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import os
from dataclasses import asdict
import pandas as pd
from langchain.callbacks import get_openai_callback
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import TokenTextSplitter
from langchain.vectorstores import Chroma
from tqdm import tqdm
from wandb.integration.langchain import WandbTracer
import wandb
from config import config
def get_data(artifact_name: str, total_episodes=None):
podcast_artifact = wandb.use_artifact(artifact_name, type="dataset")
podcast_artifact_dir = podcast_artifact.download(config.root_artifact_dir)
filename = artifact_name.split(":")[0].split("/")[-1]
df = pd.read_csv(os.path.join(podcast_artifact_dir, f"{filename}.csv"))
if total_episodes is not None:
df = df.iloc[:total_episodes]
return df
def create_embeddings(episode_df: pd.DataFrame, index: int):
# load docs into langchain format
loader = DataFrameLoader(episode_df, page_content_column="transcript")
data = loader.load()
# split the documents
text_splitter = TokenTextSplitter.from_tiktoken_encoder(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(data)
title = data[0].metadata["title"]
print(f"Number of documents for podcast {title}: {len(docs)}")
# initialize embedding engine
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(
docs,
embeddings,
persist_directory=os.path.join(config.root_data_dir / "chromadb", str(index)),
)
db.persist()
if __name__ == "__main__":
# initialize wandb tracer
WandbTracer.init(
{
"project": config.project_name,
"job_type": "embed_transcripts",
"config": asdict(config),
}
)
# get data
df = get_data(artifact_name=config.summarized_que_data_artifact)
# create embeddings
with get_openai_callback() as cb:
for episode in tqdm(df.iterrows(), total=len(df), desc="Embedding transcripts"):
episode_data = episode[1].to_frame().T
create_embeddings(episode_data, index=episode[0])
print("*" * 25)
print(cb)
print("*" * 25)
wandb.log(
{
"total_prompt_tokens": cb.prompt_tokens,
"total_completion_tokens": cb.completion_tokens,
"total_tokens": cb.total_tokens,
"total_cost": cb.total_cost,
}
)
# log embeddings to wandb artifact
artifact = wandb.Artifact("transcript_embeddings", type="dataset")
artifact.add_dir(config.root_data_dir / "chromadb")
wandb.log_artifact(artifact)
WandbTracer.finish()
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