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Merge branch 'rag' into frontier

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binary-husky
2024-09-02 15:01:12 +00:00
parent a95b3daab9 80b1a6f99b
commit 34784c1d40
21 changed files with 1376 additions and 48 deletions
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28
request_llms/bridge_all.py
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@@ -407,22 +407,46 @@ model_info = {
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# Gemini
# Note: now gemini-pro is an alias of gemini-1.0-pro.
# Warning: gemini-pro-vision has been deprecated.
# Support for gemini-pro-vision has been removed.
"gemini-pro": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": gemini_endpoint,
"has_multimodal_capacity": False,
"max_token": 1024 * 32,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gemini-pro-vision": {
"gemini-1.0-pro": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": gemini_endpoint,
"has_multimodal_capacity": False,
"max_token": 1024 * 32,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gemini-1.5-pro": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": gemini_endpoint,
"has_multimodal_capacity": True,
"max_token": 1024 * 204800,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
"gemini-1.5-flash": {
"fn_with_ui": genai_ui,
"fn_without_ui": genai_noui,
"endpoint": gemini_endpoint,
"has_multimodal_capacity": True,
"max_token": 1024 * 204800,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
},
# cohere
"cohere-command-r-plus": {
@@ -857,7 +881,7 @@ if "sparkv2" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
})
except:
print(trimmed_format_exc())
if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS: # 讯飞星火认知大模型
if any(x in AVAIL_LLM_MODELS for x in ("sparkv3", "sparkv3.5", "sparkv4")): # 讯飞星火认知大模型
try:
from .bridge_spark import predict_no_ui_long_connection as spark_noui
from .bridge_spark import predict as spark_ui

43
request_llms/bridge_google_gemini.py
View File

@@ -8,15 +8,15 @@ import os
import time
from request_llms.com_google import GoogleChatInit
from toolbox import ChatBotWithCookies
from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat
from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat, encode_image
proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
console_slience=False):
def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[],
console_slience:bool=False):
# 检查API_KEY
if get_conf("GEMINI_API_KEY") == "":
raise ValueError(f"请配置 GEMINI_API_KEY。")
@@ -44,9 +44,20 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
return gpt_replying_buffer
def make_media_input(inputs, image_paths):
image_base64_array = []
for image_path in image_paths:
path = os.path.abspath(image_path)
inputs = inputs + f'<br/><br/><div align="center"><img src="file={path}"></div>'
base64 = encode_image(path)
image_base64_array.append(base64)
return inputs, image_base64_array
def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
from .bridge_all import model_info
# 检查API_KEY
if get_conf("GEMINI_API_KEY") == "":
yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
@@ -57,18 +68,17 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
if "vision" in llm_kwargs["llm_model"]:
have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
if not have_recent_file:
chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
return
def make_media_input(inputs, image_paths):
for image_path in image_paths:
inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
return inputs
if have_recent_file:
inputs = make_media_input(inputs, image_paths)
# multimodal capacity
# inspired by codes in bridge_chatgpt
has_multimodal_capacity = model_info[llm_kwargs['llm_model']].get('has_multimodal_capacity', False)
if has_multimodal_capacity:
has_recent_image_upload, image_paths = have_any_recent_upload_image_files(chatbot, pop=True)
else:
has_recent_image_upload, image_paths = False, []
if has_recent_image_upload:
inputs, image_base64_array = make_media_input(inputs, image_paths)
else:
inputs, image_base64_array = inputs, []
chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history)
@@ -76,7 +86,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
retry = 0
while True:
try:
stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity)
break
except Exception as e:
retry += 1
@@ -112,7 +122,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
yield from update_ui(chatbot=chatbot, history=history)
if __name__ == '__main__':
import sys
llm_kwargs = {'llm_model': 'gemini-pro'}

47
request_llms/com_google.py
View File

@@ -7,7 +7,7 @@ import os
import re
import requests
from typing import List, Dict, Tuple
from toolbox import get_conf, encode_image, get_pictures_list, to_markdown_tabs
from toolbox import get_conf, update_ui, encode_image, get_pictures_list, to_markdown_tabs
proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS")
@@ -112,6 +112,14 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr
return a
def reverse_base64_from_input(inputs):
pattern = re.compile(r'<br/><br/><div align="center"><img[^<>]+base64="([^"]+)"></div>')
base64_strings = pattern.findall(inputs)
return base64_strings
def contain_base64(inputs):
base64_strings = reverse_base64_from_input(inputs)
return len(base64_strings) > 0
class GoogleChatInit:
def __init__(self, llm_kwargs):
@@ -119,9 +127,9 @@ class GoogleChatInit:
endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k"
def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
def generate_chat(self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False):
headers, payload = self.generate_message_payload(
inputs, llm_kwargs, history, system_prompt
inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity
)
response = requests.post(
url=self.url_gemini,
@@ -133,13 +141,16 @@ class GoogleChatInit:
)
return response.iter_lines()
def __conversation_user(self, user_input, llm_kwargs):
def __conversation_user(self, user_input, llm_kwargs, enable_multimodal_capacity):
what_i_have_asked = {"role": "user", "parts": []}
if "vision" not in self.url_gemini:
from .bridge_all import model_info
if enable_multimodal_capacity:
input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
else:
input_ = user_input
encode_img = []
else:
input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
what_i_have_asked["parts"].append({"text": input_})
if encode_img:
for data in encode_img:
@@ -153,12 +164,12 @@ class GoogleChatInit:
)
return what_i_have_asked
def __conversation_history(self, history, llm_kwargs):
def __conversation_history(self, history, llm_kwargs, enable_multimodal_capacity):
messages = []
conversation_cnt = len(history) // 2
if conversation_cnt:
for index in range(0, 2 * conversation_cnt, 2):
what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
what_i_have_asked = self.__conversation_user(history[index], llm_kwargs, enable_multimodal_capacity)
what_gpt_answer = {
"role": "model",
"parts": [{"text": history[index + 1]}],
@@ -168,7 +179,7 @@ class GoogleChatInit:
return messages
def generate_message_payload(
self, inputs, llm_kwargs, history, system_prompt
self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False
) -> Tuple[Dict, Dict]:
messages = [
# {"role": "system", "parts": [{"text": system_prompt}]}, # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。
@@ -179,21 +190,29 @@ class GoogleChatInit:
"%m", llm_kwargs["llm_model"]
).replace("%k", get_conf("GEMINI_API_KEY"))
header = {"Content-Type": "application/json"}
if "vision" not in self.url_gemini: # 不是vision 才处理history
if has_multimodal_capacity:
enable_multimodal_capacity = (len(image_base64_array) > 0) or any([contain_base64(h) for h in history])
else:
enable_multimodal_capacity = False
if not enable_multimodal_capacity:
messages.extend(
self.__conversation_history(history, llm_kwargs)
self.__conversation_history(history, llm_kwargs, enable_multimodal_capacity)
) # 处理 history
messages.append(self.__conversation_user(inputs, llm_kwargs)) # 处理用户对话
messages.append(self.__conversation_user(inputs, llm_kwargs, enable_multimodal_capacity)) # 处理用户对话
payload = {
"contents": messages,
"generationConfig": {
# "maxOutputTokens": 800,
# "maxOutputTokens": llm_kwargs.get("max_token", 1024),
"stopSequences": str(llm_kwargs.get("stop", "")).split(" "),
"temperature": llm_kwargs.get("temperature", 1),
"topP": llm_kwargs.get("top_p", 0.8),
"topK": 10,
},
}
return header, payload

40
request_llms/embed_models/bridge_all_embed.py Normal file
View File

@@ -0,0 +1,40 @@
import tiktoken, copy, re
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
# Endpoint 重定向
API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
openai_endpoint = "https://api.openai.com/v1/chat/completions"
if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
openai_embed_endpoint = openai_endpoint.replace("chat/completions", "embeddings")
from .openai_embed import OpenAiEmbeddingModel
embed_model_info = {
# text-embedding-3-small Increased performance over 2nd generation ada embedding model | 1,536
"text-embedding-3-small": {
"embed_class": OpenAiEmbeddingModel,
"embed_endpoint": openai_embed_endpoint,
"embed_dimension": 1536,
},
# text-embedding-3-large Most capable embedding model for both english and non-english tasks | 3,072
"text-embedding-3-large": {
"embed_class": OpenAiEmbeddingModel,
"embed_endpoint": openai_embed_endpoint,
"embed_dimension": 3072,
},
# text-embedding-ada-002 Most capable 2nd generation embedding model, replacing 16 first generation models | 1,536
"text-embedding-ada-002": {
"embed_class": OpenAiEmbeddingModel,
"embed_endpoint": openai_embed_endpoint,
"embed_dimension": 1536,
},
}

79
request_llms/embed_models/openai_embed.py Normal file
View File

@@ -0,0 +1,79 @@
from llama_index.embeddings.openai import OpenAIEmbedding
from openai import OpenAI
from toolbox import get_conf
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder, ProxyNetworkActivate
from shared_utils.key_pattern_manager import select_api_key_for_embed_models
from typing import List, Any
import numpy as np
def mean_agg(embeddings):
"""Mean aggregation for embeddings."""
return np.array(embeddings).mean(axis=0).tolist()
class EmbeddingModel():
def get_agg_embedding_from_queries(
self,
queries: List[str],
agg_fn = None,
):
"""Get aggregated embedding from multiple queries."""
query_embeddings = [self.get_query_embedding(query) for query in queries]
agg_fn = agg_fn or mean_agg
return agg_fn(query_embeddings)
def get_text_embedding_batch(
self,
texts: List[str],
show_progress: bool = False,
):
return self.compute_embedding(texts, batch_mode=True)
class OpenAiEmbeddingModel(EmbeddingModel):
def __init__(self, llm_kwargs:dict=None):
self.llm_kwargs = llm_kwargs
def get_query_embedding(self, query: str):
return self.compute_embedding(query)
def compute_embedding(self, text="这是要计算嵌入的文本", llm_kwargs:dict=None, batch_mode=False):
from .bridge_all_embed import embed_model_info
# load kwargs
if llm_kwargs is None:
llm_kwargs = self.llm_kwargs
if llm_kwargs is None:
raise RuntimeError("llm_kwargs is not provided!")
# setup api and req url
api_key = select_api_key_for_embed_models(llm_kwargs['api_key'], llm_kwargs['embed_model'])
embed_model = llm_kwargs['embed_model']
base_url = embed_model_info[llm_kwargs['embed_model']]['embed_endpoint'].replace('embeddings', '')
# send and compute
with ProxyNetworkActivate("Connect_OpenAI_Embedding"):
self.oai_client = OpenAI(api_key=api_key, base_url=base_url)
if batch_mode:
input = text
assert isinstance(text, list)
else:
input = [text]
assert isinstance(text, str)
res = self.oai_client.embeddings.create(input=input, model=embed_model)
# parse result
if batch_mode:
embedding = [d.embedding for d in res.data]
else:
embedding = res.data[0].embedding
return embedding
def embedding_dimension(self, llm_kwargs):
from .bridge_all_embed import embed_model_info
return embed_model_info[llm_kwargs['embed_model']]['embed_dimension']
if __name__ == "__main__":
pass