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import os
import pandas as pd
import chainlit as cl
from chainlit import user_session
from chainlit.types import LLMSettings
from langchain import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import AzureOpenAI
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferWindowMemory
from langchain.vectorstores import Chroma
from langchain.vectorstores.base import VectorStoreRetriever
current_agent = os.environ["AGENT"]
def load_dialogues():
df = pd.read_excel(os.environ["DIALOGUE_SHEET"], header=0, keep_default_na=False)
df = df[df["Agent"] == current_agent]
return df.astype(str)
def load_persona():
df = pd.read_excel(os.environ["PERSONA_SHEET"], header=0, keep_default_na=False)
df = df[df["Agent"] == current_agent]
return df.astype(str)
def load_prompt_engineering():
df = pd.read_excel(
os.environ["PROMPT_ENGINEERING_SHEET"], header=0, keep_default_na=False
)
df = df[df["Agent"] == current_agent]
return df.astype(str)
def load_documents(df, page_content_column: str):
return DataFrameLoader(df, page_content_column).load()
def init_embedding_function():
EMBEDDING_MODEL_FOLDER = ".embedding-model"
return HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2",
encode_kwargs={"normalize_embeddings": True},
cache_folder=EMBEDDING_MODEL_FOLDER,
)
def load_vectordb(init: bool = False):
vectordb = None
VECTORDB_FOLDER = ".vectordb"
if not init:
vectordb = Chroma(
embedding_function=init_embedding_function(),
persist_directory=VECTORDB_FOLDER,
)
if init or not vectordb.get()["ids"]:
vectordb = Chroma.from_documents(
documents=load_documents(load_dialogues(), page_content_column="Utterance"),
embedding=init_embedding_function(),
persist_directory=VECTORDB_FOLDER,
)
vectordb.persist()
return vectordb
def get_retriever(context_state: str, vectordb):
return VectorStoreRetriever(
vectorstore=vectordb,
search_type="similarity",
search_kwargs={
"filter": {
"$or": [{"Context": {"$eq": ""}}, {"Context": {"$eq": context_state}}]
},
"k": 1,
},
)
vectordb = load_vectordb()
@cl.langchain_factory(use_async=True)
def factory():
df_prompt_engineering = load_prompt_engineering()
user_session.set("context_state", "")
llm_settings = LLMSettings(
model_name="text-davinci-003",
temperature=df_prompt_engineering["Temperature"].values[0],
)
user_session.set("llm_settings", llm_settings)
llm = AzureOpenAI(
deployment_name="davinci003",
model_name=llm_settings.model_name,
temperature=llm_settings.temperature,
streaming=True,
)
utterance_prompt = PromptTemplate.from_template(
df_prompt_engineering["Utterance-Prompt"].values[0]
)
chat_memory = ConversationBufferWindowMemory(
memory_key="History",
input_key="Utterance",
k=df_prompt_engineering["History"].values[0],
)
utterance_chain = LLMChain(
prompt=utterance_prompt,
llm=llm,
verbose=False,
memory=chat_memory,
)
continuation_prompt = PromptTemplate.from_template(
df_prompt_engineering["Continuation-Prompt"].values[0]
)
continuation_chain = LLMChain(
prompt=continuation_prompt,
llm=llm,
verbose=False,
memory=chat_memory,
)
user_session.set("continuation_chain", continuation_chain)
return utterance_chain
@cl.langchain_run
async def run(agent, input_str):
global vectordb
if input_str == "/reload":
vectordb = load_vectordb(True)
await cl.Message(content="Data loaded").send()
else:
df_persona = load_persona()
retriever = get_retriever(user_session.get("context_state"), vectordb)
document = retriever.get_relevant_documents(query=input_str)
response = await agent.acall(
{
"Persona": df_persona.loc[
df_persona["AI"] == document[0].metadata["AI"]
]["Persona"].values[0],
"Utterance": input_str,
"Response": document[0].metadata["Response"],
},
callbacks=[cl.AsyncLangchainCallbackHandler()],
)
await cl.Message(
content=response["text"],
author=document[0].metadata["AI"],
llm_settings=user_session.get("llm_settings"),
).send()
user_session.set("context_state", document[0].metadata["Contextualisation"])
continuation = document[0].metadata["Continuation"]
while continuation != "":
document_continuation = vectordb.get(where={"Intent": continuation})
continuation_chain = user_session.get("continuation_chain")
response = await continuation_chain.acall(
{
"Persona": df_persona.loc[
df_persona["AI"] == document_continuation["metadatas"][0]["AI"]
]["Persona"].values[0],
"Utterance": "",
"Response": document_continuation["metadatas"][0]["Response"],
},
callbacks=[cl.AsyncLangchainCallbackHandler()],
)
await cl.Message(
content=response["text"],
author=document_continuation["metadatas"][0]["AI"],
llm_settings=user_session.get("llm_settings"),
).send()
user_session.set(
"context_state",
document_continuation["metadatas"][0]["Contextualisation"],
)
continuation = document_continuation["metadatas"][0]["Continuation"]
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