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Fix Deprecation warnings in logs service logs #1444

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7 changes: 4 additions & 3 deletions comps/dataprep/src/integrations/elasticsearch.py
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_huggingface import HuggingFaceEmbeddings

from comps import CustomLogger, DocPath, OpeaComponent, OpeaComponentRegistry, ServiceType
from comps.dataprep.src.utils import (
Expand Down Expand Up @@ -78,7 +79,7 @@ def create_index(self) -> None:
if not self.es_client.indices.exists(index=INDEX_NAME):
self.es_client.indices.create(index=INDEX_NAME)

def get_embedder(self) -> Union[HuggingFaceInferenceAPIEmbeddings, HuggingFaceBgeEmbeddings]:
def get_embedder(self) -> Union[HuggingFaceInferenceAPIEmbeddings, HuggingFaceEmbeddings]:
"""Obtain required Embedder."""
if TEI_EMBEDDING_ENDPOINT:
if not HUGGINGFACEHUB_API_TOKEN:
Expand All @@ -99,10 +100,10 @@ def get_embedder(self) -> Union[HuggingFaceInferenceAPIEmbeddings, HuggingFaceBg
)
return embedder
else:
return HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
return HuggingFaceEmbeddings(model_name=EMBED_MODEL)

def get_elastic_store(
self, embedder: Union[HuggingFaceInferenceAPIEmbeddings, HuggingFaceBgeEmbeddings]
self, embedder: Union[HuggingFaceInferenceAPIEmbeddings, HuggingFaceEmbeddings]
) -> ElasticsearchStore:
"""Get Elasticsearch vector store."""
return ElasticsearchStore(index_name=INDEX_NAME, embedding=embedder, es_connection=self.es_client)
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/milvus.py
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings, OpenAIEmbeddings
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_milvus.vectorstores import Milvus
from langchain_text_splitters import HTMLHeaderTextSplitter

Expand Down Expand Up @@ -213,7 +214,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ milvus embedding ] LOCAL_EMBEDDING_MODEL:{LOCAL_EMBEDDING_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=LOCAL_EMBEDDING_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=LOCAL_EMBEDDING_MODEL)
return embeddings

def check_health(self) -> bool:
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/opensearch.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import HTMLHeaderTextSplitter
from opensearchpy import OpenSearch

Expand Down Expand Up @@ -99,7 +100,7 @@ def __init__(self, name: str, description: str, config: dict = None):
api_key=HUGGINGFACEHUB_API_TOKEN, model_name=model_id, api_url=TEI_EMBEDDING_ENDPOINT
)
else:
self.embeddings = HuggingFaceBgeEmbeddings(model_name=Config.EMBED_MODEL)
self.embeddings = HuggingFaceEmbeddings(model_name=Config.EMBED_MODEL)

# OpenSearch client setup
self.auth = ("admin", Config.OPENSEARCH_INITIAL_ADMIN_PASSWORD)
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/pgvect.py
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import PGVector
from langchain_huggingface import HuggingFaceEmbeddings

from comps import CustomLogger, DocPath, OpeaComponent, OpeaComponentRegistry, ServiceType
from comps.dataprep.src.utils import (
Expand Down Expand Up @@ -73,7 +74,7 @@ def __init__(self, name: str, description: str, config: dict = None):
)
else:
# create embeddings using local embedding model
self.embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
self.embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)

# Perform health check
health_status = self.check_health()
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/pipecone.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from fastapi import Body, File, Form, HTTPException, UploadFile
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_text_splitters import HTMLHeaderTextSplitter
from pinecone import Pinecone, ServerlessSpec
Expand Down Expand Up @@ -72,7 +73,7 @@ def __init__(self, name: str, description: str, config: dict = None):
)
else:
# create embeddings using local embedding model
self.embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
self.embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
self.pc = Pinecone(api_key=PINECONE_API_KEY)

# Perform health check
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/qdrant.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import Qdrant
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import HTMLHeaderTextSplitter
from qdrant_client import QdrantClient

Expand Down Expand Up @@ -70,7 +71,7 @@ def __init__(self, name: str, description: str, config: dict = None):
)
else:
# create embeddings using local embedding model
self.embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
self.embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)

# Perform health check
health_status = self.check_health()
Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/redis.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import Redis
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import HTMLHeaderTextSplitter
from redis import asyncio as aioredis
from redis.commands.search.field import TextField
Expand Down Expand Up @@ -326,7 +327,7 @@ async def _initialize_embedder(self):
)
else:
# create embeddings using local embedding model
embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embedder

async def check_health(self) -> bool:
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_core.documents import Document
from langchain_huggingface import HuggingFaceEndpointEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings, HuggingFaceEndpointEmbeddings
from langchain_redis import RedisConfig, RedisVectorStore
from openai import OpenAI
from tqdm import tqdm
Expand Down Expand Up @@ -47,7 +47,7 @@ def get_embedder():
embedder = HuggingFaceEndpointEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
else:
# create embeddings using local embedding model
embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embedder


Expand Down
3 changes: 2 additions & 1 deletion comps/dataprep/src/integrations/vdms.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores.vdms import VDMS, VDMS_Client
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import HTMLHeaderTextSplitter

from comps import CustomLogger, DocPath, OpeaComponent, OpeaComponentRegistry, ServiceType
Expand Down Expand Up @@ -83,7 +84,7 @@ def __init__(self, name: str, description: str, config: dict = None):
)
else:
# create embeddings using local embedding model
self.embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
self.embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)

# Perform health check
health_status = self.check_health()
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/elasticsearch.py
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_elasticsearch import ElasticsearchStore
from langchain_huggingface import HuggingFaceEmbeddings

from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType

Expand Down Expand Up @@ -61,7 +62,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{EMBED_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embeddings

def _initialize_client(self) -> Elasticsearch:
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/milvus.py
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_milvus.vectorstores import Milvus

from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType
Expand Down Expand Up @@ -64,7 +65,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{LOCAL_EMBEDDING_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=LOCAL_EMBEDDING_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=LOCAL_EMBEDDING_MODEL)
return embeddings

def _initialize_client(self) -> Milvus:
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/opensearch.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_huggingface import HuggingFaceEmbeddings
from pydantic import conlist

from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType
Expand Down Expand Up @@ -71,7 +72,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{EMBED_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embeddings

def _initialize_client(self):
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/pgvector.py
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import PGVector
from langchain_huggingface import HuggingFaceEmbeddings

from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType

Expand Down Expand Up @@ -60,7 +61,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{EMBED_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embeddings

def _initialize_client(self) -> PGVector:
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/pinecone.py
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@

from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone, ServerlessSpec

Expand Down Expand Up @@ -62,7 +63,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{EMBED_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embeddings

def _initialize_client(self) -> Pinecone:
Expand Down
4 changes: 2 additions & 2 deletions comps/retrievers/src/integrations/redis.py
Original file line number Diff line number Diff line change
Expand Up @@ -48,9 +48,9 @@ def __init__(self, name: str, description: str, config: dict = None):
self.embeddings = BridgeTowerEmbedding()
else:
# create embeddings using local embedding model
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.embeddings import HuggingFaceEmbeddings

self.embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
self.embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
self.client = self._initialize_client()
health_status = self.check_health()
if not health_status:
Expand Down
3 changes: 2 additions & 1 deletion comps/retrievers/src/integrations/vdms.py
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from fastapi import HTTPException
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores.vdms import VDMS, VDMS_Client
from langchain_huggingface import HuggingFaceEmbeddings

from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType

Expand Down Expand Up @@ -73,7 +74,7 @@ def _initialize_embedder(self):
# create embeddings using local embedding model
if logflag:
logger.info(f"[ init embedder ] LOCAL_EMBEDDING_MODEL:{EMBED_MODEL}")
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
return embeddings

def _initialize_vector_db(self) -> VDMS:
Expand Down
1 change: 1 addition & 0 deletions comps/third_parties/pathway/src/requirements.txt
Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
langchain
langchain-community
langchain-huggingface
openai
pathway[xpack-llm]
sentence-transformers
Expand Down
3 changes: 2 additions & 1 deletion comps/third_parties/pathway/src/vectorstore_pathway.py
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import pathway as pw
from langchain import text_splitter
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings
from pathway.xpacks.llm.parsers import ParseUnstructured
from pathway.xpacks.llm.vector_store import VectorStoreServer

Expand Down Expand Up @@ -52,7 +53,7 @@
)
else:
# create embeddings using local embedding model
embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)

server = VectorStoreServer.from_langchain_components(
*data_sources,
Expand Down
1 change: 1 addition & 0 deletions requirements.txt
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@ httpx
kubernetes
langchain
langchain-community
langchain-huggingface
opentelemetry-api
opentelemetry-exporter-otlp
opentelemetry-sdk
Expand Down
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