RAG Pipeline Targets#
Retrieval Augmented Generation (RAG) pipelines are built by combining NeMo Retriever and LLM. A retriever pipeline is used to retrieve relevant documents based on a query, and the LLM is used to generate answers based on the query and the retrieved documents. For more information, refer to RAG Evaluation Type.
Answer Evaluation#
NeMo Evaluator supports Answer Evaluation RAG pipelines. The rag pipeline is replaced by a cached_outputs field that contains pre-generated retrieved documents and pre-generated answers. For more information, refer to Using Custom Data.
To Create a RAG Answer Evaluation Target#
Choose one of the following options to create a RAG answer evaluation target.
import os
from nemo_microservices import NeMoMicroservices
# Initialize the client
client = NeMoMicroservices(
base_url=os.environ['EVALUATOR_BASE_URL']
)
# Create a RAG answer evaluation target
client.evaluation.targets.create(
type="rag",
name="my-target-rag-1",
namespace="my-organization",
rag={
"cached_outputs": {
"files_url": "hf://datasets/<my-dataset-namespace>/<my-dataset-name>/<my-dataset-file-path>"
}
}
)
print("RAG answer evaluation target created successfully")
curl -X "POST" "${EVALUATOR_BASE_URL}/evaluation/targets" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"type": "rag",
"name": "my-target-rag-1",
"namespace": "my-organization",
"rag": {
"cached_outputs": {
"files_url": "hf://datasets/<my-dataset-namespace>/<my-dataset-name>/<my-dataset-file-path>"
}
}
}'
Answer Generation and Evaluation#
NeMo Evaluator supports Answer Generation + Answer Evaluation RAG pipelines. The retriever pipeline is replaced by a cached_outputs field that contains pre-generated retrieved documents. For more information, refer to Using Custom Data.
To Create a RAG Answer Generation + Evaluation Target#
Choose one of the following options to create a RAG answer generation + evaluation target.
import os
from nemo_microservices import NeMoMicroservices
# Initialize the client
client = NeMoMicroservices(
base_url=os.environ['EVALUATOR_BASE_URL']
)
# Create a RAG answer generation + evaluation target
client.evaluation.targets.create(
type="rag",
name="my-target-rag-2",
namespace="my-organization",
rag={
"pipeline": {
"retriever": {
"cached_outputs": {
"files_url": "hf://datasets/<my-dataset-namespace>/<my-dataset-name>/<my-dataset-file-path>"
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
)
print("RAG answer generation + evaluation target created successfully")
curl -X "POST" "${EVALUATOR_BASE_URL}/evaluation/targets" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"type": "rag",
"name": "my-target-rag-2",
"namespace": "my-organization",
"rag": {
"pipeline": {
"retriever": {
"cached_outputs": {
"files_url": "hf://datasets/<my-dataset-namespace>/<my-dataset-name>/<my-dataset-file-path>"
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
}'
Retrieval#
Embedding + Answer Generation and Evaluation#
NeMo Evaluator supports Retrieval + Answer Generation + Answer Evaluation RAG pipelines. The rag pipeline field contains a retriever pipeline and a model.
To Create a RAG Embedding + Answer Generation + Evaluation Target#
Choose one of the following options to create a RAG embedding + answer generation + evaluation target.
import os
from nemo_microservices import NeMoMicroservices
# Initialize the client
client = NeMoMicroservices(
base_url=os.environ['EVALUATOR_BASE_URL']
)
# Create a RAG embedding + answer generation + evaluation target
client.evaluation.targets.create(
type="rag",
name="my-target-rag-3",
namespace="my-organization",
rag={
"pipeline": {
"retriever": {
"pipeline": {
"query_embedding_model": {
"api_endpoint": {
"url": "<my-query-embedding-url>",
"model_id": "<my-query-embedding-model>"
}
},
"index_embedding_model": {
"api_endpoint": {
"url": "<my-index-embedding-url>",
"model_id": "<my-index-embedding-model>"
}
},
"top_k": 3
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
)
print("RAG embedding + generation + evaluation target created successfully")
curl -X "POST" "${EVALUATOR_BASE_URL}/evaluation/targets" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"type": "rag",
"name": "my-target-rag-3",
"namespace": "my-organization",
"rag": {
"pipeline": {
"retriever": {
"pipeline": {
"query_embedding_model": {
"api_endpoint": {
"url": "<my-query-embedding-url>",
"model_id": "<my-query-embedding-model>"
}
},
"index_embedding_model": {
"api_endpoint": {
"url": "<my-index-embedding-url>",
"model_id": "<my-index-embedding-model>"
}
},
"top_k": 3
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
}'
Embedding, Reranking + Answer Generation and Evaluation#
NeMo Evaluator supports Retrieval + Answer Generation + Answer Evaluation RAG pipelines. The rag pipeline field contains a retriever pipeline and a model.
To Create a RAG Embedding + Reranking + Answer Generation + Evaluation Target#
Choose one of the following options to create a RAG embedding + reranking + answer generation + evaluation target.
import os
from nemo_microservices import NeMoMicroservices
# Initialize the client
client = NeMoMicroservices(
base_url=os.environ['EVALUATOR_BASE_URL']
)
# Create a RAG embedding + reranking + answer generation + evaluation target
client.evaluation.targets.create(
type="rag",
name="my-target-rag-4",
namespace="my-organization",
rag={
"pipeline": {
"retriever": {
"pipeline": {
"query_embedding_model": {
"api_endpoint": {
"url": "<my-query-embedding-url>",
"model_id": "<my-query-embedding-model>"
}
},
"index_embedding_model": {
"api_endpoint": {
"url": "<my-index-embedding-url>",
"model_id": "<my-index-embedding-model>"
}
},
"reranker_model": {
"api_endpoint": {
"url": "<my-ranker-url>",
"model_id": "<my-ranker-model>"
}
},
"top_k": 3
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
)
print("RAG embedding + reranking + generation + evaluation target created successfully")
curl -X "POST" "${EVALUATOR_BASE_URL}/evaluation/targets" \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"type": "rag",
"name": "my-target-rag-4",
"namespace": "my-organization",
"rag": {
"pipeline": {
"retriever": {
"pipeline": {
"query_embedding_model": {
"api_endpoint": {
"url": "<my-query-embedding-url>",
"model_id": "<my-query-embedding-model>"
}
},
"index_embedding_model": {
"api_endpoint": {
"url": "<my-index-embedding-url>",
"model_id": "<my-index-embedding-model>"
}
},
"reranker_model": {
"api_endpoint": {
"url": "<my-ranker-url>",
"model_id": "<my-ranker-model>"
}
},
"top_k": 3
}
},
"model": {
"api_endpoint": {
"url": "<my-nim-deployment-base-url>/chat/completions",
"model_id": "<my-model>"
}
}
}
}
}'