Fix the retriever issue of Milvus

README.md

@@ -61,8 +61,6 @@ The initially supported `Microservices` are described in the below table. More `
 A `Microservices` can be created by using the decorator `register_microservice`. Taking the `embedding microservice` as an example:
 
 ```python
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-
 from comps import register_microservice, EmbedDoc, ServiceType, TextDoc
 
 

comps/retrievers/deployment/docker_compose/compose.yaml

@@ -3,6 +3,7 @@
 
 include:
   - ../../../third_parties/elasticsearch/deployment/docker_compose/compose.yaml
+  - ../../../third_parties/milvus/deployment/docker_compose/compose.yaml
   - ../../../third_parties/opensearch/deployment/docker_compose/compose.yaml
   - ../../../third_parties/neo4j/deployment/docker_compose/compose.yaml
   - ../../../third_parties/pgvector/deployment/docker_compose/compose.yaml
@@ -49,6 +50,12 @@ services:
     depends_on:
       tei-embedding-serving:
         condition: service_healthy
+      standalone:
+        condition: service_healthy
+      etcd:
+        condition: service_healthy
+      minio:
+        condition: service_healthy
 
   retriever-neo4j:
     extends: retriever

comps/retrievers/src/README_elasticsearch.md

@@ -50,6 +50,7 @@ Please refer to this [readme](../../third_parties/elasticsearch/src/README.md).
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_ELASTICSEARCH"
 python opea_retrievers_microservice.py
 ```
@@ -64,6 +65,7 @@ export ES_CONNECTION_STRING="http://localhost:9200"
 export INDEX_NAME=${your_index_name}
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_ELASTICSEARCH"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 ```
 
 ### 2.2 Build Docker Image
@@ -83,7 +85,8 @@ You can choose one as needed.
 ### 2.3 Run Docker with CLI (Option A)
 
 ```bash
-docker run -d --name="retriever-elasticsearch" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e ES_CONNECTION_STRING=$ES_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/retriever:latest
+docker run -d --name="retriever-elasticsearch" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e ES_CONNECTION_STRING=$ES_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=${TEI_EMBEDDING_ENDPOINT}
+-e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} opea/retriever:latest
 ```
 
 ### 2.4 Run Docker with Docker Compose (Option B)

comps/retrievers/src/README_milvus.md

@@ -22,12 +22,13 @@ export MILVUS_HOST=${your_milvus_host_ip}
 export MILVUS_PORT=19530
 export COLLECTION_NAME=${your_collection_name}
 export TEI_EMBEDDING_ENDPOINT=${your_emdding_endpoint}
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 ```
 
 ### Start Retriever Service
 
 ```bash
-export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:${your_embedding_port}"
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_MILVUS"
 python opea_retrievers_microservice.py
 ```
@@ -44,7 +45,7 @@ docker build -t opea/retriever:latest --build-arg https_proxy=$https_proxy --bui
 ### Run Docker with CLI (Option A)
 
 ```bash
-docker run -d --name="retriever-milvus-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=${your_emdding_endpoint} -e MILVUS_HOST=${your_milvus_host_ip} -e RETRIEVER_COMPONENT_NAME=$RETRIEVER_COMPONENT_NAME opea/retriever:latest
+docker run -d --name="retriever-milvus-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e TEI_EMBEDDING_ENDPOINT=${your_emdding_endpoint} -e MILVUS_HOST=${your_milvus_host_ip} -e HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token} -e RETRIEVER_COMPONENT_NAME=$RETRIEVER_COMPONENT_NAME opea/retriever:latest
 ```
 
 ### Run Docker with Docker Compose (Option B)

comps/retrievers/src/README_opensearch.md

@@ -44,6 +44,7 @@ Please refer to this [readme](../../third_parties/opensearch/src/README.md).
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_OPENSEARCH"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_token}
 python opea_retrievers_microservice.py
 ```
 

comps/retrievers/src/README_vdms.md

@@ -65,6 +65,7 @@ Please refer to this [readme](../../third_parties/vdms/src/README.md).
 
 ```bash
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_VDMS"
 python opea_retrievers_microservice.py
 ```
@@ -77,6 +78,7 @@ python opea_retrievers_microservice.py
 export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
 export INDEX_NAME=${your_index_name or collection_name}
 export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_VDMS"
 ```
 
@@ -97,7 +99,7 @@ You can choose one as needed.
 ### 2.3 Run Docker with CLI (Option A)
 
 ```bash
-docker run -d --name="retriever-vdms-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e RETRIEVER_COMPONENT_NAME=$RETRIEVER_COMPONENT_NAME opea/retriever:latest
+docker run -d --name="retriever-vdms-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e RETRIEVER_COMPONENT_NAME=$RETRIEVER_COMPONENT_NAME opea/retriever:latest
 ```
 
 ### 2.4 Run Docker with Docker Compose (Option B)

comps/retrievers/src/integrations/config.py

@@ -44,8 +44,9 @@ def get_boolean_env_var(var_name, default_value=False):
 # Embedding model
 EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
 LOCAL_EMBEDDING_MODEL = os.getenv("LOCAL_EMBEDDING_MODEL", "maidalun1020/bce-embedding-base_v1")
-TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT")
+TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT", "")
 BRIDGE_TOWER_EMBEDDING = os.getenv("BRIDGE_TOWER_EMBEDDING", False)
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN", "")
 
 # Directory pathss
 current_file_path = os.path.abspath(__file__)
@@ -122,7 +123,6 @@ def format_redis_conn_from_env():
 COLLECTION_NAME = os.getenv("COLLECTION_NAME", "rag_milvus")
 # TEI configuration
 TEI_EMBEDDING_MODEL = os.environ.get("TEI_EMBEDDING_MODEL", "/home/user/bce-embedding-base_v1")
-TEI_EMBEDDING_ENDPOINT = os.environ.get("TEI_EMBEDDING_ENDPOINT", "")
 os.environ["OPENAI_API_BASE"] = TEI_EMBEDDING_ENDPOINT
 os.environ["OPENAI_API_KEY"] = "Dummy key"
 

comps/retrievers/src/integrations/elasticsearch.py

@@ -5,12 +5,13 @@
 import os
 
 from elasticsearch import Elasticsearch
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from fastapi import HTTPException
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
 from langchain_elasticsearch import ElasticsearchStore
 
 from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType
 
-from .config import EMBED_MODEL, ES_CONNECTION_STRING, ES_INDEX_NAME, TEI_EMBEDDING_ENDPOINT
+from .config import EMBED_MODEL, ES_CONNECTION_STRING, ES_INDEX_NAME, HUGGINGFACEHUB_API_TOKEN, TEI_EMBEDDING_ENDPOINT
 
 logger = CustomLogger("es_retrievers")
 logflag = os.getenv("LOGFLAG", False)
@@ -40,7 +41,22 @@ def _initialize_embedder(self):
             # create embeddings using TEI endpoint service
             if logflag:
                 logger.info(f"[ init embedder ] TEI_EMBEDDING_ENDPOINT:{TEI_EMBEDDING_ENDPOINT}")
-            embeddings = HuggingFaceHubEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
+            if not HUGGINGFACEHUB_API_TOKEN:
+                raise HTTPException(
+                    status_code=400,
+                    detail="You MUST offer the `HUGGINGFACEHUB_API_TOKEN` when using `TEI_EMBEDDING_ENDPOINT`.",
+                )
+            import requests
+
+            response = requests.get(TEI_EMBEDDING_ENDPOINT + "/info")
+            if response.status_code != 200:
+                raise HTTPException(
+                    status_code=400, detail=f"TEI embedding endpoint {TEI_EMBEDDING_ENDPOINT} is not available."
+                )
+            model_id = response.json()["model_id"]
+            embeddings = HuggingFaceInferenceAPIEmbeddings(
+                api_key=HUGGINGFACEHUB_API_TOKEN, model_name=model_id, api_url=TEI_EMBEDDING_ENDPOINT
+            )
         else:
             # create embeddings using local embedding model
             if logflag:

comps/retrievers/src/integrations/milvus.py

@@ -3,14 +3,21 @@
 
 
 import os
-from typing import List, Optional
 
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from fastapi import HTTPException
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
 from langchain_milvus.vectorstores import Milvus
 
 from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType
 
-from .config import COLLECTION_NAME, INDEX_PARAMS, LOCAL_EMBEDDING_MODEL, MILVUS_URI, TEI_EMBEDDING_ENDPOINT
+from .config import (
+    COLLECTION_NAME,
+    HUGGINGFACEHUB_API_TOKEN,
+    INDEX_PARAMS,
+    LOCAL_EMBEDDING_MODEL,
+    MILVUS_URI,
+    TEI_EMBEDDING_ENDPOINT,
+)
 
 logger = CustomLogger("milvus_retrievers")
 logflag = os.getenv("LOGFLAG", False)
@@ -28,7 +35,6 @@ def __init__(self, name: str, description: str, config: dict = None):
         super().__init__(name, ServiceType.RETRIEVER.name.lower(), description, config)
 
         self.embedder = self._initialize_embedder()
-        self.client = self._initialize_client()
         health_status = self.check_health()
         if not health_status:
             logger.error("OpeaMilvusRetriever health check failed.")
@@ -38,7 +44,22 @@ def _initialize_embedder(self):
             # create embeddings using TEI endpoint service
             if logflag:
                 logger.info(f"[ init embedder ] TEI_EMBEDDING_ENDPOINT:{TEI_EMBEDDING_ENDPOINT}")
-            embeddings = HuggingFaceHubEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
+            if not HUGGINGFACEHUB_API_TOKEN:
+                raise HTTPException(
+                    status_code=400,
+                    detail="You MUST offer the `HUGGINGFACEHUB_API_TOKEN` when using `TEI_EMBEDDING_ENDPOINT`.",
+                )
+            import requests
+
+            response = requests.get(TEI_EMBEDDING_ENDPOINT + "/info")
+            if response.status_code != 200:
+                raise HTTPException(
+                    status_code=400, detail=f"TEI embedding endpoint {TEI_EMBEDDING_ENDPOINT} is not available."
+                )
+            model_id = response.json()["model_id"]
+            embeddings = HuggingFaceInferenceAPIEmbeddings(
+                api_key=HUGGINGFACEHUB_API_TOKEN, model_name=model_id, api_url=TEI_EMBEDDING_ENDPOINT
+            )
         else:
             # create embeddings using local embedding model
             if logflag:
@@ -70,7 +91,14 @@ def check_health(self) -> bool:
         if logflag:
             logger.info("[ check health ] start to check health of milvus")
         try:
-            _ = self.client.client.list_collections()
+            client = Milvus(
+                embedding_function=self.embedder,
+                collection_name=COLLECTION_NAME,
+                connection_args={"uri": MILVUS_URI},
+                index_params=INDEX_PARAMS,
+                auto_id=True,
+            )
+            _ = client.client.list_collections()
             if logflag:
                 logger.info("[ check health ] Successfully connected to Milvus!")
             return True
@@ -89,21 +117,29 @@ async def invoke(self, input: EmbedDoc) -> list:
         if logflag:
             logger.info(input)
 
+        my_milvus = Milvus(
+            embedding_function=self.embedder,
+            collection_name=COLLECTION_NAME,
+            connection_args={"uri": MILVUS_URI},
+            index_params=INDEX_PARAMS,
+            auto_id=True,
+        )
+
         if input.search_type == "similarity":
-            search_res = await self.client.asimilarity_search_by_vector(embedding=input.embedding, k=input.k)
+            search_res = await my_milvus.asimilarity_search_by_vector(embedding=input.embedding, k=input.k)
         elif input.search_type == "similarity_distance_threshold":
             if input.distance_threshold is None:
                 raise ValueError("distance_threshold must be provided for " + "similarity_distance_threshold retriever")
-            search_res = await self.client.asimilarity_search_by_vector(
+            search_res = await my_milvus.asimilarity_search_by_vector(
                 embedding=input.embedding, k=input.k, distance_threshold=input.distance_threshold
             )
         elif input.search_type == "similarity_score_threshold":
-            docs_and_similarities = await self.client.asimilarity_search_with_relevance_scores(
+            docs_and_similarities = await my_milvus.asimilarity_search_with_relevance_scores(
                 query=input.text, k=input.k, score_threshold=input.score_threshold
             )
             search_res = [doc for doc, _ in docs_and_similarities]
         elif input.search_type == "mmr":
-            search_res = await self.client.amax_marginal_relevance_search(
+            search_res = await my_milvus.amax_marginal_relevance_search(
                 query=input.text, k=input.k, fetch_k=input.fetch_k, lambda_mult=input.lambda_mult
             )
 

comps/retrievers/src/integrations/opensearch.py

@@ -6,7 +6,8 @@
 from typing import Callable, List, Union
 
 import numpy as np
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from fastapi import HTTPException
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
 from langchain_community.vectorstores import OpenSearchVectorSearch
 from pydantic import conlist
 
@@ -15,6 +16,7 @@
 
 from .config import (
     EMBED_MODEL,
+    HUGGINGFACEHUB_API_TOKEN,
     OPENSEARCH_INDEX_NAME,
     OPENSEARCH_INITIAL_ADMIN_PASSWORD,
     OPENSEARCH_URL,
@@ -49,7 +51,22 @@ def _initialize_embedder(self):
             # create embeddings using TEI endpoint service
             if logflag:
                 logger.info(f"[ init embedder ] TEI_EMBEDDING_ENDPOINT:{TEI_EMBEDDING_ENDPOINT}")
-            embeddings = HuggingFaceHubEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
+            if not HUGGINGFACEHUB_API_TOKEN:
+                raise HTTPException(
+                    status_code=400,
+                    detail="You MUST offer the `HUGGINGFACEHUB_API_TOKEN` when using `TEI_EMBEDDING_ENDPOINT`.",
+                )
+            import requests
+
+            response = requests.get(TEI_EMBEDDING_ENDPOINT + "/info")
+            if response.status_code != 200:
+                raise HTTPException(
+                    status_code=400, detail=f"TEI embedding endpoint {TEI_EMBEDDING_ENDPOINT} is not available."
+                )
+            model_id = response.json()["model_id"]
+            embeddings = HuggingFaceInferenceAPIEmbeddings(
+                api_key=HUGGINGFACEHUB_API_TOKEN, model_name=model_id, api_url=TEI_EMBEDDING_ENDPOINT
+            )
         else:
             # create embeddings using local embedding model
             if logflag:

comps/retrievers/src/integrations/pgvector.py

@@ -4,12 +4,13 @@
 
 import os
 
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from fastapi import HTTPException
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
 from langchain_community.vectorstores import PGVector
 
 from comps import CustomLogger, EmbedDoc, OpeaComponent, OpeaComponentRegistry, ServiceType
 
-from .config import EMBED_MODEL, PG_CONNECTION_STRING, PG_INDEX_NAME, TEI_EMBEDDING_ENDPOINT
+from .config import EMBED_MODEL, HUGGINGFACEHUB_API_TOKEN, PG_CONNECTION_STRING, PG_INDEX_NAME, TEI_EMBEDDING_ENDPOINT
 
 logger = CustomLogger("pgvector_retrievers")
 logflag = os.getenv("LOGFLAG", False)
@@ -39,7 +40,22 @@ def _initialize_embedder(self):
             # create embeddings using TEI endpoint service
             if logflag:
                 logger.info(f"[ init embedder ] TEI_EMBEDDING_ENDPOINT:{TEI_EMBEDDING_ENDPOINT}")
-            embeddings = HuggingFaceHubEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
+            if not HUGGINGFACEHUB_API_TOKEN:
+                raise HTTPException(
+                    status_code=400,
+                    detail="You MUST offer the `HUGGINGFACEHUB_API_TOKEN` when using `TEI_EMBEDDING_ENDPOINT`.",
+                )
+            import requests
+
+            response = requests.get(TEI_EMBEDDING_ENDPOINT + "/info")
+            if response.status_code != 200:
+                raise HTTPException(
+                    status_code=400, detail=f"TEI embedding endpoint {TEI_EMBEDDING_ENDPOINT} is not available."
+                )
+            model_id = response.json()["model_id"]
+            embeddings = HuggingFaceInferenceAPIEmbeddings(
+                api_key=HUGGINGFACEHUB_API_TOKEN, model_name=model_id, api_url=TEI_EMBEDDING_ENDPOINT
+            )
         else:
             # create embeddings using local embedding model
             if logflag: