mirror of
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Feat: Adds OpenSearch2.19.1 as the vector_database support (#7140)
### 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>
This commit is contained in:
pyyuhao
GitHub
parent
9a8dda8fc7
commit
c8c3b756b0
@ -19,6 +19,7 @@ from enum import IntEnum, Enum
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import json
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import json
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import rag.utils.es_conn
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import rag.utils.es_conn
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import rag.utils.infinity_conn
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import rag.utils.infinity_conn
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import rag.utils.opensearch_coon
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import rag.utils
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import rag.utils
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from rag.nlp import search
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from rag.nlp import search
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@ -132,11 +133,14 @@ def init_settings():
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global DOC_ENGINE, docStoreConn, retrievaler, kg_retrievaler
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global DOC_ENGINE, docStoreConn, retrievaler, kg_retrievaler
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DOC_ENGINE = os.environ.get('DOC_ENGINE', "elasticsearch")
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DOC_ENGINE = os.environ.get('DOC_ENGINE', "elasticsearch")
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# DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch")
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lower_case_doc_engine = DOC_ENGINE.lower()
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lower_case_doc_engine = DOC_ENGINE.lower()
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if lower_case_doc_engine == "elasticsearch":
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if lower_case_doc_engine == "elasticsearch":
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docStoreConn = rag.utils.es_conn.ESConnection()
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docStoreConn = rag.utils.es_conn.ESConnection()
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elif lower_case_doc_engine == "infinity":
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elif lower_case_doc_engine == "infinity":
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docStoreConn = rag.utils.infinity_conn.InfinityConnection()
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docStoreConn = rag.utils.infinity_conn.InfinityConnection()
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elif lower_case_doc_engine == "opensearch":
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docStoreConn = rag.utils.opensearch_coon.OSConnection()
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else:
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else:
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raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
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raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
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213
conf/os_mapping.json
Normal file
213
conf/os_mapping.json
Normal file
@ -0,0 +1,213 @@
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{
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"settings": {
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"index": {
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"number_of_shards": 2,
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"number_of_replicas": 0,
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"refresh_interval": "1000ms",
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"knn": true,
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"similarity": {
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"scripted_sim": {
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"type": "scripted",
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"script": {
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"source": "double idf = Math.log(1+(field.docCount-term.docFreq+0.5)/(term.docFreq + 0.5))/Math.log(1+((field.docCount-0.5)/1.5)); return query.boost * idf * Math.min(doc.freq, 1);"
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}
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}
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}
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}
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},
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"mappings": {
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"properties": {
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"lat_lon": {
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"type": "geo_point",
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"store": "true"
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}
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},
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"date_detection": "true",
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"dynamic_templates": [
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{
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"int": {
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"match": "*_int",
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"mapping": {
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"type": "integer",
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"store": "true"
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}
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}
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},
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{
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"ulong": {
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"match": "*_ulong",
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"mapping": {
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"type": "unsigned_long",
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"store": "true"
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}
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}
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},
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{
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"long": {
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"match": "*_long",
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"mapping": {
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"type": "long",
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"store": "true"
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}
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}
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},
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{
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"short": {
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"match": "*_short",
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"mapping": {
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"type": "short",
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"store": "true"
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}
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}
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},
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{
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"numeric": {
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"match": "*_flt",
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"mapping": {
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"type": "float",
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"store": true
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}
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}
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},
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{
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"tks": {
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"match": "*_tks",
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"mapping": {
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"type": "text",
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"similarity": "scripted_sim",
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"analyzer": "whitespace",
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"store": true
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}
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}
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},
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{
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"ltks": {
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"match": "*_ltks",
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"mapping": {
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"type": "text",
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"analyzer": "whitespace",
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"store": true
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}
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}
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},
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{
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"kwd": {
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"match_pattern": "regex",
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"match": "^(.*_(kwd|id|ids|uid|uids)|uid)$",
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"mapping": {
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"type": "keyword",
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"similarity": "boolean",
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"store": true
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}
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}
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},
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{
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"dt": {
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"match_pattern": "regex",
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"match": "^.*(_dt|_time|_at)$",
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"mapping": {
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"type": "date",
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"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||yyyy-MM-dd_HH:mm:ss",
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"store": true
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}
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}
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},
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{
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"nested": {
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"match": "*_nst",
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"mapping": {
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"type": "nested"
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}
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}
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},
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{
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"object": {
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"match": "*_obj",
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"mapping": {
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"type": "object",
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"dynamic": "true"
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}
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}
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},
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{
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"string": {
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"match_pattern": "regex",
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"match": "^.*_(with_weight|list)$",
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"mapping": {
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"type": "text",
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"index": "false",
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"store": true
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}
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}
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},
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{
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"rank_feature": {
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"match": "*_fea",
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"mapping": {
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"type": "rank_feature"
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}
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}
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},
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{
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"rank_features": {
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"match": "*_feas",
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"mapping": {
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"type": "rank_features"
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}
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}
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},
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{
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"knn_vector": {
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"match": "*_512_vec",
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"mapping": {
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"type": "knn_vector",
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"index": true,
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"space_type": "cosinesimil",
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"dimension": 512
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}
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}
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},
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{
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"knn_vector": {
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"match": "*_768_vec",
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"mapping": {
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"type": "knn_vector",
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"index": true,
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"space_type": "cosinesimil",
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"dimension": 768
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}
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}
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},
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{
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"knn_vector": {
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"match": "*_1024_vec",
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"mapping": {
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"type": "knn_vector",
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"index": true,
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"space_type": "cosinesimil",
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"dimension": 1024
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}
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}
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},
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{
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"knn_vector": {
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"match": "*_1536_vec",
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"mapping": {
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"type": "knn_vector",
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"index": true,
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"space_type": "cosinesimil",
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"dimension": 1536
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}
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}
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},
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{
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"binary": {
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"match": "*_bin",
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"mapping": {
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"type": "binary"
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}
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}
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}
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]
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}
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}
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@ -17,6 +17,10 @@ es:
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hosts: 'http://localhost:1200'
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hosts: 'http://localhost:1200'
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username: 'elastic'
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username: 'elastic'
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password: 'infini_rag_flow'
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password: 'infini_rag_flow'
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os:
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hosts: 'http://localhost:1201'
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username: 'admin'
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password: 'infini_rag_flow_OS_01'
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infinity:
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infinity:
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uri: 'localhost:23817'
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uri: 'localhost:23817'
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db_name: 'default_db'
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db_name: 'default_db'
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12
docker/.env
12
docker/.env
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# Available options:
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# Available options:
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# - `elasticsearch` (default)
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# - `elasticsearch` (default)
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# - `infinity` (https://github.com/infiniflow/infinity)
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# - `infinity` (https://github.com/infiniflow/infinity)
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# - `opensearch` (https://github.com/opensearch-project/OpenSearch)
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DOC_ENGINE=${DOC_ENGINE:-elasticsearch}
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DOC_ENGINE=${DOC_ENGINE:-elasticsearch}
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# ------------------------------
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# ------------------------------
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# docker env var for specifying vector db type at startup
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# docker env var for specifying vector db type at startup
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# (based on the vector db type, the corresponding docker
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# (based on the vector db type, the corresponding docker
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@ -24,6 +26,16 @@ ES_PORT=1200
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# The password for Elasticsearch.
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# The password for Elasticsearch.
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ELASTIC_PASSWORD=infini_rag_flow
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ELASTIC_PASSWORD=infini_rag_flow
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# the hostname where OpenSearch service is exposed, set it not the same as elasticsearch
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OS_PORT=1201
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# The hostname where the OpenSearch service is exposed
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OS_HOST=opensearch01
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# The password for OpenSearch.
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# At least one uppercase letter, one lowercase letter, one digit, and one special character
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OPENSEARCH_PASSWORD=infini_rag_flow_OS_01
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# The port used to expose the Kibana service to the host machine,
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# The port used to expose the Kibana service to the host machine,
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# allowing EXTERNAL access to the service running inside the Docker container.
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# allowing EXTERNAL access to the service running inside the Docker container.
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KIBANA_PORT=6601
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KIBANA_PORT=6601
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@ -35,6 +35,44 @@ services:
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- ragflow
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- ragflow
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restart: on-failure
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restart: on-failure
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opensearch01:
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container_name: ragflow-opensearch-01
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profiles:
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- opensearch
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image: hub.icert.top/opensearchproject/opensearch:2.19.1
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volumes:
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- osdata01:/usr/share/opensearch/data
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ports:
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- ${OS_PORT}:9201
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env_file: .env
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environment:
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- node.name=opensearch01
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- OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD}
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- OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD}
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- bootstrap.memory_lock=false
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- discovery.type=single-node
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- plugins.security.disabled=false
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- plugins.security.ssl.http.enabled=false
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- plugins.security.ssl.transport.enabled=true
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- cluster.routing.allocation.disk.watermark.low=5gb
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- cluster.routing.allocation.disk.watermark.high=3gb
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- cluster.routing.allocation.disk.watermark.flood_stage=2gb
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- TZ=${TIMEZONE}
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- http.port=9201
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mem_limit: ${MEM_LIMIT}
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ulimits:
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memlock:
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soft: -1
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hard: -1
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healthcheck:
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test: ["CMD-SHELL", "curl http://localhost:9201"]
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interval: 10s
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timeout: 10s
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retries: 120
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networks:
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- ragflow
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restart: on-failure
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infinity:
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infinity:
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container_name: ragflow-infinity
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container_name: ragflow-infinity
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profiles:
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profiles:
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@ -133,6 +171,8 @@ services:
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volumes:
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volumes:
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esdata01:
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esdata01:
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driver: local
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driver: local
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osdata01:
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driver: local
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infinity_data:
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infinity_data:
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driver: local
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driver: local
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mysql_data:
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mysql_data:
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@ -17,6 +17,10 @@ es:
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hosts: 'http://${ES_HOST:-es01}:9200'
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hosts: 'http://${ES_HOST:-es01}:9200'
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username: '${ES_USER:-elastic}'
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username: '${ES_USER:-elastic}'
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password: '${ELASTIC_PASSWORD:-infini_rag_flow}'
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password: '${ELASTIC_PASSWORD:-infini_rag_flow}'
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os:
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hosts: 'http://${OS_HOST:-opensearch01}:9201'
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username: '${OS_USER:-admin}'
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password: '${OPENSEARCHH_PASSWORD:-infini_rag_flow_OS_01}'
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infinity:
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infinity:
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uri: '${INFINITY_HOST:-infinity}:23817'
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uri: '${INFINITY_HOST:-infinity}:23817'
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db_name: 'default_db'
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db_name: 'default_db'
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@ -127,6 +127,7 @@ dependencies = [
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"langfuse>=2.60.0",
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"langfuse>=2.60.0",
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"debugpy>=1.8.13",
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"debugpy>=1.8.13",
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"mcp>=1.6.0",
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"mcp>=1.6.0",
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"opensearch-py==2.7.1"
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]
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]
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[project.optional-dependencies]
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[project.optional-dependencies]
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@ -31,10 +31,13 @@ AZURE = {}
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S3 = {}
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S3 = {}
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MINIO = {}
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MINIO = {}
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OSS = {}
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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
|
# Initialize the selected configuration data based on environment variables to solve the problem of initialization errors due to lack of configuration
|
||||||
if DOC_ENGINE == 'elasticsearch':
|
if DOC_ENGINE == 'elasticsearch':
|
||||||
ES = get_base_config("es", {})
|
ES = get_base_config("es", {})
|
||||||
|
elif DOC_ENGINE == 'opensearch':
|
||||||
|
OS = get_base_config("os", {})
|
||||||
elif DOC_ENGINE == 'infinity':
|
elif DOC_ENGINE == 'infinity':
|
||||||
INFINITY = get_base_config("infinity", {"uri": "infinity:23817"})
|
INFINITY = get_base_config("infinity", {"uri": "infinity:23817"})
|
||||||
|
|
||||||
|
|||||||
558
rag/utils/opensearch_coon.py
Normal file
558
rag/utils/opensearch_coon.py
Normal file
@ -0,0 +1,558 @@
|
|||||||
|
#
|
||||||
|
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
#
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import re
|
||||||
|
import json
|
||||||
|
import time
|
||||||
|
import os
|
||||||
|
|
||||||
|
import copy
|
||||||
|
from opensearchpy import OpenSearch, NotFoundError
|
||||||
|
from opensearchpy import UpdateByQuery, Q, Search, Index
|
||||||
|
from opensearchpy import ConnectionTimeout
|
||||||
|
from rag import settings
|
||||||
|
from rag.settings import TAG_FLD, PAGERANK_FLD
|
||||||
|
from rag.utils import singleton
|
||||||
|
from api.utils.file_utils import get_project_base_directory
|
||||||
|
from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \
|
||||||
|
FusionExpr
|
||||||
|
from rag.nlp import is_english, rag_tokenizer
|
||||||
|
|
||||||
|
ATTEMPT_TIME = 2
|
||||||
|
|
||||||
|
logger = logging.getLogger('ragflow.opensearch_conn')
|
||||||
|
|
||||||
|
|
||||||
|
@singleton
|
||||||
|
class OSConnection(DocStoreConnection):
|
||||||
|
def __init__(self):
|
||||||
|
self.info = {}
|
||||||
|
logger.info(f"Use OpenSearch {settings.OS['hosts']} as the doc engine.")
|
||||||
|
for _ in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
self.os = OpenSearch(
|
||||||
|
settings.OS["hosts"].split(","),
|
||||||
|
http_auth=(settings.OS["username"], settings.OS[
|
||||||
|
"password"]) if "username" in settings.OS and "password" in settings.OS else None,
|
||||||
|
verify_certs=False,
|
||||||
|
timeout=600
|
||||||
|
)
|
||||||
|
if self.os:
|
||||||
|
self.info = self.os.info()
|
||||||
|
break
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"{str(e)}. Waiting OpenSearch {settings.OS['hosts']} to be healthy.")
|
||||||
|
time.sleep(5)
|
||||||
|
if not self.os.ping():
|
||||||
|
msg = f"OpenSearch {settings.OS['hosts']} is unhealthy in 120s."
|
||||||
|
logger.error(msg)
|
||||||
|
raise Exception(msg)
|
||||||
|
v = self.info.get("version", {"number": "2.18.0"})
|
||||||
|
v = v["number"].split(".")[0]
|
||||||
|
if int(v) < 2:
|
||||||
|
msg = f"OpenSearch version must be greater than or equal to 2, current version: {v}"
|
||||||
|
logger.error(msg)
|
||||||
|
raise Exception(msg)
|
||||||
|
fp_mapping = os.path.join(get_project_base_directory(), "conf", "os_mapping.json")
|
||||||
|
if not os.path.exists(fp_mapping):
|
||||||
|
msg = f"OpenSearch mapping file not found at {fp_mapping}"
|
||||||
|
logger.error(msg)
|
||||||
|
raise Exception(msg)
|
||||||
|
self.mapping = json.load(open(fp_mapping, "r"))
|
||||||
|
logger.info(f"OpenSearch {settings.OS['hosts']} is healthy.")
|
||||||
|
|
||||||
|
"""
|
||||||
|
Database operations
|
||||||
|
"""
|
||||||
|
|
||||||
|
def dbType(self) -> str:
|
||||||
|
return "opensearch"
|
||||||
|
|
||||||
|
def health(self) -> dict:
|
||||||
|
health_dict = dict(self.os.cluster.health())
|
||||||
|
health_dict["type"] = "opensearch"
|
||||||
|
return health_dict
|
||||||
|
|
||||||
|
"""
|
||||||
|
Table operations
|
||||||
|
"""
|
||||||
|
|
||||||
|
def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int):
|
||||||
|
if self.indexExist(indexName, knowledgebaseId):
|
||||||
|
return True
|
||||||
|
try:
|
||||||
|
from opensearchpy.client import IndicesClient
|
||||||
|
return IndicesClient(self.os).create(index=indexName,
|
||||||
|
body=self.mapping)
|
||||||
|
except Exception:
|
||||||
|
logger.exception("OSConnection.createIndex error %s" % (indexName))
|
||||||
|
|
||||||
|
def deleteIdx(self, indexName: str, knowledgebaseId: str):
|
||||||
|
if len(knowledgebaseId) > 0:
|
||||||
|
# The index need to be alive after any kb deletion since all kb under this tenant are in one index.
|
||||||
|
return
|
||||||
|
try:
|
||||||
|
self.os.indices.delete(index=indexName, allow_no_indices=True)
|
||||||
|
except NotFoundError:
|
||||||
|
pass
|
||||||
|
except Exception:
|
||||||
|
logger.exception("OSConnection.deleteIdx error %s" % (indexName))
|
||||||
|
|
||||||
|
def indexExist(self, indexName: str, knowledgebaseId: str = None) -> bool:
|
||||||
|
s = Index(indexName, self.os)
|
||||||
|
for i in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
return s.exists()
|
||||||
|
except Exception as e:
|
||||||
|
logger.exception("OSConnection.indexExist got exception")
|
||||||
|
if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0:
|
||||||
|
continue
|
||||||
|
break
|
||||||
|
return False
|
||||||
|
|
||||||
|
"""
|
||||||
|
CRUD operations
|
||||||
|
"""
|
||||||
|
|
||||||
|
def search(
|
||||||
|
self, selectFields: list[str],
|
||||||
|
highlightFields: list[str],
|
||||||
|
condition: dict,
|
||||||
|
matchExprs: list[MatchExpr],
|
||||||
|
orderBy: OrderByExpr,
|
||||||
|
offset: int,
|
||||||
|
limit: int,
|
||||||
|
indexNames: str | list[str],
|
||||||
|
knowledgebaseIds: list[str],
|
||||||
|
aggFields: list[str] = [],
|
||||||
|
rank_feature: dict | None = None
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
Refers to https://github.com/opensearch-project/opensearch-py/blob/main/guides/dsl.md
|
||||||
|
"""
|
||||||
|
use_knn = False
|
||||||
|
if isinstance(indexNames, str):
|
||||||
|
indexNames = indexNames.split(",")
|
||||||
|
assert isinstance(indexNames, list) and len(indexNames) > 0
|
||||||
|
assert "_id" not in condition
|
||||||
|
|
||||||
|
bqry = Q("bool", must=[])
|
||||||
|
condition["kb_id"] = knowledgebaseIds
|
||||||
|
for k, v in condition.items():
|
||||||
|
if k == "available_int":
|
||||||
|
if v == 0:
|
||||||
|
bqry.filter.append(Q("range", available_int={"lt": 1}))
|
||||||
|
else:
|
||||||
|
bqry.filter.append(
|
||||||
|
Q("bool", must_not=Q("range", available_int={"lt": 1})))
|
||||||
|
continue
|
||||||
|
if not v:
|
||||||
|
continue
|
||||||
|
if isinstance(v, list):
|
||||||
|
bqry.filter.append(Q("terms", **{k: v}))
|
||||||
|
elif isinstance(v, str) or isinstance(v, int):
|
||||||
|
bqry.filter.append(Q("term", **{k: v}))
|
||||||
|
else:
|
||||||
|
raise Exception(
|
||||||
|
f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
|
||||||
|
|
||||||
|
s = Search()
|
||||||
|
vector_similarity_weight = 0.5
|
||||||
|
for m in matchExprs:
|
||||||
|
if isinstance(m, FusionExpr) and m.method == "weighted_sum" and "weights" in m.fusion_params:
|
||||||
|
assert len(matchExprs) == 3 and isinstance(matchExprs[0], MatchTextExpr) and isinstance(matchExprs[1],
|
||||||
|
MatchDenseExpr) and isinstance(
|
||||||
|
matchExprs[2], FusionExpr)
|
||||||
|
weights = m.fusion_params["weights"]
|
||||||
|
vector_similarity_weight = float(weights.split(",")[1])
|
||||||
|
knn_query = {}
|
||||||
|
for m in matchExprs:
|
||||||
|
if isinstance(m, MatchTextExpr):
|
||||||
|
minimum_should_match = m.extra_options.get("minimum_should_match", 0.0)
|
||||||
|
if isinstance(minimum_should_match, float):
|
||||||
|
minimum_should_match = str(int(minimum_should_match * 100)) + "%"
|
||||||
|
bqry.must.append(Q("query_string", fields=m.fields,
|
||||||
|
type="best_fields", query=m.matching_text,
|
||||||
|
minimum_should_match=minimum_should_match,
|
||||||
|
boost=1))
|
||||||
|
bqry.boost = 1.0 - vector_similarity_weight
|
||||||
|
|
||||||
|
# Elasticsearch has the encapsulation of KNN_search in python sdk
|
||||||
|
# while the Python SDK for OpenSearch does not provide encapsulation for KNN_search,
|
||||||
|
# the following codes implement KNN_search in OpenSearch using DSL
|
||||||
|
# Besides, Opensearch's DSL for KNN_search query syntax differs from that in Elasticsearch, I also made some adaptions for it
|
||||||
|
elif isinstance(m, MatchDenseExpr):
|
||||||
|
assert (bqry is not None)
|
||||||
|
similarity = 0.0
|
||||||
|
if "similarity" in m.extra_options:
|
||||||
|
similarity = m.extra_options["similarity"]
|
||||||
|
use_knn = True
|
||||||
|
vector_column_name = m.vector_column_name
|
||||||
|
knn_query[vector_column_name] = {}
|
||||||
|
knn_query[vector_column_name]["vector"] = list(m.embedding_data)
|
||||||
|
knn_query[vector_column_name]["k"] = m.topn
|
||||||
|
knn_query[vector_column_name]["filter"] = bqry.to_dict()
|
||||||
|
knn_query[vector_column_name]["boost"] = similarity
|
||||||
|
|
||||||
|
if bqry and rank_feature:
|
||||||
|
for fld, sc in rank_feature.items():
|
||||||
|
if fld != PAGERANK_FLD:
|
||||||
|
fld = f"{TAG_FLD}.{fld}"
|
||||||
|
bqry.should.append(Q("rank_feature", field=fld, linear={}, boost=sc))
|
||||||
|
|
||||||
|
if bqry:
|
||||||
|
s = s.query(bqry)
|
||||||
|
for field in highlightFields:
|
||||||
|
s = s.highlight(field)
|
||||||
|
|
||||||
|
if orderBy:
|
||||||
|
orders = list()
|
||||||
|
for field, order in orderBy.fields:
|
||||||
|
order = "asc" if order == 0 else "desc"
|
||||||
|
if field in ["page_num_int", "top_int"]:
|
||||||
|
order_info = {"order": order, "unmapped_type": "float",
|
||||||
|
"mode": "avg", "numeric_type": "double"}
|
||||||
|
elif field.endswith("_int") or field.endswith("_flt"):
|
||||||
|
order_info = {"order": order, "unmapped_type": "float"}
|
||||||
|
else:
|
||||||
|
order_info = {"order": order, "unmapped_type": "text"}
|
||||||
|
orders.append({field: order_info})
|
||||||
|
s = s.sort(*orders)
|
||||||
|
|
||||||
|
for fld in aggFields:
|
||||||
|
s.aggs.bucket(f'aggs_{fld}', 'terms', field=fld, size=1000000)
|
||||||
|
|
||||||
|
if limit > 0:
|
||||||
|
s = s[offset:offset + limit]
|
||||||
|
q = s.to_dict()
|
||||||
|
logger.debug(f"OSConnection.search {str(indexNames)} query: " + json.dumps(q))
|
||||||
|
|
||||||
|
if use_knn:
|
||||||
|
del q["query"]
|
||||||
|
q["query"] = {"knn" : knn_query}
|
||||||
|
|
||||||
|
for i in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
res = self.os.search(index=indexNames,
|
||||||
|
body=q,
|
||||||
|
timeout=600,
|
||||||
|
# search_type="dfs_query_then_fetch",
|
||||||
|
track_total_hits=True,
|
||||||
|
_source=True)
|
||||||
|
if str(res.get("timed_out", "")).lower() == "true":
|
||||||
|
raise Exception("OpenSearch Timeout.")
|
||||||
|
logger.debug(f"OSConnection.search {str(indexNames)} res: " + str(res))
|
||||||
|
return res
|
||||||
|
except Exception as e:
|
||||||
|
logger.exception(f"OSConnection.search {str(indexNames)} query: " + str(q))
|
||||||
|
if str(e).find("Timeout") > 0:
|
||||||
|
continue
|
||||||
|
raise e
|
||||||
|
logger.error("OSConnection.search timeout for 3 times!")
|
||||||
|
raise Exception("OSConnection.search timeout.")
|
||||||
|
|
||||||
|
def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
|
||||||
|
for i in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
res = self.os.get(index=(indexName),
|
||||||
|
id=chunkId, source=True, )
|
||||||
|
if str(res.get("timed_out", "")).lower() == "true":
|
||||||
|
raise Exception("Es Timeout.")
|
||||||
|
chunk = res["_source"]
|
||||||
|
chunk["id"] = chunkId
|
||||||
|
return chunk
|
||||||
|
except NotFoundError:
|
||||||
|
return None
|
||||||
|
except Exception as e:
|
||||||
|
logger.exception(f"OSConnection.get({chunkId}) got exception")
|
||||||
|
if str(e).find("Timeout") > 0:
|
||||||
|
continue
|
||||||
|
raise e
|
||||||
|
logger.error("OSConnection.get timeout for 3 times!")
|
||||||
|
raise Exception("OSConnection.get timeout.")
|
||||||
|
|
||||||
|
def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]:
|
||||||
|
# Refers to https://opensearch.org/docs/latest/api-reference/document-apis/bulk/
|
||||||
|
operations = []
|
||||||
|
for d in documents:
|
||||||
|
assert "_id" not in d
|
||||||
|
assert "id" in d
|
||||||
|
d_copy = copy.deepcopy(d)
|
||||||
|
meta_id = d_copy.pop("id", "")
|
||||||
|
operations.append(
|
||||||
|
{"index": {"_index": indexName, "_id": meta_id}})
|
||||||
|
operations.append(d_copy)
|
||||||
|
|
||||||
|
res = []
|
||||||
|
for _ in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
res = []
|
||||||
|
r = self.os.bulk(index=(indexName), body=operations,
|
||||||
|
refresh=False, timeout=60)
|
||||||
|
if re.search(r"False", str(r["errors"]), re.IGNORECASE):
|
||||||
|
return res
|
||||||
|
|
||||||
|
for item in r["items"]:
|
||||||
|
for action in ["create", "delete", "index", "update"]:
|
||||||
|
if action in item and "error" in item[action]:
|
||||||
|
res.append(str(item[action]["_id"]) + ":" + str(item[action]["error"]))
|
||||||
|
return res
|
||||||
|
except Exception as e:
|
||||||
|
res.append(str(e))
|
||||||
|
logger.warning("OSConnection.insert got exception: " + str(e))
|
||||||
|
res = []
|
||||||
|
if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE):
|
||||||
|
res.append(str(e))
|
||||||
|
time.sleep(3)
|
||||||
|
continue
|
||||||
|
return res
|
||||||
|
|
||||||
|
def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
|
||||||
|
doc = copy.deepcopy(newValue)
|
||||||
|
doc.pop("id", None)
|
||||||
|
if "id" in condition and isinstance(condition["id"], str):
|
||||||
|
# update specific single document
|
||||||
|
chunkId = condition["id"]
|
||||||
|
for i in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
self.os.update(index=indexName, id=chunkId, doc=doc)
|
||||||
|
return True
|
||||||
|
except Exception as e:
|
||||||
|
logger.exception(
|
||||||
|
f"OSConnection.update(index={indexName}, id={id}, doc={json.dumps(condition, ensure_ascii=False)}) got exception")
|
||||||
|
if re.search(r"(timeout|connection)", str(e).lower()):
|
||||||
|
continue
|
||||||
|
break
|
||||||
|
return False
|
||||||
|
|
||||||
|
# update unspecific maybe-multiple documents
|
||||||
|
bqry = Q("bool")
|
||||||
|
for k, v in condition.items():
|
||||||
|
if not isinstance(k, str) or not v:
|
||||||
|
continue
|
||||||
|
if k == "exists":
|
||||||
|
bqry.filter.append(Q("exists", field=v))
|
||||||
|
continue
|
||||||
|
if isinstance(v, list):
|
||||||
|
bqry.filter.append(Q("terms", **{k: v}))
|
||||||
|
elif isinstance(v, str) or isinstance(v, int):
|
||||||
|
bqry.filter.append(Q("term", **{k: v}))
|
||||||
|
else:
|
||||||
|
raise Exception(
|
||||||
|
f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.")
|
||||||
|
scripts = []
|
||||||
|
params = {}
|
||||||
|
for k, v in newValue.items():
|
||||||
|
if k == "remove":
|
||||||
|
if isinstance(v, str):
|
||||||
|
scripts.append(f"ctx._source.remove('{v}');")
|
||||||
|
if isinstance(v, dict):
|
||||||
|
for kk, vv in v.items():
|
||||||
|
scripts.append(f"int i=ctx._source.{kk}.indexOf(params.p_{kk});ctx._source.{kk}.remove(i);")
|
||||||
|
params[f"p_{kk}"] = vv
|
||||||
|
continue
|
||||||
|
if k == "add":
|
||||||
|
if isinstance(v, dict):
|
||||||
|
for kk, vv in v.items():
|
||||||
|
scripts.append(f"ctx._source.{kk}.add(params.pp_{kk});")
|
||||||
|
params[f"pp_{kk}"] = vv.strip()
|
||||||
|
continue
|
||||||
|
if (not isinstance(k, str) or not v) and k != "available_int":
|
||||||
|
continue
|
||||||
|
if isinstance(v, str):
|
||||||
|
v = re.sub(r"(['\n\r]|\\.)", " ", v)
|
||||||
|
params[f"pp_{k}"] = v
|
||||||
|
scripts.append(f"ctx._source.{k}=params.pp_{k};")
|
||||||
|
elif isinstance(v, int) or isinstance(v, float):
|
||||||
|
scripts.append(f"ctx._source.{k}={v};")
|
||||||
|
elif isinstance(v, list):
|
||||||
|
scripts.append(f"ctx._source.{k}=params.pp_{k};")
|
||||||
|
params[f"pp_{k}"] = json.dumps(v, ensure_ascii=False)
|
||||||
|
else:
|
||||||
|
raise Exception(
|
||||||
|
f"newValue `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str.")
|
||||||
|
ubq = UpdateByQuery(
|
||||||
|
index=indexName).using(
|
||||||
|
self.os).query(bqry)
|
||||||
|
ubq = ubq.script(source="".join(scripts), params=params)
|
||||||
|
ubq = ubq.params(refresh=True)
|
||||||
|
ubq = ubq.params(slices=5)
|
||||||
|
ubq = ubq.params(conflicts="proceed")
|
||||||
|
|
||||||
|
for _ in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
_ = ubq.execute()
|
||||||
|
return True
|
||||||
|
except Exception as e:
|
||||||
|
logger.error("OSConnection.update got exception: " + str(e) + "\n".join(scripts))
|
||||||
|
if re.search(r"(timeout|connection|conflict)", str(e).lower()):
|
||||||
|
continue
|
||||||
|
break
|
||||||
|
return False
|
||||||
|
|
||||||
|
def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int:
|
||||||
|
qry = None
|
||||||
|
assert "_id" not in condition
|
||||||
|
if "id" in condition:
|
||||||
|
chunk_ids = condition["id"]
|
||||||
|
if not isinstance(chunk_ids, list):
|
||||||
|
chunk_ids = [chunk_ids]
|
||||||
|
qry = Q("ids", values=chunk_ids)
|
||||||
|
else:
|
||||||
|
qry = Q("bool")
|
||||||
|
for k, v in condition.items():
|
||||||
|
if k == "exists":
|
||||||
|
qry.filter.append(Q("exists", field=v))
|
||||||
|
|
||||||
|
elif k == "must_not":
|
||||||
|
if isinstance(v, dict):
|
||||||
|
for kk, vv in v.items():
|
||||||
|
if kk == "exists":
|
||||||
|
qry.must_not.append(Q("exists", field=vv))
|
||||||
|
|
||||||
|
elif isinstance(v, list):
|
||||||
|
qry.must.append(Q("terms", **{k: v}))
|
||||||
|
elif isinstance(v, str) or isinstance(v, int):
|
||||||
|
qry.must.append(Q("term", **{k: v}))
|
||||||
|
else:
|
||||||
|
raise Exception("Condition value must be int, str or list.")
|
||||||
|
logger.debug("OSConnection.delete query: " + json.dumps(qry.to_dict()))
|
||||||
|
for _ in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
#print(Search().query(qry).to_dict(), flush=True)
|
||||||
|
res = self.os.delete_by_query(
|
||||||
|
index=indexName,
|
||||||
|
body=Search().query(qry).to_dict(),
|
||||||
|
refresh=True)
|
||||||
|
return res["deleted"]
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning("OSConnection.delete got exception: " + str(e))
|
||||||
|
if re.search(r"(timeout|connection)", str(e).lower()):
|
||||||
|
time.sleep(3)
|
||||||
|
continue
|
||||||
|
if re.search(r"(not_found)", str(e), re.IGNORECASE):
|
||||||
|
return 0
|
||||||
|
return 0
|
||||||
|
|
||||||
|
"""
|
||||||
|
Helper functions for search result
|
||||||
|
"""
|
||||||
|
|
||||||
|
def getTotal(self, res):
|
||||||
|
if isinstance(res["hits"]["total"], type({})):
|
||||||
|
return res["hits"]["total"]["value"]
|
||||||
|
return res["hits"]["total"]
|
||||||
|
|
||||||
|
def getChunkIds(self, res):
|
||||||
|
return [d["_id"] for d in res["hits"]["hits"]]
|
||||||
|
|
||||||
|
def __getSource(self, res):
|
||||||
|
rr = []
|
||||||
|
for d in res["hits"]["hits"]:
|
||||||
|
d["_source"]["id"] = d["_id"]
|
||||||
|
d["_source"]["_score"] = d["_score"]
|
||||||
|
rr.append(d["_source"])
|
||||||
|
return rr
|
||||||
|
|
||||||
|
def getFields(self, res, fields: list[str]) -> dict[str, dict]:
|
||||||
|
res_fields = {}
|
||||||
|
if not fields:
|
||||||
|
return {}
|
||||||
|
for d in self.__getSource(res):
|
||||||
|
m = {n: d.get(n) for n in fields if d.get(n) is not None}
|
||||||
|
for n, v in m.items():
|
||||||
|
if isinstance(v, list):
|
||||||
|
m[n] = v
|
||||||
|
continue
|
||||||
|
if not isinstance(v, str):
|
||||||
|
m[n] = str(m[n])
|
||||||
|
# if n.find("tks") > 0:
|
||||||
|
# m[n] = rmSpace(m[n])
|
||||||
|
|
||||||
|
if m:
|
||||||
|
res_fields[d["id"]] = m
|
||||||
|
return res_fields
|
||||||
|
|
||||||
|
def getHighlight(self, res, keywords: list[str], fieldnm: str):
|
||||||
|
ans = {}
|
||||||
|
for d in res["hits"]["hits"]:
|
||||||
|
hlts = d.get("highlight")
|
||||||
|
if not hlts:
|
||||||
|
continue
|
||||||
|
txt = "...".join([a for a in list(hlts.items())[0][1]])
|
||||||
|
if not is_english(txt.split()):
|
||||||
|
ans[d["_id"]] = txt
|
||||||
|
continue
|
||||||
|
|
||||||
|
txt = d["_source"][fieldnm]
|
||||||
|
txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
|
||||||
|
txts = []
|
||||||
|
for t in re.split(r"[.?!;\n]", txt):
|
||||||
|
for w in keywords:
|
||||||
|
t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), r"\1<em>\2</em>\3", t,
|
||||||
|
flags=re.IGNORECASE | re.MULTILINE)
|
||||||
|
if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
|
||||||
|
continue
|
||||||
|
txts.append(t)
|
||||||
|
ans[d["_id"]] = "...".join(txts) if txts else "...".join([a for a in list(hlts.items())[0][1]])
|
||||||
|
|
||||||
|
return ans
|
||||||
|
|
||||||
|
def getAggregation(self, res, fieldnm: str):
|
||||||
|
agg_field = "aggs_" + fieldnm
|
||||||
|
if "aggregations" not in res or agg_field not in res["aggregations"]:
|
||||||
|
return list()
|
||||||
|
bkts = res["aggregations"][agg_field]["buckets"]
|
||||||
|
return [(b["key"], b["doc_count"]) for b in bkts]
|
||||||
|
|
||||||
|
"""
|
||||||
|
SQL
|
||||||
|
"""
|
||||||
|
|
||||||
|
def sql(self, sql: str, fetch_size: int, format: str):
|
||||||
|
logger.debug(f"OSConnection.sql get sql: {sql}")
|
||||||
|
sql = re.sub(r"[ `]+", " ", sql)
|
||||||
|
sql = sql.replace("%", "")
|
||||||
|
replaces = []
|
||||||
|
for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql):
|
||||||
|
fld, v = r.group(1), r.group(3)
|
||||||
|
match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format(
|
||||||
|
fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v)))
|
||||||
|
replaces.append(
|
||||||
|
("{}{}'{}'".format(
|
||||||
|
r.group(1),
|
||||||
|
r.group(2),
|
||||||
|
r.group(3)),
|
||||||
|
match))
|
||||||
|
|
||||||
|
for p, r in replaces:
|
||||||
|
sql = sql.replace(p, r, 1)
|
||||||
|
logger.debug(f"OSConnection.sql to os: {sql}")
|
||||||
|
|
||||||
|
for i in range(ATTEMPT_TIME):
|
||||||
|
try:
|
||||||
|
res = self.os.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format,
|
||||||
|
request_timeout="2s")
|
||||||
|
return res
|
||||||
|
except ConnectionTimeout:
|
||||||
|
logger.exception("OSConnection.sql timeout")
|
||||||
|
continue
|
||||||
|
except Exception:
|
||||||
|
logger.exception("OSConnection.sql got exception")
|
||||||
|
return None
|
||||||
|
logger.error("OSConnection.sql timeout for 3 times!")
|
||||||
|
return None
|
||||||
26
uv.lock
generated
26
uv.lock
generated
@ -1324,6 +1324,14 @@ wheels = [
|
|||||||
{ url = "https://mirrors.aliyun.com/pypi/packages/c1/8b/5fe2cc11fee489817272089c4203e679c63b570a5aaeb18d852ae3cbba6a/et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa" },
|
{ url = "https://mirrors.aliyun.com/pypi/packages/c1/8b/5fe2cc11fee489817272089c4203e679c63b570a5aaeb18d852ae3cbba6a/et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa" },
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "events"
|
||||||
|
version = "0.5"
|
||||||
|
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://mirrors.aliyun.com/pypi/packages/25/ed/e47dec0626edd468c84c04d97769e7ab4ea6457b7f54dcb3f72b17fcd876/Events-0.5-py3-none-any.whl", hash = "sha256:a7286af378ba3e46640ac9825156c93bdba7502174dd696090fdfcd4d80a1abd" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "exceptiongroup"
|
name = "exceptiongroup"
|
||||||
version = "1.2.2"
|
version = "1.2.2"
|
||||||
@ -3652,6 +3660,22 @@ wheels = [
|
|||||||
{ url = "https://mirrors.aliyun.com/pypi/packages/c0/da/977ded879c29cbd04de313843e76868e6e13408a94ed6b987245dc7c8506/openpyxl-3.1.5-py2.py3-none-any.whl", hash = "sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2" },
|
{ url = "https://mirrors.aliyun.com/pypi/packages/c0/da/977ded879c29cbd04de313843e76868e6e13408a94ed6b987245dc7c8506/openpyxl-3.1.5-py2.py3-none-any.whl", hash = "sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2" },
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "opensearch-py"
|
||||||
|
version = "2.7.1"
|
||||||
|
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
|
||||||
|
dependencies = [
|
||||||
|
{ name = "certifi" },
|
||||||
|
{ name = "events" },
|
||||||
|
{ name = "python-dateutil" },
|
||||||
|
{ name = "requests" },
|
||||||
|
{ name = "urllib3" },
|
||||||
|
]
|
||||||
|
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/c4/ca/5be52de5c69ecd327c16f3fc0dba82b7ffda5bbd0c0e215bdf23a4d12b12/opensearch_py-2.7.1.tar.gz", hash = "sha256:67ab76e9373669bc71da417096df59827c08369ac3795d5438c9a8be21cbd759" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://mirrors.aliyun.com/pypi/packages/80/8f/db678ae203d761922a73920215ea53a79faf3bb1ec6aa9511f809c8e234c/opensearch_py-2.7.1-py3-none-any.whl", hash = "sha256:5417650eba98a1c7648e502207cebf3a12beab623ffe0ebbf55f9b1b4b6e44e9" },
|
||||||
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "orjson"
|
name = "orjson"
|
||||||
version = "3.10.15"
|
version = "3.10.15"
|
||||||
@ -4842,6 +4866,7 @@ dependencies = [
|
|||||||
{ name = "opencv-python" },
|
{ name = "opencv-python" },
|
||||||
{ name = "opencv-python-headless" },
|
{ name = "opencv-python-headless" },
|
||||||
{ name = "openpyxl" },
|
{ name = "openpyxl" },
|
||||||
|
{ name = "opensearch-py" },
|
||||||
{ name = "ormsgpack" },
|
{ name = "ormsgpack" },
|
||||||
{ name = "pandas" },
|
{ name = "pandas" },
|
||||||
{ name = "pdfplumber" },
|
{ name = "pdfplumber" },
|
||||||
@ -4978,6 +5003,7 @@ requires-dist = [
|
|||||||
{ name = "opencv-python", specifier = "==4.10.0.84" },
|
{ name = "opencv-python", specifier = "==4.10.0.84" },
|
||||||
{ name = "opencv-python-headless", specifier = "==4.10.0.84" },
|
{ name = "opencv-python-headless", specifier = "==4.10.0.84" },
|
||||||
{ name = "openpyxl", specifier = ">=3.1.0,<4.0.0" },
|
{ name = "openpyxl", specifier = ">=3.1.0,<4.0.0" },
|
||||||
|
{ name = "opensearch-py", specifier = "==2.7.1" },
|
||||||
{ name = "ormsgpack", specifier = "==1.5.0" },
|
{ name = "ormsgpack", specifier = "==1.5.0" },
|
||||||
{ name = "pandas", specifier = ">=2.2.0,<3.0.0" },
|
{ name = "pandas", specifier = ">=2.2.0,<3.0.0" },
|
||||||
{ name = "pdfplumber", specifier = "==0.10.4" },
|
{ name = "pdfplumber", specifier = "==0.10.4" },
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user