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import json
import os
import re
from langchain.chains import ConversationChain, LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains.base import Chain
from app_modules.llm_inference import LLMInference, get_system_prompt_and_user_message
from app_modules.utils import CustomizedConversationSummaryBufferMemory
from langchain.chains import LLMChain
from langchain.globals import get_debug
chat_history_enabled = os.getenv("CHAT_HISTORY_ENABLED", "false").lower() == "true"
B_INST, E_INST = "[INST]", "[/INST]"
def create_llama_2_prompt_template():
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
system_prompt, user_message = get_system_prompt_and_user_message()
SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
prompt_template = B_INST + SYSTEM_PROMPT + user_message + E_INST
return prompt_template
def create_llama_3_prompt_template():
system_prompt, user_message = get_system_prompt_and_user_message()
prompt_template = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{ system_prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
{ user_message }<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
return prompt_template
def create_phi_3_prompt_template():
system_prompt, user_message = get_system_prompt_and_user_message()
prompt_template = f"""<|system|>
{ system_prompt }<|end|>
<|user|>
{ user_message }<|end|>
<|assistant|>
"""
return prompt_template
def create_orca_2_prompt_template():
system_prompt, user_message = get_system_prompt_and_user_message(orca=False)
prompt_template = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
return prompt_template
def create_mistral_prompt_template():
system_prompt, user_message = get_system_prompt_and_user_message()
prompt_template = B_INST + system_prompt + "\n\n" + user_message + E_INST
return prompt_template
def create_gemma_prompt_template():
return "<start_of_turn>user\n{input}<end_of_turn>\n<start_of_turn>model\n"
def create_prompt_template(model_name):
print(f"creating prompt template for model: {model_name}")
if re.search(r"llama-?2", model_name, re.IGNORECASE):
return create_llama_2_prompt_template()
elif re.search(r"llama-?3", model_name, re.IGNORECASE):
return create_llama_3_prompt_template()
elif re.search(r"phi-?3", model_name, re.IGNORECASE):
return create_phi_3_prompt_template()
elif model_name.lower().startswith("orca"):
return create_orca_2_prompt_template()
elif model_name.lower().startswith("mistral"):
return create_mistral_prompt_template()
elif model_name.lower().startswith("gemma"):
return create_gemma_prompt_template()
return (
"""You are a chatbot having a conversation with a human.
{history}
Human: {input}
Chatbot:"""
if chat_history_enabled
else """You are a chatbot having a conversation with a human.
Human: {input}
Chatbot:"""
)
class ChatChain(LLMInference):
def __init__(self, llm_loader):
super().__init__(llm_loader)
def create_chain(self) -> Chain:
template = create_prompt_template(self.llm_loader.model_name)
print(f"template: {template}")
if chat_history_enabled:
prompt = PromptTemplate(
input_variables=["history", "input"], template=template
)
memory = CustomizedConversationSummaryBufferMemory(
llm=self.llm_loader.llm, max_token_limit=1024, return_messages=False
)
llm_chain = ConversationChain(
llm=self.llm_loader.llm,
prompt=prompt,
verbose=False,
memory=memory,
)
else:
prompt = PromptTemplate(input_variables=["input"], template=template)
llm_chain = LLMChain(llm=self.llm_loader.llm, prompt=prompt)
return llm_chain
def _process_inputs(self, inputs):
if not isinstance(inputs, list):
inputs = {"input": inputs["question"]}
elif self.llm_loader.llm_model_type == "huggingface":
inputs = [self.apply_chat_template(input["question"]) for input in inputs]
else:
inputs = [{"input": i["question"]} for i in inputs]
if get_debug():
print("_process_inputs:", json.dumps(inputs, indent=4))
return inputs
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