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"""This module contains functions for loading a ConversationalRetrievalChain"""
import logging
import wandb
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
## deprectated from langchain.vectorstores import Chroma
from langchain_community.vectorstores import Chroma
from prompts import load_chat_prompt
logger = logging.getLogger(__name__)
def load_vector_store(wandb_run: wandb.run, openai_api_key: str) -> Chroma:
"""Load a vector store from a Weights & Biases artifact
Args:
run (wandb.run): An active Weights & Biases run
openai_api_key (str): The OpenAI API key to use for embedding
Returns:
Chroma: A chroma vector store object
"""
# load vector store artifact
vector_store_artifact_dir = wandb_run.use_artifact(
wandb_run.config.vector_store_artifact, type="search_index"
).download()
embedding_fn = OpenAIEmbeddings(openai_api_key=openai_api_key)
# load vector store
vector_store = Chroma(
embedding_function=embedding_fn, persist_directory=vector_store_artifact_dir
)
return vector_store
def load_chain(wandb_run: wandb.run, vector_store: Chroma, openai_api_key: str):
"""Load a ConversationalQA chain from a config and a vector store
Args:
wandb_run (wandb.run): An active Weights & Biases run
vector_store (Chroma): A Chroma vector store object
openai_api_key (str): The OpenAI API key to use for embedding
Returns:
ConversationalRetrievalChain: A ConversationalRetrievalChain object
"""
retriever = vector_store.as_retriever()
llm = ChatOpenAI(
openai_api_key=openai_api_key,
model_name=wandb_run.config.model_name,
temperature=wandb_run.config.chat_temperature,
max_retries=wandb_run.config.max_fallback_retries,
)
chat_prompt_dir = wandb_run.use_artifact(
wandb_run.config.chat_prompt_artifact, type="prompt"
).download()
qa_prompt = load_chat_prompt(f"{chat_prompt_dir}/chat_prompt_massa.json")
print ( '\\n===================\\nqa_prompt = ', qa_prompt)
qa_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
chain_type="stuff",
retriever=retriever,
combine_docs_chain_kwargs={"prompt": qa_prompt},
return_source_documents=True,
)
return qa_chain
def get_answer(
chain: ConversationalRetrievalChain,
question: str,
chat_history: list[tuple[str, str]],
wandb_run: wandb.run
):
"""Get an answer from a ConversationalRetrievalChain
Args:
chain (ConversationalRetrievalChain): A ConversationalRetrievalChain object
question (str): The question to ask
chat_history (list[tuple[str, str]]): A list of tuples of (question, answer)
Returns:
str: The answer to the question
"""
# Define logging configuration
logging.basicConfig(filename='user_input.log', level=logging.INFO,
format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
# Log user question
logging.info(f"User question: {question}")
wandb.log({"question": question })
# Log training progress
result = chain(
inputs={"question": question, "chat_history": chat_history},
return_only_outputs=True,
)
response = f"Answer:\t{result['answer']}"
print( "file name"+ wandb_run.config.log_file)
f_name = wandb_run.config.log_file
if isinstance(f_name, str) and f_name:
f_name = pathlib.Path(f_name)
with open(f_name, "w") as file1:
# Writing data to a file
file1.write("Hello \n")
file1.writelines(L)
#if f_name and f_name.is_file():
# ret = f_name.write("r"))
# if f_name and f_name.is_file():
##template = json.load(f_name.open("r"))
print("File writing complete."+"quest = "+question+" answer : "+ result['answer'])
return response
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