agent_chat(
|
||||
self,
|
||||
query: str,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
self,
|
||||
query: str,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
):
|
||||
'''
|
||||
对应api.py/chat/agent_chat 接口
|
||||
@ -327,17 +333,17 @@ class ApiRequest:
|
||||
return self._httpx_stream2generator(response, as_json=True)
|
||||
|
||||
def knowledge_base_chat(
|
||||
self,
|
||||
query: str,
|
||||
knowledge_base_name: str,
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: float = SCORE_THRESHOLD,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
self,
|
||||
query: str,
|
||||
knowledge_base_name: str,
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: float = SCORE_THRESHOLD,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
):
|
||||
'''
|
||||
对应api.py/chat/knowledge_base_chat接口
|
||||
@ -366,28 +372,29 @@ class ApiRequest:
|
||||
return self._httpx_stream2generator(response, as_json=True)
|
||||
|
||||
def upload_temp_docs(
|
||||
self,
|
||||
files: List[Union[str, Path, bytes]],
|
||||
knowledge_id: str = None,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
self,
|
||||
files: List[Union[str, Path, bytes]],
|
||||
knowledge_id: str = None,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/upload_tmep_docs接口
|
||||
'''
|
||||
|
||||
def convert_file(file, filename=None):
|
||||
if isinstance(file, bytes): # raw bytes
|
||||
if isinstance(file, bytes): # raw bytes
|
||||
file = BytesIO(file)
|
||||
elif hasattr(file, "read"): # a file io like object
|
||||
elif hasattr(file, "read"): # a file io like object
|
||||
filename = filename or file.name
|
||||
else: # a local path
|
||||
else: # a local path
|
||||
file = Path(file).absolute().open("rb")
|
||||
filename = filename or os.path.split(file.name)[-1]
|
||||
return filename, file
|
||||
|
||||
files = [convert_file(file) for file in files]
|
||||
data={
|
||||
data = {
|
||||
"knowledge_id": knowledge_id,
|
||||
"chunk_size": chunk_size,
|
||||
"chunk_overlap": chunk_overlap,
|
||||
@ -402,17 +409,17 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def file_chat(
|
||||
self,
|
||||
query: str,
|
||||
knowledge_id: str,
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: float = SCORE_THRESHOLD,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
self,
|
||||
query: str,
|
||||
knowledge_id: str,
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: float = SCORE_THRESHOLD,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
):
|
||||
'''
|
||||
对应api.py/chat/file_chat接口
|
||||
@ -440,18 +447,23 @@ class ApiRequest:
|
||||
)
|
||||
return self._httpx_stream2generator(response, as_json=True)
|
||||
|
||||
@deprecated(
|
||||
since="0.3.0",
|
||||
message="搜索引擎问答将于 Langchain-Chatchat 0.3.x重写, 0.2.x中相关功能将废弃",
|
||||
removal="0.3.0"
|
||||
)
|
||||
def search_engine_chat(
|
||||
self,
|
||||
query: str,
|
||||
search_engine_name: str,
|
||||
top_k: int = SEARCH_ENGINE_TOP_K,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
split_result: bool = False,
|
||||
self,
|
||||
query: str,
|
||||
search_engine_name: str,
|
||||
top_k: int = SEARCH_ENGINE_TOP_K,
|
||||
history: List[Dict] = [],
|
||||
stream: bool = True,
|
||||
model: str = LLM_MODELS[0],
|
||||
temperature: float = TEMPERATURE,
|
||||
max_tokens: int = None,
|
||||
prompt_name: str = "default",
|
||||
split_result: bool = False,
|
||||
):
|
||||
'''
|
||||
对应api.py/chat/search_engine_chat接口
|
||||
@ -482,7 +494,7 @@ class ApiRequest:
|
||||
# 知识库相关操作
|
||||
|
||||
def list_knowledge_bases(
|
||||
self,
|
||||
self,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/list_knowledge_bases接口
|
||||
@ -493,10 +505,10 @@ class ApiRequest:
|
||||
value_func=lambda r: r.get("data", []))
|
||||
|
||||
def create_knowledge_base(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
vector_store_type: str = DEFAULT_VS_TYPE,
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
vector_store_type: str = DEFAULT_VS_TYPE,
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/create_knowledge_base接口
|
||||
@ -514,8 +526,8 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def delete_knowledge_base(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/delete_knowledge_base接口
|
||||
@ -527,8 +539,8 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def list_kb_docs(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/list_files接口
|
||||
@ -542,13 +554,13 @@ class ApiRequest:
|
||||
value_func=lambda r: r.get("data", []))
|
||||
|
||||
def search_kb_docs(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
query: str = "",
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: int = SCORE_THRESHOLD,
|
||||
file_name: str = "",
|
||||
metadata: dict = {},
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
query: str = "",
|
||||
top_k: int = VECTOR_SEARCH_TOP_K,
|
||||
score_threshold: int = SCORE_THRESHOLD,
|
||||
file_name: str = "",
|
||||
metadata: dict = {},
|
||||
) -> List:
|
||||
'''
|
||||
对应api.py/knowledge_base/search_docs接口
|
||||
@ -569,9 +581,9 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def update_docs_by_id(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
docs: Dict[str, Dict],
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
docs: Dict[str, Dict],
|
||||
) -> bool:
|
||||
'''
|
||||
对应api.py/knowledge_base/update_docs_by_id接口
|
||||
@ -587,32 +599,33 @@ class ApiRequest:
|
||||
return self._get_response_value(response)
|
||||
|
||||
def upload_kb_docs(
|
||||
self,
|
||||
files: List[Union[str, Path, bytes]],
|
||||
knowledge_base_name: str,
|
||||
override: bool = False,
|
||||
to_vector_store: bool = True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
docs: Dict = {},
|
||||
not_refresh_vs_cache: bool = False,
|
||||
self,
|
||||
files: List[Union[str, Path, bytes]],
|
||||
knowledge_base_name: str,
|
||||
override: bool = False,
|
||||
to_vector_store: bool = True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
docs: Dict = {},
|
||||
not_refresh_vs_cache: bool = False,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/upload_docs接口
|
||||
'''
|
||||
|
||||
def convert_file(file, filename=None):
|
||||
if isinstance(file, bytes): # raw bytes
|
||||
if isinstance(file, bytes): # raw bytes
|
||||
file = BytesIO(file)
|
||||
elif hasattr(file, "read"): # a file io like object
|
||||
elif hasattr(file, "read"): # a file io like object
|
||||
filename = filename or file.name
|
||||
else: # a local path
|
||||
else: # a local path
|
||||
file = Path(file).absolute().open("rb")
|
||||
filename = filename or os.path.split(file.name)[-1]
|
||||
return filename, file
|
||||
|
||||
files = [convert_file(file) for file in files]
|
||||
data={
|
||||
data = {
|
||||
"knowledge_base_name": knowledge_base_name,
|
||||
"override": override,
|
||||
"to_vector_store": to_vector_store,
|
||||
@ -633,11 +646,11 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def delete_kb_docs(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
file_names: List[str],
|
||||
delete_content: bool = False,
|
||||
not_refresh_vs_cache: bool = False,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
file_names: List[str],
|
||||
delete_content: bool = False,
|
||||
not_refresh_vs_cache: bool = False,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/delete_docs接口
|
||||
@ -655,8 +668,7 @@ class ApiRequest:
|
||||
)
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
|
||||
def update_kb_info(self,knowledge_base_name,kb_info):
|
||||
def update_kb_info(self, knowledge_base_name, kb_info):
|
||||
'''
|
||||
对应api.py/knowledge_base/update_info接口
|
||||
'''
|
||||
@ -672,15 +684,15 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def update_kb_docs(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
file_names: List[str],
|
||||
override_custom_docs: bool = False,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
docs: Dict = {},
|
||||
not_refresh_vs_cache: bool = False,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
file_names: List[str],
|
||||
override_custom_docs: bool = False,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
docs: Dict = {},
|
||||
not_refresh_vs_cache: bool = False,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/update_docs接口
|
||||
@ -706,14 +718,14 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def recreate_vector_store(
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
allow_empty_kb: bool = True,
|
||||
vs_type: str = DEFAULT_VS_TYPE,
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
self,
|
||||
knowledge_base_name: str,
|
||||
allow_empty_kb: bool = True,
|
||||
vs_type: str = DEFAULT_VS_TYPE,
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
chunk_overlap=OVERLAP_SIZE,
|
||||
zh_title_enhance=ZH_TITLE_ENHANCE,
|
||||
):
|
||||
'''
|
||||
对应api.py/knowledge_base/recreate_vector_store接口
|
||||
@ -738,8 +750,8 @@ class ApiRequest:
|
||||
|
||||
# LLM模型相关操作
|
||||
def list_running_models(
|
||||
self,
|
||||
controller_address: str = None,
|
||||
self,
|
||||
controller_address: str = None,
|
||||
):
|
||||
'''
|
||||
获取Fastchat中正运行的模型列表
|
||||
@ -755,8 +767,7 @@ class ApiRequest:
|
||||
"/llm_model/list_running_models",
|
||||
json=data,
|
||||
)
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", []))
|
||||
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", []))
|
||||
|
||||
def get_default_llm_model(self, local_first: bool = True) -> Tuple[str, bool]:
|
||||
'''
|
||||
@ -764,6 +775,7 @@ class ApiRequest:
|
||||
当 local_first=True 时，优先返回运行中的本地模型，否则优先按LLM_MODELS配置顺序返回。
|
||||
返回类型为（model_name, is_local_model）
|
||||
'''
|
||||
|
||||
def ret_sync():
|
||||
running_models = self.list_running_models()
|
||||
if not running_models:
|
||||
@ -780,7 +792,7 @@ class ApiRequest:
|
||||
model = m
|
||||
break
|
||||
|
||||
if not model: # LLM_MODELS中配置的模型都不在running_models里
|
||||
if not model: # LLM_MODELS中配置的模型都不在running_models里
|
||||
model = list(running_models)[0]
|
||||
is_local = not running_models[model].get("online_api")
|
||||
return model, is_local
|
||||
@ -801,7 +813,7 @@ class ApiRequest:
|
||||
model = m
|
||||
break
|
||||
|
||||
if not model: # LLM_MODELS中配置的模型都不在running_models里
|
||||
if not model: # LLM_MODELS中配置的模型都不在running_models里
|
||||
model = list(running_models)[0]
|
||||
is_local = not running_models[model].get("online_api")
|
||||
return model, is_local
|
||||
@ -812,8 +824,8 @@ class ApiRequest:
|
||||
return ret_sync()
|
||||
|
||||
def list_config_models(
|
||||
self,
|
||||
types: List[str] = ["local", "online"],
|
||||
self,
|
||||
types: List[str] = ["local", "online"],
|
||||
) -> Dict[str, Dict]:
|
||||
'''
|
||||
获取服务器configs中配置的模型列表，返回形式为{"type": {model_name: config}, ...}。
|
||||
@ -825,23 +837,23 @@ class ApiRequest:
|
||||
"/llm_model/list_config_models",
|
||||
json=data,
|
||||
)
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", {}))
|
||||
|
||||
def get_model_config(
|
||||
self,
|
||||
model_name: str = None,
|
||||
self,
|
||||
model_name: str = None,
|
||||
) -> Dict:
|
||||
'''
|
||||
获取服务器上模型配置
|
||||
'''
|
||||
data={
|
||||
data = {
|
||||
"model_name": model_name,
|
||||
}
|
||||
response = self.post(
|
||||
"/llm_model/get_model_config",
|
||||
json=data,
|
||||
)
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", {}))
|
||||
|
||||
def list_search_engines(self) -> List[str]:
|
||||
'''
|
||||
@ -850,12 +862,12 @@ class ApiRequest:
|
||||
response = self.post(
|
||||
"/server/list_search_engines",
|
||||
)
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r:r.get("data", {}))
|
||||
return self._get_response_value(response, as_json=True, value_func=lambda r: r.get("data", {}))
|
||||
|
||||
def stop_llm_model(
|
||||
self,
|
||||
model_name: str,
|
||||
controller_address: str = None,
|
||||
self,
|
||||
model_name: str,
|
||||
controller_address: str = None,
|
||||
):
|
||||
'''
|
||||
停止某个LLM模型。
|
||||
@ -873,10 +885,10 @@ class ApiRequest:
|
||||
return self._get_response_value(response, as_json=True)
|
||||
|
||||
def change_llm_model(
|
||||
self,
|
||||
model_name: str,
|
||||
new_model_name: str,
|
||||
controller_address: str = None,
|
||||
self,
|
||||
model_name: str,
|
||||
new_model_name: str,
|
||||
controller_address: str = None,
|
||||
):
|
||||
'''
|
||||
向fastchat controller请求切换LLM模型。
|
||||
@ -959,10 +971,10 @@ class ApiRequest:
|
||||
return ret_sync()
|
||||
|
||||
def embed_texts(
|
||||
self,
|
||||
texts: List[str],
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
to_query: bool = False,
|
||||
self,
|
||||
texts: List[str],
|
||||
embed_model: str = EMBEDDING_MODEL,
|
||||
to_query: bool = False,
|
||||
) -> List[List[float]]:
|
||||
'''
|
||||
对文本进行向量化，可选模型包括本地 embed_models 和支持 embeddings 的在线模型
|
||||
@ -979,10 +991,10 @@ class ApiRequest:
|
||||
return self._get_response_value(resp, as_json=True, value_func=lambda r: r.get("data"))
|
||||
|
||||
def chat_feedback(
|
||||
self,
|
||||
message_id: str,
|
||||
score: int,
|
||||
reason: str = "",
|
||||
self,
|
||||
message_id: str,
|
||||
score: int,
|
||||
reason: str = "",
|
||||
) -> int:
|
||||
'''
|
||||
反馈对话评价
|
||||
@ -1019,9 +1031,9 @@ def check_success_msg(data: Union[str, dict, list], key: str = "msg") -> str:
|
||||
return error message if error occured when requests API
|
||||
'''
|
||||
if (isinstance(data, dict)
|
||||
and key in data
|
||||
and "code" in data
|
||||
and data["code"] == 200):
|
||||
and key in data
|
||||
and "code" in data
|
||||
and data["code"] == 200):
|
||||
return data[key]
|
||||
return ""
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user