Refactor ChromaSearchComponent configuration

This commit is contained in:
Gabriel Luiz Freitas Almeida 2024-02-15 18:48:29 -03:00
commit dc055c5ee6

View file

@ -3,7 +3,7 @@ from typing import List, Optional
import chromadb # type: ignore
from langchain_community.vectorstores.chroma import Chroma
from langflow import CustomComponent
from langflow.field_typing import Document, Embeddings, Text
from langflow.field_typing import Embeddings, Text
from langflow.schema import Record, docs_to_records
@ -15,6 +15,7 @@ class ChromaSearchComponent(CustomComponent):
display_name: str = "Chroma Search"
description: str = "Search a Chroma collection for similar documents."
beta: bool = True
icon = "Chroma"
def build_config(self):
"""
@ -25,13 +26,19 @@ class ChromaSearchComponent(CustomComponent):
"""
return {
"inputs": {"display_name": "Input"},
"search_type": {"display_name": "Search Type", "options": ["Similarity", "MMR"]},
"search_type": {
"display_name": "Search Type",
"options": ["Similarity", "MMR"],
},
"collection_name": {"display_name": "Collection Name", "value": "langflow"},
"persist": {"display_name": "Persist"},
"persist_directory": {"display_name": "Persist Directory"},
# "persist": {"display_name": "Persist"},
"index_directory": {"display_name": "Index Directory"},
"code": {"show": False, "display_name": "Code"},
"documents": {"display_name": "Documents", "is_list": True},
"embedding": {"display_name": "Embedding"},
"embedding": {
"display_name": "Embedding",
"info": "Embedding model to vectorize inputs (make sure to use same as index)",
},
"chroma_server_cors_allow_origins": {
"display_name": "Server CORS Allow Origins",
"advanced": True,
@ -53,11 +60,9 @@ class ChromaSearchComponent(CustomComponent):
inputs: Text,
search_type: str,
collection_name: str,
persist: bool,
embedding: Embeddings,
chroma_server_ssl_enabled: bool,
persist_directory: Optional[str] = None,
documents: Optional[List[Document]] = None,
index_directory: Optional[str] = None,
chroma_server_cors_allow_origins: Optional[str] = None,
chroma_server_host: Optional[str] = None,
chroma_server_port: Optional[int] = None,
@ -87,26 +92,20 @@ class ChromaSearchComponent(CustomComponent):
if chroma_server_host is not None:
chroma_settings = chromadb.config.Settings(
chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,
chroma_server_cors_allow_origins=chroma_server_cors_allow_origins
or None,
chroma_server_host=chroma_server_host,
chroma_server_port=chroma_server_port or None,
chroma_server_grpc_port=chroma_server_grpc_port or None,
chroma_server_ssl_enabled=chroma_server_ssl_enabled,
)
# If documents, then we need to create a Chroma instance using .from_documents
if documents is not None and embedding is not None:
if len(documents) == 0:
raise ValueError("If documents are provided, there must be at least one document.")
chroma = Chroma.from_documents(
documents=documents, # type: ignore
persist_directory=persist_directory if persist else None,
collection_name=collection_name,
embedding=embedding,
client_settings=chroma_settings,
)
else:
chroma = Chroma(persist_directory=persist_directory, client_settings=chroma_settings)
chroma = Chroma(
embedding_function=embedding,
collection_name=collection_name,
persist_directory=index_directory,
client_settings=chroma_settings,
)
# Validate the inputs
docs = []