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# # Files used:
# conf.files.context
# conf.files.index
# conf.files.embeddings
import logging
import csv
import io
import hydra
from omegaconf import DictConfig, OmegaConf
import faiss
from datasets import load_dataset, IterableDataset
from tqdm.auto import tqdm
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
log = logging.getLogger(__name__)
def get_hnsw_pq_index(conf: DictConfig) -> faiss.IndexHNSWPQ:
index = faiss.index_factory(
conf.embeddings.dim,
f"HNSW{conf.indexes.hnsw_pq.hnsw_m},PQ{conf.indexes.hnsw_pq.pq_m}x{conf.indexes.hnsw_pq.bits}",
)
index.hnsw.efConstruction = conf.indexes.hnsw_pq.ef_construction
index.hnsw.efSearch = conf.indexes.hnsw_pq.ef_search
return index
def get_hnsw_index(conf: DictConfig) -> faiss.IndexHNSW:
index = faiss.index_factory(
conf.embeddings.dim,
f"HNSW{conf.indexes.hnsw_pq.hnsw_m}",
)
index.hnsw.efConstruction = conf.indexes.hnsw_pq.ef_construction
index.hnsw.efSearch = conf.indexes.hnsw_pq.ef_search
return index
def get_index(conf: DictConfig) -> faiss.Index:
if conf.indexing.index_type == "hnsw_pq":
return get_hnsw_pq_index(conf)
elif conf.indexing.index_type == "hnsw":
return get_hnsw_index(conf)
else:
raise ValueError(f"Unknown index type: {conf.indexing.index_type}")
def get_embedding_model(conf: DictConfig) -> HuggingFaceEmbeddings:
return HuggingFaceEmbeddings(
model_name=conf.embeddings.model,
model_kwargs={"trust_remote_code": True, "device": conf.embeddings.device},
)
def get_faiss_store(conf: DictConfig, embedding_model: HuggingFaceEmbeddings) -> FAISS:
index = get_index(conf)
return FAISS(
embedding_function=embedding_model,
index=index,
docstore=InMemoryDocstore(),
index_to_docstore_id={},
)
def get_dataset(
conf,
) -> IterableDataset:
with open(conf.files.context, "r") as f:
num_lines = sum(1 for _ in f)
dataset = load_dataset(
"json",
data_files={"train": [conf.files.context]},
streaming=True,
split="train",
)
batched_dataset = dataset.batch(batch_size=conf.indexing.batch_size)
return batched_dataset, num_lines
def add_batch(
embedding_model: HuggingFaceEmbeddings,
faiss_store: FAISS,
csv_writer: csv.writer,
f: io.TextIOWrapper,
batch: dict,
) -> None:
embeddings = embedding_model.embed_documents(batch["text_content"])
text_and_embeddings = zip(batch["text_content"], embeddings)
metadatas = [
{
"source_name": source_name,
"associated_dates": associated_date,
"chunk_id": chunk_id,
}
for source_name, associated_date, chunk_id in zip(
batch["source_name"], batch["associated_dates"], batch["chunk_id"]
)
]
faiss_store.add_embeddings(
text_embeddings=text_and_embeddings, metadatas=metadatas, ids=batch["chunk_id"]
)
csv_writer.writerows([[f"{x:.8f}" for x in row] for row in embeddings])
f.flush()
def save_index(conf: DictConfig, faiss_store: FAISS) -> None:
faiss_store.save_local(conf.files.index)
@hydra.main(version_base=None, config_path="conf", config_name="config")
def main(conf: DictConfig) -> None:
log.info("Config:\n%s", OmegaConf.to_yaml(conf))
embedding_model = get_embedding_model(conf)
faiss_store = get_faiss_store(conf, embedding_model)
dataset, num_lines = get_dataset(conf)
with open(conf.files.embeddings, "a", newline="") as f:
csv_writer = csv.writer(f, delimiter="\t")
for batch in tqdm(dataset, total=num_lines // conf.indexing.batch_size):
add_batch(embedding_model, faiss_store, csv_writer, f, batch)
save_index(conf, faiss_store)
if __name__ == "__main__":
main()
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