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Implement persistent chroma DB

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Nielson Janné 2025-03-12 22:05:02 +01:00
parent 860cfc3438
commit 06dbfed5a0
3 changed files with 564 additions and 55 deletions
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31
generic_rag/app.py
View File

@ -7,13 +7,14 @@ from pathlib import Path
import chainlit as cl
from chainlit.cli import run_chainlit
from langchain import hub
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict
from backend.models import BackendType, get_embedding_model, get_chat_model
from parsers.parser import process_local_files, process_web_sites
from langchain_community.vectorstores.utils import filter_complex_metadata
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@ -36,6 +37,8 @@ parser.add_argument("-w", "--web-data", type=str, nargs="*", default=[],
help="One or multiple URLs to use for retrieval.")
parser.add_argument("--web-chunk-size", type=int, default=200,
help="The size of the chunks to split the text into.")
parser.add_argument("-c", "--chroma-db-location", type=Path, default=Path(".chroma_db"),
help="file path to store or load a Chroma DB from/to.")
args = parser.parse_args()
@ -64,17 +67,12 @@ def generate(state: State):
@cl.on_chat_start
async def on_chat_start():
embedding = get_embedding_model(args.back_end)
vector_store = InMemoryVectorStore(embedding)
vector_store = Chroma(collection_name="generic_rag",
embedding_function=get_embedding_model(args.back_end),
persist_directory=str(args.chroma_db_location))
pdf_splits = await cl.make_async(process_local_files)(args.pdf_data, args.pdf_chunk_size,
args.pdf_chunk_overlap, args.pdf_add_start_index)
web_splits = await cl.make_async(process_web_sites)(args.web_data, args.web_chunk_size)
_ = vector_store.add_documents(documents=pdf_splits + web_splits)
cl.user_session.set("emb_model", embedding)
cl.user_session.set("vector_store", vector_store)
cl.user_session.set("emb_model", get_embedding_model(args.back_end))
cl.user_session.set("chat_model", get_chat_model(args.back_end))
cl.user_session.set("prompt", hub.pull("rlm/rag-prompt"))
@ -84,6 +82,7 @@ async def on_chat_start():
cl.user_session.set("graph", graph)
@cl.on_message
async def on_message(message: cl.Message):
graph = cl.user_session.get("graph")
@ -112,4 +111,16 @@ async def set_starters():
if __name__ == "__main__":
pdf_splits = process_local_files(args.pdf_data, args.pdf_chunk_size, args.pdf_chunk_overlap,
args.pdf_add_start_index)
web_splits = process_web_sites(args.web_data, args.web_chunk_size)
filtered_splits = filter_complex_metadata(pdf_splits + web_splits)
vector_store = Chroma(collection_name="generic_rag",
embedding_function=get_embedding_model(args.back_end),
persist_directory=str(args.chroma_db_location))
_ = vector_store.add_documents(documents=filtered_splits)
del vector_store
run_chainlit(__file__)

1
pyproject.toml
View File

@ -10,6 +10,7 @@ dependencies = [
"dotenv>=0.9.9",
"langchain>=0.3.20",
"langchain-aws>=0.2.15",
"langchain-chroma>=0.2.2",
"langchain-community>=0.3.19",
"langchain-google-vertexai>=2.0.15",
"langchain-huggingface>=0.1.2",

587
uv.lock
View File

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