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from typing import Any, List, Mapping, Optional
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from typing import Literal
import requests
from langchain.prompts import PromptTemplate, ChatPromptTemplate
from operator import itemgetter
# from langchain.memory import ConversationBufferMemory
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_models import ChatOpenAI
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_core.messages import AIMessage, HumanMessage
def custom_chain_with_history(llm, memory):
prompt = PromptTemplate.from_template("""<s><INST><|system|>
You are a college student at Gunadarma University, majoring in Informatics / Computer Science. Your name is Jonathan Jordan, but people call you Jojo. You have expertise in Artificial Intelligence and Data Science. You are also good at Data Engineering with AirFlow and Apache Spark. You currently live in Jakarta, Indonesia. You are 21 years old. Your birthday is Friday, 21 June 2002.
Below are the detailed personal information of you:
{context}
Respond to the user with the following chat history between you and the user:
{chat_history}
<|interviewer|>
{question}
<|you|>
""")
def prompt_memory(_):
t = ""
for x in memory.chat_memory.messages:
t += f"<|assistant|>\n<s>{x.content}</s>\n" if type(x) is AIMessage else f"<|user|>\n{x.content}\n"
# t += f"You: {x.content}\n" if type(x) is AIMessage else f"User: {x.content}\n"
print(q)
return "" if len(t) == 0 else t
def format_docs(docs):
print(len(docs))
return "\n".join([f"{i+1}. {d.page_content}" for i,d in enumerate(docs)])
# prompt = ChatPromptTemplate.from_messages(
# [
# ("system", "You are a helpful chatbot"),
# MessagesPlaceholder(variable_name="history"),
# ("human", "{input}"),
# ]
# )
return {"chat_history":prompt_memory, "context":db.as_retriever(search_type="similarity", search_kwargs={"k": 8}) | format_docs, "question": RunnablePassthrough()} | prompt | llm
class CustomLLM(LLM):
repo_id : str
api_token : str
model_type: Literal["text2text-generation", "text-generation"]
max_new_tokens: int = None
temperature: float = 0.001
timeout: float = None
top_p: float = None
top_k : int = None
repetition_penalty : float = None
stop : List[str] = []
@property
def _llm_type(self) -> str:
return "custom"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
headers = {"Authorization": f"Bearer {self.api_token}"}
API_URL = f"https://api-inference.huggingface.co/models/{self.repo_id}"
parameters_dict = {
'max_new_tokens': self.max_new_tokens,
'temperature': self.temperature,
'timeout': self.timeout,
'top_p': self.top_p,
'top_k': self.top_k,
'repetition_penalty': self.repetition_penalty,
'stop':self.stop
}
if self.model_type == 'text-generation':
parameters_dict["return_full_text"]=False
data = {"inputs": prompt, "parameters":parameters_dict, "options":{"wait_for_model":True}}
data = requests.post(API_URL, headers=headers, json=data).json()
print(data)
return data[0]['generated_text']
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
'repo_id': self.repo_id,
'model_type':self.model_type,
'stop_sequences':self.stop,,
'max_new_tokens': self.max_new_tokens,
'temperature': self.temperature,
'timeout': self.timeout,
'top_p': self.top_p,
'top_k': self.top_k,
'repetition_penalty': self.repetition_penalty
}
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