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from langchain import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferWindowMemory, CombinedMemory
from langchain import PromptTemplate
from constants import prompt_number_snippets, gpt_model_to_use, gpt_max_tokens
from search_indexing import search_faiss_index
class SnippetsBufferWindowMemory(ConversationBufferWindowMemory):
"""
MemoryBuffer used to hold the document snippets. Inherits from ConversationBufferWindowMemory, and overwrites the
load_memory_variables method
"""
index: FAISS = None
pages: list = []
memory_key = 'snippets'
snippets: list = []
def __init__(self, *args, **kwargs):
ConversationBufferWindowMemory.__init__(self, *args, **kwargs)
self.index = kwargs['index']
def load_memory_variables(self, inputs) -> dict:
"""
Based on the user inputs, search the index and add the similar snippets to memory (but only if they aren't in the
memory already)
"""
# Search snippets
similar_snippets = search_faiss_index(self.index, inputs['user_messages_history'])
# In order to respect the buffer size and make its pruning work, need to reverse the list, and then un-reverse it later
# This way, the most relevant snippets are kept at the start of the list
self.snippets = [snippet for snippet in reversed(self.snippets)]
self.pages = [page for page in reversed(self.pages)]
for snippet in similar_snippets:
page_number = snippet.metadata['page']
# Load into memory only new snippets
snippet_to_add = f"The following snippet was extracted from the following document: "
if snippet.metadata['title'] == snippet.metadata['source']:
snippet_to_add += f"{snippet.metadata['source']}\n"
else:
snippet_to_add += f"[{snippet.metadata['title']}]({snippet.metadata['source']})\n"
snippet_to_add += f"<START_SNIPPET_PAGE_{page_number + 1}>\n"
snippet_to_add += f"{snippet.page_content}\n"
snippet_to_add += f"<END_SNIPPET_PAGE_{page_number + 1}>\n"
if snippet_to_add not in self.snippets:
self.pages.append(page_number)
self.snippets.append(snippet_to_add)
# Reverse list of snippets and pages, in order to keep the most relevant at the top
# Also prune the list to keep the buffer within the define size (k)
self.snippets = [snippet for snippet in reversed(self.snippets)][:self.k]
self.pages = [page for page in reversed(self.pages)][:self.k]
to_return = ''.join(self.snippets)
return {'snippets': to_return}
def construct_conversation(prompt: str, llm, memory) -> ConversationChain:
"""
Construct a ConversationChain object
"""
prompt = PromptTemplate.from_template(
template=prompt,
)
conversation = ConversationChain(
llm=llm,
memory=memory,
verbose=False,
prompt=prompt
)
return conversation
def initialize_chat_conversation(index: FAISS,
model_to_use: str = gpt_model_to_use,
max_tokens: int = gpt_max_tokens) -> ConversationChain:
prompt_header = """You are an expert, tasked with helping customers with their questions. They will ask you questions and provide technical snippets that may or may not contain the answer, and it's your job to find the answer if possible, while taking into account the entire conversation context.
The following snippets can be used to help you answer the questions:
{snippets}
The following is a friendly conversation between a customer and you. Please answer the customer's needs based on the provided snippets and the conversation history. Make sure to take the previous messages in consideration, as they contain additional context.
If the provided snippets don't include the answer, please say so, and don't try to make up an answer instead. Include in your reply the title of the document and the page from where your answer is coming from, if applicable.
{history}
Customer: {input}
"""
llm = ChatOpenAI(model_name=model_to_use, max_tokens=max_tokens)
conv_memory = ConversationBufferWindowMemory(k=3, input_key="input")
snippets_memory = SnippetsBufferWindowMemory(k=prompt_number_snippets, index=index, memory_key='snippets', input_key="snippets")
memory = CombinedMemory(memories=[conv_memory, snippets_memory])
conversation = construct_conversation(prompt_header, llm, memory)
return conversation
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