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139 lines
4.8 KiB
139 lines
4.8 KiB
import asyncio
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import time
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from .prompts import *
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from .langchain_memory import BotConversationSummerBufferWindowMemory
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from langchain import PromptTemplate
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from langchain import LLMChain, ConversationChain
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from langchain.memory import ConversationBufferMemory
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from langchain.chains.base import Chain
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from typing import Dict, List
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from langchain.document_loaders import TextLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.embeddings import SentenceTransformerEmbeddings
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from langchain.vectorstores import Chroma
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import logging
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logger = logging.getLogger(__name__)
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class RoleplayChain(Chain):
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llm_chain: LLMChain
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character_name: str
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persona: str
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scenario: str
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ai_name_chat: str
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human_name_chat: str
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output_key: str = "output_text" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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return ["character_name", "persona", "scenario", "ai_name_chat", "human_name_chat", "llm_chain"]
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@property
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def output_keys(self) -> List[str]:
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return [self.output_key]
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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other_keys = {k: v for k, v in inputs.items() if k not in self.input_keys}
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result = self.llm_chain.predict(**other_keys)
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return {self.output_key: result}
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class AI(object):
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def __init__(self, bot, text_wrapper, image_wrapper, memory_path: str):
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self.name = bot.name
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self.bot = bot
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self.memory_path = memory_path
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from ..wrappers.langchain_koboldcpp import KoboldCpp
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self.llm_chat = KoboldCpp(temperature=self.bot.temperature, endpoint_url="http://172.16.85.10:5001/api/latest/generate", stop=['<|endoftext|>'])
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self.llm_summary = KoboldCpp(temperature=0.2, endpoint_url="http://172.16.85.10:5001/api/latest/generate", stop=['<|endoftext|>'])
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self.text_wrapper = text_wrapper
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self.image_wrapper = image_wrapper
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#self.memory = BotConversationSummerBufferWindowMemory(llm=self.llm_summary, max_token_limit=1200, min_token_limit=200)
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self.memory = ConversationBufferMemory(memory_key="chat_history", human_prefix="You", ai_prefix=self.bot.name)
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async def generate(self, message, reply_fn, typing_fn):
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embeddings = SentenceTransformerEmbeddings()
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#embeddings = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
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loader = TextLoader('./germany.txt')
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(
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# Set a really small chunk size, just to show.
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chunk_size = 600,
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chunk_overlap = 100,
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length_function = len,
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)
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docs = text_splitter.split_documents(documents)
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db = Chroma(persist_directory=f'{self.memory_path}/chroma-db', embedding_function=embeddings)
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print(f"Indexing {len(docs)} documents")
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texts = [doc.page_content for doc in docs]
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metadatas = [doc.metadata for doc in docs]
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#db.add_texts(texts=texts, metadatas=metadatas, ids=None)
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#db.persist()
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query = "How is climate in Germany?"
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output_docs = db.similarity_search_with_score(query)
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print(query)
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print('###')
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for doc, score in output_docs:
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print("-" * 80)
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print("Score: ", score)
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print(doc.page_content)
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print("-" * 80)
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prompt_template = "{input}"
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chain = LLMChain(
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llm=self.llm_chat,
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prompt=PromptTemplate.from_template(prompt_template),
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)
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output = chain.run(message.message)
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return output.strip()
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async def generate_roleplay(self, message, reply_fn, typing_fn):
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self.memory.human_prefix = message.user_name
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prompt = prompt_vicuna.partial(
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ai_name=self.bot.name,
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persona=self.bot.persona,
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scenario=self.bot.scenario,
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human_name=message.user_name,
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ai_name_chat=self.bot.name,
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)
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chain = ConversationChain(
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llm=self.llm_chat,
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prompt=prompt,
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verbose=True,
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memory=self.memory,
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#stop=['<|endoftext|>', '\nYou:', f"\n{message.user_name}:"],
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)
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# output = llm_chain(inputs={"ai_name": self.bot.name, "persona": self.bot.persona, "scenario": self.bot.scenario, "human_name": message.user_name, "ai_name_chat": self.bot.name, "chat_history": "", "input": message.message})['results'][0]['text']
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#roleplay_chain = RoleplayChain(llm_chain=chain, character_name=self.bot.name, persona=self.bot.persona, scenario=self.bot.scenario, ai_name_chat=self.bot.name, human_name_chat=message.user_name)
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output = chain.run({"input":message.message, "stop": ['<|endoftext|>', f"\n{message.user_name}:"]})
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return output.strip()
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def estimate_num_tokens(input_text: str):
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return len(input_text)//4+1
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