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c8c3b756b0
### What problem does this PR solve?
This PR adds the support for latest OpenSearch2.19.1 as the store engine
& search engine option for RAGFlow.
### Main Benefit
1. OpenSearch2.19.1 is licensed under the [Apache v2.0 License] which is
much better than Elasticsearch
2. For search, OpenSearch2.19.1 supports full-text
search、vector_search、hybrid_search those are similar with Elasticsearch
on schema
3. For store, OpenSearch2.19.1 stores text、vector those are quite
simliar with Elasticsearch on schema
### Changes
- Support opensearch_python_connetor. I make a lot of adaptions since
the schema and api/method between ES and Opensearch differs in many
ways(especially the knn_search has a significant gap) :
rag/utils/opensearch_coon.py
- Support static config adaptions by changing:
conf/service_conf.yaml、api/settings.py、rag/settings.py
- Supprt some store&search schema changes between OpenSearch and ES:
conf/os_mapping.json
- Support OpenSearch python sdk : pyproject.toml
- Support docker config for OpenSearch2.19.1 :
docker/.env、docker/docker-compose-base.yml、docker/service_conf.yaml.template
### How to use
- I didn't change the priority that ES as the default doc/search engine.
Only if in docker/.env , we set DOC_ENGINE=${DOC_ENGINE:-opensearch}, it
will work.
### Others
Our team tested a lot of docs in our environment by using OpenSearch as
the vector database ,it works very well.
All the conifg for OpenSearch is necessary.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Yongteng Lei <yongtengrey@outlook.com>
Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import logging
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from api.utils import get_base_config, decrypt_database_config
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from api.utils.file_utils import get_project_base_directory
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# Server
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RAG_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
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# Get storage type and document engine from system environment variables
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STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO')
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DOC_ENGINE = os.getenv('DOC_ENGINE', 'elasticsearch')
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ES = {}
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INFINITY = {}
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AZURE = {}
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S3 = {}
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MINIO = {}
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OSS = {}
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OS = {}
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# Initialize the selected configuration data based on environment variables to solve the problem of initialization errors due to lack of configuration
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if DOC_ENGINE == 'elasticsearch':
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ES = get_base_config("es", {})
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elif DOC_ENGINE == 'opensearch':
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OS = get_base_config("os", {})
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elif DOC_ENGINE == 'infinity':
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INFINITY = get_base_config("infinity", {"uri": "infinity:23817"})
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if STORAGE_IMPL_TYPE in ['AZURE_SPN', 'AZURE_SAS']:
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AZURE = get_base_config("azure", {})
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elif STORAGE_IMPL_TYPE == 'AWS_S3':
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S3 = get_base_config("s3", {})
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elif STORAGE_IMPL_TYPE == 'MINIO':
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MINIO = decrypt_database_config(name="minio")
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elif STORAGE_IMPL_TYPE == 'OSS':
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OSS = get_base_config("oss", {})
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try:
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REDIS = decrypt_database_config(name="redis")
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except Exception:
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REDIS = {}
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pass
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DOC_MAXIMUM_SIZE = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
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SVR_QUEUE_NAME = "rag_flow_svr_queue"
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SVR_CONSUMER_GROUP_NAME = "rag_flow_svr_task_broker"
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PAGERANK_FLD = "pagerank_fea"
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TAG_FLD = "tag_feas"
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PARALLEL_DEVICES = None
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try:
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import torch.cuda
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PARALLEL_DEVICES = torch.cuda.device_count()
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logging.info(f"found {PARALLEL_DEVICES} gpus")
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except Exception:
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logging.info("can't import package 'torch'")
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def print_rag_settings():
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logging.info(f"MAX_CONTENT_LENGTH: {DOC_MAXIMUM_SIZE}")
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logging.info(f"MAX_FILE_COUNT_PER_USER: {int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))}")
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def get_svr_queue_name(priority: int) -> str:
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if priority == 0:
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return SVR_QUEUE_NAME
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return f"{SVR_QUEUE_NAME}_{priority}"
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def get_svr_queue_names():
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return [get_svr_queue_name(priority) for priority in [1, 0]]
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