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import faiss
from tqdm import tqdm
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.docstore import InMemoryDocstore
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.memory import (
ConversationBufferMemory,
CombinedMemory,
)
from langchain.prompts import PromptTemplate
from langchain.vectorstores import FAISS
from data_driven_characters.memory import ConversationVectorStoreRetrieverMemory
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
import pickle
import os.path
class RetrievalChatBot:
def __init__(self, character_definition, documents):
self.character_definition = character_definition
self.documents = documents
self.num_context_memories = 10
self.chat_history_key = "chat_history"
self.context_key = "context"
self.input_key = "input"
self.chain = self.create_chain(character_definition)
def create_chain(self, character_definition):
conv_memory = ConversationBufferMemory(
memory_key=self.chat_history_key, input_key=self.input_key
)
#embeddings = OpenAIEmbeddings()
#saved_db = FAISS.load_local('tzamir.ifass', embeddings)
context_memory = ConversationVectorStoreRetrieverMemory(
retriever=FAISS(
OpenAIEmbeddings().embed_query,
faiss.IndexFlatL2(1536), # Dimensions of the OpenAIEmbeddings
InMemoryDocstore({}),
{},
).as_retriever(search_kwargs=dict(k=self.num_context_memories)),
#retriever=saved_db.as_retriever(search_kwargs=dict(k=self.num_context_memories)),
memory_key=self.context_key,
output_prefix=character_definition.name,
blacklist=[self.chat_history_key],
)
# add the documents to the context memory if not saved on disk
memory_path = 'output/tzamir/memory.pkl'
if not os.path.exists(memory_path):
print("gerando os indices")
for i, summary in tqdm(enumerate(self.documents)):
context_memory.save_context(inputs={}, outputs={f"[{i}]": summary})
# salvando no disco
memory_pickle = open('output/tzamir/memory.pkl', 'wb')
pickle.dump(context_memory, memory_pickle)
else:
print("carregando memoria do disco")
memory_pickle = open('output/tzamir/memory.pkl', 'rb')
context_memory = pickle.load(memory_pickle)
# Combined
memory = CombinedMemory(memories=[conv_memory, context_memory])
#print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
#print(memory)
#print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
prompt = PromptTemplate.from_template(
f"""Your name is {character_definition.name}.
You will have a conversation with a Human, and you will engage in a dialogue with them.
You will not exaggerate your personality, interests, desires, emotions, and other traits. Keep your tone as objective as possible.
You will stay in character as {character_definition.name} throughout the conversation, even if the Human asks you questions that you don't know the answer to.
You will not break character as {character_definition.name}.
You are {character_definition.name} in the following story snippets, which describe events in your life.
---
{{{self.context_key}}}
---
Current conversation:
---
{character_definition.name}: {character_definition.greeting}
{{{self.chat_history_key}}}
---
Human: {{{self.input_key}}}
{character_definition.name}:"""
)
GPT3 = ChatOpenAI(model_name="gpt-3.5-turbo",temperature=0.5)
chatbot = ConversationChain(
llm=GPT3, verbose=True, memory=memory, prompt=prompt
)
return chatbot
def greet(self):
return self.character_definition.greeting
def step(self, input):
return self.chain.run(input=input)
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