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Posted to commits@answer.apache.org by li...@apache.org on 2026/05/08 12:21:24 UTC
(answer-plugins) branch main updated (0f638e3 -> 3ffe46a)
This is an automated email from the ASF dual-hosted git repository.
LinkinStars pushed a change to branch main
in repository https://gitbox.apache.org/repos/asf/answer-plugins.git
from 0f638e3 storage-s3: Add DigitalOcean note.
new 3943d14 feat: add in-memory vector search plugin with multilingual support
new 3ffe46a feat(i18n): add options for embedding level in English and Chinese translations
The 2 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
vector-search-memory/README.md | 55 ++++
{captcha-google-v2 => vector-search-memory}/go.mod | 2 +-
{captcha-google-v2 => vector-search-memory}/go.sum | 0
.../i18n/en_US.yaml | 43 +--
vector-search-memory/i18n/translation.go | 42 +++
.../i18n/zh_CN.yaml | 43 +--
{cache-redis => vector-search-memory}/info.yaml | 8 +-
vector-search-memory/memory.go | 332 +++++++++++++++++++++
vector-search-memory/sync.go | 102 +++++++
9 files changed, 582 insertions(+), 45 deletions(-)
create mode 100644 vector-search-memory/README.md
copy {captcha-google-v2 => vector-search-memory}/go.mod (97%)
copy {captcha-google-v2 => vector-search-memory}/go.sum (100%)
copy {search-algolia => vector-search-memory}/i18n/en_US.yaml (50%)
create mode 100644 vector-search-memory/i18n/translation.go
copy {search-algolia => vector-search-memory}/i18n/zh_CN.yaml (51%)
copy {cache-redis => vector-search-memory}/info.yaml (84%)
create mode 100644 vector-search-memory/memory.go
create mode 100644 vector-search-memory/sync.go
(answer-plugins) 02/02: feat(i18n): add options for embedding level in English and Chinese translations
Posted by li...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
LinkinStars pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/answer-plugins.git
commit 3ffe46a31018805ddff1741f986992b271206620
Author: hgaol <dh...@hotmail.com>
AuthorDate: Wed Apr 22 22:30:01 2026 +0800
feat(i18n): add options for embedding level in English and Chinese translations
---
vector-search-memory/i18n/en_US.yaml | 5 +++++
vector-search-memory/i18n/translation.go | 6 ++++--
vector-search-memory/i18n/zh_CN.yaml | 5 +++++
vector-search-memory/memory.go | 4 ++--
4 files changed, 16 insertions(+), 4 deletions(-)
diff --git a/vector-search-memory/i18n/en_US.yaml b/vector-search-memory/i18n/en_US.yaml
index 8fee915..224cf4e 100644
--- a/vector-search-memory/i18n/en_US.yaml
+++ b/vector-search-memory/i18n/en_US.yaml
@@ -44,6 +44,11 @@ plugin:
other: Embedding Level
description:
other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately"
+ options:
+ question:
+ other: Question
+ answer:
+ other: Answer
similarity_threshold:
title:
other: Similarity Threshold
diff --git a/vector-search-memory/i18n/translation.go b/vector-search-memory/i18n/translation.go
index bfce94e..f4bfbe6 100644
--- a/vector-search-memory/i18n/translation.go
+++ b/vector-search-memory/i18n/translation.go
@@ -32,8 +32,10 @@ const (
ConfigEmbeddingModelTitle = "plugin.memory_vector_search.backend.config.embedding_model.title"
ConfigEmbeddingModelDescription = "plugin.memory_vector_search.backend.config.embedding_model.description"
- ConfigEmbeddingLevelTitle = "plugin.memory_vector_search.backend.config.embedding_level.title"
- ConfigEmbeddingLevelDescription = "plugin.memory_vector_search.backend.config.embedding_level.description"
+ ConfigEmbeddingLevelTitle = "plugin.memory_vector_search.backend.config.embedding_level.title"
+ ConfigEmbeddingLevelDescription = "plugin.memory_vector_search.backend.config.embedding_level.description"
+ ConfigEmbeddingLevelOptionQuestion = "plugin.memory_vector_search.backend.config.embedding_level.options.question"
+ ConfigEmbeddingLevelOptionAnswer = "plugin.memory_vector_search.backend.config.embedding_level.options.answer"
ConfigSimilarityThresholdTitle = "plugin.memory_vector_search.backend.config.similarity_threshold.title"
ConfigSimilarityThresholdDescription = "plugin.memory_vector_search.backend.config.similarity_threshold.description"
diff --git a/vector-search-memory/i18n/zh_CN.yaml b/vector-search-memory/i18n/zh_CN.yaml
index 81eadd7..66dedf1 100644
--- a/vector-search-memory/i18n/zh_CN.yaml
+++ b/vector-search-memory/i18n/zh_CN.yaml
@@ -44,6 +44,11 @@ plugin:
other: Embedding 级别
description:
other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入"
+ options:
+ question:
+ other: 问题
+ answer:
+ other: 回答
similarity_threshold:
title:
other: 相似度阈值
diff --git a/vector-search-memory/memory.go b/vector-search-memory/memory.go
index 9f24a58..158514b 100644
--- a/vector-search-memory/memory.go
+++ b/vector-search-memory/memory.go
@@ -245,8 +245,8 @@ func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField {
Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription),
Required: true,
Options: []plugin.ConfigFieldOption{
- {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), Value: "question"},
- {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), Value: "answer"},
+ {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionQuestion), Value: "question"},
+ {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelOptionAnswer), Value: "answer"},
},
Value: e.Config.EmbeddingLevel,
},
(answer-plugins) 01/02: feat: add in-memory vector search plugin with multilingual support
Posted by li...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
LinkinStars pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/answer-plugins.git
commit 3943d145b946ed39d9d0048c3741af552e72d4e0
Author: hgaol <dh...@hotmail.com>
AuthorDate: Sun Apr 12 17:45:14 2026 +0800
feat: add in-memory vector search plugin with multilingual support
- Implemented the in-memory vector search functionality in the new `vector-search-memory` plugin.
- Added English and Chinese localization files for plugin configuration and descriptions.
- Created a comprehensive configuration structure for embedding API integration, including API host, key, model, level, and similarity threshold.
- Developed core functionalities for document management, including adding, updating, deleting, and searching documents based on cosine similarity.
- Introduced synchronization capabilities to bulk index questions and answers from the registered syncer.
- Embedded necessary metadata and logging for better traceability and debugging.
---
vector-search-memory/README.md | 55 +++
vector-search-memory/go.mod | 55 +++
vector-search-memory/go.sum | 758 +++++++++++++++++++++++++++++++
vector-search-memory/i18n/en_US.yaml | 51 +++
vector-search-memory/i18n/translation.go | 40 ++
vector-search-memory/i18n/zh_CN.yaml | 51 +++
vector-search-memory/info.yaml | 22 +
vector-search-memory/memory.go | 332 ++++++++++++++
vector-search-memory/sync.go | 102 +++++
9 files changed, 1466 insertions(+)
diff --git a/vector-search-memory/README.md b/vector-search-memory/README.md
new file mode 100644
index 0000000..cf2bdb2
--- /dev/null
+++ b/vector-search-memory/README.md
@@ -0,0 +1,55 @@
+# Apache Answer Plugin: In-Memory Vector Search
+
+A lightweight, pure-Go, in-memory vector search plugin for [Apache Answer](https://github.com/apache/answer). Designed for **development and testing** -- no external services, no Docker, no CGo required.
+
+## When to Use This Plugin
+
+- **Development** -- quickly test semantic search without setting up a database
+- **Testing** -- verify vector search integration in CI/CD pipelines
+- **Small demos** -- run Answer with vector search on a single machine with zero setup
+
+**Not recommended for production** -- all data is stored in memory and lost on restart.
+
+## Installation
+
+Build Apache Answer with this plugin:
+
+```bash
+./answer build --with github.com/apache/answer-plugins/vector-search-memory
+```
+
+No special environment variables needed. Works with `CGO_ENABLED=0`.
+
+## Configuration
+
+After enabling the plugin in the Admin UI (**Admin > Plugins > Vector Search**), configure the following fields:
+
+| Field | Description | Example |
+|---|---|---|
+| **Embedding API Host** | OpenAI-compatible API base URL | `https://api.openai.com` |
+| **Embedding API Key** | API key for the embedding service | `sk-...` |
+| **Embedding Model** | Model name for generating embeddings | `text-embedding-3-small` |
+| **Embedding Level** | `question` embeds question + all answers + comments together; `answer` embeds each answer separately | `question` |
+| **Similarity Threshold** | Minimum cosine similarity score (0-1). Default `0` means no filtering | `0.5` |
+
+No connection endpoint or database path is needed -- everything runs in-process.
+
+## How It Works
+
+- Stores all document embeddings in a Go `map[string]*document` guarded by `sync.RWMutex`
+- Search performs brute-force cosine similarity over all stored vectors
+- Embedding dimensions are auto-detected from the configured model
+- Changing the embedding model clears all stored documents (since dimensions may differ)
+- A full sync of all questions/answers is triggered when the plugin starts
+
+## Limitations
+
+- **No persistence** -- data is lost when Answer restarts
+- **No scalability** -- brute-force search is O(n) per query; suitable for thousands of documents, not millions
+- **Memory usage** -- each document stores a full embedding vector in RAM
+
+For production use, consider [pgvector](../vector-search-pgvector/), [Elasticsearch](../vector-search-elasticsearch/), [Weaviate](../vector-search-weaviate/), [Milvus](../vector-search-milvus/), [Qdrant](../vector-search-qdrant/), or [ChromaDB](../vector-search-chromadb/).
+
+## License
+
+[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
diff --git a/vector-search-memory/go.mod b/vector-search-memory/go.mod
new file mode 100644
index 0000000..d016222
--- /dev/null
+++ b/vector-search-memory/go.mod
@@ -0,0 +1,55 @@
+module github.com/apache/answer-plugins/vector-search-memory
+
+go 1.23.0
+
+require (
+ github.com/apache/answer v1.7.0
+ github.com/apache/answer-plugins/util v1.0.3-0.20250107030257-cf94ebc70954
+ github.com/segmentfault/pacman v1.0.5-0.20230822083413-c0075a2d401f
+)
+
+require (
+ github.com/LinkinStars/go-i18n/v2 v2.2.2 // indirect
+ github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect
+ github.com/aymerick/douceur v0.2.0 // indirect
+ github.com/bytedance/sonic v1.12.2 // indirect
+ github.com/bytedance/sonic/loader v0.2.0 // indirect
+ github.com/cloudwego/base64x v0.1.4 // indirect
+ github.com/cloudwego/iasm v0.2.0 // indirect
+ github.com/gabriel-vasile/mimetype v1.4.5 // indirect
+ github.com/gin-contrib/sse v0.1.0 // indirect
+ github.com/gin-gonic/gin v1.10.0 // indirect
+ github.com/go-playground/locales v0.14.1 // indirect
+ github.com/go-playground/universal-translator v0.18.1 // indirect
+ github.com/go-playground/validator/v10 v10.22.1 // indirect
+ github.com/goccy/go-json v0.10.3 // indirect
+ github.com/golang/snappy v0.0.4 // indirect
+ github.com/google/wire v0.5.0 // indirect
+ github.com/gorilla/css v1.0.1 // indirect
+ github.com/json-iterator/go v1.1.12 // indirect
+ github.com/klauspost/cpuid/v2 v2.2.8 // indirect
+ github.com/leodido/go-urn v1.4.0 // indirect
+ github.com/mattn/go-isatty v0.0.20 // indirect
+ github.com/microcosm-cc/bluemonday v1.0.27 // indirect
+ github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
+ github.com/modern-go/reflect2 v1.0.2 // indirect
+ github.com/pelletier/go-toml/v2 v2.2.3 // indirect
+ github.com/segmentfault/pacman/contrib/i18n v0.0.0-20230822083413-c0075a2d401f // indirect
+ github.com/syndtr/goleveldb v1.0.0 // indirect
+ github.com/tidwall/gjson v1.17.3 // indirect
+ github.com/tidwall/match v1.1.1 // indirect
+ github.com/tidwall/pretty v1.2.1 // indirect
+ github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
+ github.com/ugorji/go/codec v1.2.12 // indirect
+ github.com/yuin/goldmark v1.7.4 // indirect
+ golang.org/x/arch v0.10.0 // indirect
+ golang.org/x/crypto v0.36.0 // indirect
+ golang.org/x/net v0.38.0 // indirect
+ golang.org/x/sys v0.31.0 // indirect
+ golang.org/x/text v0.23.0 // indirect
+ google.golang.org/protobuf v1.34.2 // indirect
+ gopkg.in/yaml.v3 v3.0.1 // indirect
+ sigs.k8s.io/yaml v1.4.0 // indirect
+ xorm.io/builder v0.3.13 // indirect
+ xorm.io/xorm v1.3.2 // indirect
+)
diff --git a/vector-search-memory/go.sum b/vector-search-memory/go.sum
new file mode 100644
index 0000000..a58d055
--- /dev/null
+++ b/vector-search-memory/go.sum
@@ -0,0 +1,758 @@
+cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+filippo.io/edwards25519 v1.1.0 h1:FNf4tywRC1HmFuKW5xopWpigGjJKiJSV0Cqo0cJWDaA=
+filippo.io/edwards25519 v1.1.0/go.mod h1:BxyFTGdWcka3PhytdK4V28tE5sGfRvvvRV7EaN4VDT4=
+gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a h1:lSA0F4e9A2NcQSqGqTOXqu2aRi/XEQxDCBwM8yJtE6s=
+gitea.com/xorm/sqlfiddle v0.0.0-20180821085327-62ce714f951a/go.mod h1:EXuID2Zs0pAQhH8yz+DNjUbjppKQzKFAn28TMYPB6IU=
+gitee.com/travelliu/dm v1.8.11192/go.mod h1:DHTzyhCrM843x9VdKVbZ+GKXGRbKM2sJ4LxihRxShkE=
+github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
+github.com/BurntSushi/toml v1.0.0 h1:dtDWrepsVPfW9H/4y7dDgFc2MBUSeJhlaDtK13CxFlU=
+github.com/BurntSushi/toml v1.0.0/go.mod h1:CxXYINrC8qIiEnFrOxCa7Jy5BFHlXnUU2pbicEuybxQ=
+github.com/Knetic/govaluate v3.0.1-0.20171022003610-9aa49832a739+incompatible/go.mod h1:r7JcOSlj0wfOMncg0iLm8Leh48TZaKVeNIfJntJ2wa0=
+github.com/LinkinStars/go-i18n/v2 v2.2.2 h1:ZfjpzbW13dv6btv3RALKZkpN9A+7K1JA//2QcNeWaxU=
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+github.com/Masterminds/semver/v3 v3.1.1/go.mod h1:VPu/7SZ7ePZ3QOrcuXROw5FAcLl4a0cBrbBpGY/8hQs=
+github.com/Shopify/sarama v1.19.0/go.mod h1:FVkBWblsNy7DGZRfXLU0O9RCGt5g3g3yEuWXgklEdEo=
+github.com/Shopify/toxiproxy v2.1.4+incompatible/go.mod h1:OXgGpZ6Cli1/URJOF1DMxUHB2q5Ap20/P/eIdh4G0pI=
+github.com/VividCortex/gohistogram v1.0.0/go.mod h1:Pf5mBqqDxYaXu3hDrrU+w6nw50o/4+TcAqDqk/vUH7g=
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diff --git a/vector-search-memory/i18n/en_US.yaml b/vector-search-memory/i18n/en_US.yaml
new file mode 100644
index 0000000..8fee915
--- /dev/null
+++ b/vector-search-memory/i18n/en_US.yaml
@@ -0,0 +1,51 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+plugin:
+ memory_vector_search:
+ backend:
+ info:
+ name:
+ other: In-Memory Vector Search
+ description:
+ other: Lightweight in-memory vector search for development and testing (no external dependencies)
+ config:
+ api_host:
+ title:
+ other: Embedding API Host
+ description:
+ other: "OpenAI-compatible API base URL for embedding generation (e.g. https://api.openai.com)"
+ api_key:
+ title:
+ other: Embedding API Key
+ description:
+ other: API key for the embedding service
+ embedding_model:
+ title:
+ other: Embedding Model
+ description:
+ other: "Model name for embedding generation (e.g. text-embedding-3-small)"
+ embedding_level:
+ title:
+ other: Embedding Level
+ description:
+ other: "Choose 'question' to embed question + all answers + comments, or 'answer' to embed each answer separately"
+ similarity_threshold:
+ title:
+ other: Similarity Threshold
+ description:
+ other: "Minimum cosine similarity score (0-1) for search results. Default 0 (no filtering)."
diff --git a/vector-search-memory/i18n/translation.go b/vector-search-memory/i18n/translation.go
new file mode 100644
index 0000000..bfce94e
--- /dev/null
+++ b/vector-search-memory/i18n/translation.go
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+package i18n
+
+const (
+ InfoName = "plugin.memory_vector_search.backend.info.name"
+ InfoDescription = "plugin.memory_vector_search.backend.info.description"
+
+ ConfigAPIHostTitle = "plugin.memory_vector_search.backend.config.api_host.title"
+ ConfigAPIHostDescription = "plugin.memory_vector_search.backend.config.api_host.description"
+
+ ConfigAPIKeyTitle = "plugin.memory_vector_search.backend.config.api_key.title"
+ ConfigAPIKeyDescription = "plugin.memory_vector_search.backend.config.api_key.description"
+
+ ConfigEmbeddingModelTitle = "plugin.memory_vector_search.backend.config.embedding_model.title"
+ ConfigEmbeddingModelDescription = "plugin.memory_vector_search.backend.config.embedding_model.description"
+
+ ConfigEmbeddingLevelTitle = "plugin.memory_vector_search.backend.config.embedding_level.title"
+ ConfigEmbeddingLevelDescription = "plugin.memory_vector_search.backend.config.embedding_level.description"
+
+ ConfigSimilarityThresholdTitle = "plugin.memory_vector_search.backend.config.similarity_threshold.title"
+ ConfigSimilarityThresholdDescription = "plugin.memory_vector_search.backend.config.similarity_threshold.description"
+)
diff --git a/vector-search-memory/i18n/zh_CN.yaml b/vector-search-memory/i18n/zh_CN.yaml
new file mode 100644
index 0000000..81eadd7
--- /dev/null
+++ b/vector-search-memory/i18n/zh_CN.yaml
@@ -0,0 +1,51 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+plugin:
+ memory_vector_search:
+ backend:
+ info:
+ name:
+ other: 内存向量搜索
+ description:
+ other: 轻量级内存向量搜索，适用于开发和测试（无外部依赖）
+ config:
+ api_host:
+ title:
+ other: Embedding API 地址
+ description:
+ other: "用于生成 Embedding 的 OpenAI 兼容 API 地址（如 https://api.openai.com）"
+ api_key:
+ title:
+ other: Embedding API 密钥
+ description:
+ other: Embedding 服务的 API 密钥
+ embedding_model:
+ title:
+ other: Embedding 模型
+ description:
+ other: "Embedding 模型名称（如 text-embedding-3-small）"
+ embedding_level:
+ title:
+ other: Embedding 级别
+ description:
+ other: "选择 'question' 将问题+所有回答+评论合并嵌入，或 'answer' 将每个回答单独嵌入"
+ similarity_threshold:
+ title:
+ other: 相似度阈值
+ description:
+ other: "搜索结果的最低余弦相似度分数（0-1）。默认为 0（不过滤）。"
diff --git a/vector-search-memory/info.yaml b/vector-search-memory/info.yaml
new file mode 100644
index 0000000..26cb591
--- /dev/null
+++ b/vector-search-memory/info.yaml
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+slug_name: memory_vector_search
+type: vector_search
+version: 0.1.0
+author: answerdev
+link: https://github.com/apache/answer-plugins/tree/main/vector-search-memory
diff --git a/vector-search-memory/memory.go b/vector-search-memory/memory.go
new file mode 100644
index 0000000..9f24a58
--- /dev/null
+++ b/vector-search-memory/memory.go
@@ -0,0 +1,332 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+package memory_vector_search
+
+import (
+ "context"
+ "embed"
+ "encoding/json"
+ "fmt"
+ "math"
+ "sort"
+ "strconv"
+ "sync"
+
+ "github.com/apache/answer-plugins/util"
+ "github.com/apache/answer-plugins/vector-search-memory/i18n"
+ "github.com/apache/answer/plugin"
+ "github.com/segmentfault/pacman/log"
+)
+
+//go:embed info.yaml
+var Info embed.FS
+
+type document struct {
+ objectID string
+ objectType string
+ title string
+ content string
+ metadata string
+ embedding []float32
+}
+
+// VectorSearchEngine implements plugin.VectorSearch using an in-memory store.
+type VectorSearchEngine struct {
+ Config *VectorSearchConfig
+ docs map[string]*document
+ mu sync.RWMutex
+ syncer plugin.VectorSearchSyncer
+ syncing bool
+ lock sync.Mutex
+ embeddingDimensions int
+ configured bool
+}
+
+// VectorSearchConfig holds all plugin configuration.
+type VectorSearchConfig struct {
+ APIHost string `json:"api_host"`
+ APIKey string `json:"api_key"`
+ EmbeddingModel string `json:"embedding_model"`
+ EmbeddingLevel string `json:"embedding_level"`
+ SimilarityThreshold float64 `json:"similarity_threshold"`
+}
+
+func init() {
+ plugin.Register(&VectorSearchEngine{
+ Config: &VectorSearchConfig{},
+ docs: make(map[string]*document),
+ lock: sync.Mutex{},
+ })
+}
+
+func (e *VectorSearchEngine) Info() plugin.Info {
+ info := &util.Info{}
+ info.GetInfo(Info)
+
+ return plugin.Info{
+ Name: plugin.MakeTranslator(i18n.InfoName),
+ SlugName: info.SlugName,
+ Description: plugin.MakeTranslator(i18n.InfoDescription),
+ Author: info.Author,
+ Version: info.Version,
+ Link: info.Link,
+ }
+}
+
+func (e *VectorSearchEngine) Description() plugin.VectorSearchDesc {
+ return plugin.VectorSearchDesc{
+ Icon: "",
+ Link: "",
+ }
+}
+
+func (e *VectorSearchEngine) RegisterSyncer(ctx context.Context, syncer plugin.VectorSearchSyncer) {
+ log.Debugf("memory: RegisterSyncer called, configured=%v", e.configured)
+ e.syncer = syncer
+ if e.configured {
+ e.sync()
+ }
+}
+
+func (e *VectorSearchEngine) SearchSimilar(ctx context.Context, query string, topK int) ([]plugin.VectorSearchResult, error) {
+ if !e.configured {
+ return nil, fmt.Errorf("memory: not initialized")
+ }
+ if topK <= 0 {
+ topK = 10
+ }
+
+ log.Debugf("memory: SearchSimilar query=%q topK=%d", query, topK)
+
+ embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, query)
+ if err != nil {
+ return nil, fmt.Errorf("generate query embedding: %w", err)
+ }
+
+ log.Debugf("memory: search embedding generated, dimensions=%d", len(embedding))
+
+ e.mu.RLock()
+ type scored struct {
+ doc *document
+ score float64
+ }
+ candidates := make([]scored, 0, len(e.docs))
+ for _, doc := range e.docs {
+ score := cosineSimilarity(embedding, doc.embedding)
+ if e.Config.SimilarityThreshold > 0 && score < e.Config.SimilarityThreshold {
+ continue
+ }
+ candidates = append(candidates, scored{doc: doc, score: score})
+ }
+ e.mu.RUnlock()
+
+ sort.Slice(candidates, func(i, j int) bool {
+ return candidates[i].score > candidates[j].score
+ })
+
+ if len(candidates) > topK {
+ candidates = candidates[:topK]
+ }
+
+ results := make([]plugin.VectorSearchResult, 0, len(candidates))
+ for _, c := range candidates {
+ results = append(results, plugin.VectorSearchResult{
+ ObjectID: c.doc.objectID,
+ ObjectType: c.doc.objectType,
+ Metadata: c.doc.metadata,
+ Score: c.score,
+ })
+ }
+
+ log.Debugf("memory: SearchSimilar returning %d results", len(results))
+ return results, nil
+}
+
+func (e *VectorSearchEngine) UpdateContent(ctx context.Context, content *plugin.VectorSearchContent) error {
+ if !e.configured {
+ return fmt.Errorf("memory: not initialized")
+ }
+
+ log.Debugf("memory: UpdateContent objectID=%s objectType=%s", content.ObjectID, content.ObjectType)
+
+ embedding, err := plugin.GenerateEmbedding(ctx, e.Config.APIHost, e.Config.APIKey, e.Config.EmbeddingModel, content.Content)
+ if err != nil {
+ return fmt.Errorf("generate embedding for %s: %w", content.ObjectID, err)
+ }
+
+ log.Debugf("memory: embedding generated for %s, dimensions=%d", content.ObjectID, len(embedding))
+
+ e.mu.Lock()
+ e.docs[content.ObjectID] = &document{
+ objectID: content.ObjectID,
+ objectType: content.ObjectType,
+ title: content.Title,
+ content: content.Content,
+ metadata: content.Metadata,
+ embedding: embedding,
+ }
+ e.mu.Unlock()
+
+ log.Debugf("memory: upserted document %s successfully (total docs=%d)", content.ObjectID, len(e.docs))
+ return nil
+}
+
+func (e *VectorSearchEngine) DeleteContent(ctx context.Context, objectID string) error {
+ if !e.configured {
+ return fmt.Errorf("memory: not initialized")
+ }
+
+ log.Debugf("memory: DeleteContent objectID=%s", objectID)
+
+ e.mu.Lock()
+ delete(e.docs, objectID)
+ e.mu.Unlock()
+
+ log.Debugf("memory: deleted document %s", objectID)
+ return nil
+}
+
+func (e *VectorSearchEngine) ConfigFields() []plugin.ConfigField {
+ return []plugin.ConfigField{
+ {
+ Name: "api_host",
+ Type: plugin.ConfigTypeInput,
+ Title: plugin.MakeTranslator(i18n.ConfigAPIHostTitle),
+ Description: plugin.MakeTranslator(i18n.ConfigAPIHostDescription),
+ Required: true,
+ UIOptions: plugin.ConfigFieldUIOptions{
+ InputType: plugin.InputTypeText,
+ },
+ Value: e.Config.APIHost,
+ },
+ {
+ Name: "api_key",
+ Type: plugin.ConfigTypeInput,
+ Title: plugin.MakeTranslator(i18n.ConfigAPIKeyTitle),
+ Description: plugin.MakeTranslator(i18n.ConfigAPIKeyDescription),
+ Required: true,
+ UIOptions: plugin.ConfigFieldUIOptions{
+ InputType: plugin.InputTypePassword,
+ },
+ Value: e.Config.APIKey,
+ },
+ {
+ Name: "embedding_model",
+ Type: plugin.ConfigTypeInput,
+ Title: plugin.MakeTranslator(i18n.ConfigEmbeddingModelTitle),
+ Description: plugin.MakeTranslator(i18n.ConfigEmbeddingModelDescription),
+ Required: true,
+ UIOptions: plugin.ConfigFieldUIOptions{
+ InputType: plugin.InputTypeText,
+ },
+ Value: e.Config.EmbeddingModel,
+ },
+ {
+ Name: "embedding_level",
+ Type: plugin.ConfigTypeSelect,
+ Title: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle),
+ Description: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelDescription),
+ Required: true,
+ Options: []plugin.ConfigFieldOption{
+ {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), Value: "question"},
+ {Label: plugin.MakeTranslator(i18n.ConfigEmbeddingLevelTitle), Value: "answer"},
+ },
+ Value: e.Config.EmbeddingLevel,
+ },
+ {
+ Name: "similarity_threshold",
+ Type: plugin.ConfigTypeInput,
+ Title: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdTitle),
+ Description: plugin.MakeTranslator(i18n.ConfigSimilarityThresholdDescription),
+ Required: false,
+ UIOptions: plugin.ConfigFieldUIOptions{
+ InputType: plugin.InputTypeText,
+ },
+ Value: fmt.Sprintf("%.2f", e.Config.SimilarityThreshold),
+ },
+ }
+}
+
+func (e *VectorSearchEngine) ConfigReceiver(config []byte) error {
+ log.Debugf("memory: ConfigReceiver called")
+
+ var raw map[string]interface{}
+ if err := json.Unmarshal(config, &raw); err != nil {
+ return fmt.Errorf("unmarshal config: %w", err)
+ }
+ if threshStr, ok := raw["similarity_threshold"].(string); ok {
+ if thresh, err := strconv.ParseFloat(threshStr, 64); err == nil {
+ raw["similarity_threshold"] = thresh
+ }
+ }
+ fixed, _ := json.Marshal(raw)
+
+ conf := &VectorSearchConfig{}
+ if err := json.Unmarshal(fixed, conf); err != nil {
+ return fmt.Errorf("unmarshal config: %w", err)
+ }
+
+ e.Config = conf
+
+ log.Debugf("memory: config parsed: model=%s level=%s threshold=%.2f",
+ conf.EmbeddingModel, conf.EmbeddingLevel, conf.SimilarityThreshold)
+
+ if !plugin.StatusManager.IsEnabled("memory_vector_search") {
+ log.Debugf("memory: plugin not active, skipping initialization")
+ return nil
+ }
+
+ // Auto-detect embedding dimensions.
+ log.Debugf("memory: detecting embedding dimensions via probe call")
+ probeEmbedding, err := plugin.GenerateEmbedding(context.Background(), conf.APIHost, conf.APIKey, conf.EmbeddingModel, "dimension probe")
+ if err != nil {
+ return fmt.Errorf("detect embedding dimensions: %w", err)
+ }
+ e.embeddingDimensions = len(probeEmbedding)
+ log.Infof("memory: auto-detected embedding dimensions=%d for model=%s", e.embeddingDimensions, conf.EmbeddingModel)
+
+ // Clear existing documents since model may have changed.
+ e.mu.Lock()
+ e.docs = make(map[string]*document)
+ e.mu.Unlock()
+
+ e.configured = true
+
+ log.Debugf("memory: ConfigReceiver completed successfully")
+ return nil
+}
+
+// cosineSimilarity computes the cosine similarity between two vectors.
+func cosineSimilarity(a, b []float32) float64 {
+ if len(a) != len(b) || len(a) == 0 {
+ return 0
+ }
+ var dot, normA, normB float64
+ for i := range a {
+ ai, bi := float64(a[i]), float64(b[i])
+ dot += ai * bi
+ normA += ai * ai
+ normB += bi * bi
+ }
+ if normA == 0 || normB == 0 {
+ return 0
+ }
+ return dot / (math.Sqrt(normA) * math.Sqrt(normB))
+}
diff --git a/vector-search-memory/sync.go b/vector-search-memory/sync.go
new file mode 100644
index 0000000..8d8c3fb
--- /dev/null
+++ b/vector-search-memory/sync.go
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+package memory_vector_search
+
+import (
+ "context"
+
+ "github.com/apache/answer/plugin"
+ "github.com/segmentfault/pacman/log"
+)
+
+func (e *VectorSearchEngine) sync() {
+ if e.syncer == nil {
+ log.Warn("memory: syncer not registered, skip sync")
+ return
+ }
+ if e.syncing {
+ log.Warn("memory: sync already running, skip")
+ return
+ }
+
+ go func() {
+ e.lock.Lock()
+ defer e.lock.Unlock()
+ if e.syncing {
+ return
+ }
+ e.syncing = true
+ defer func() { e.syncing = false }()
+
+ page, pageSize := 1, 100
+
+ if e.Config.EmbeddingLevel == "" || e.Config.EmbeddingLevel == "question" {
+ log.Info("memory: starting question sync...")
+ page = 1
+ for {
+ log.Infof("memory: sync questions page %d", page)
+ questions, err := e.syncer.GetQuestionsPage(context.TODO(), page, pageSize)
+ if err != nil {
+ log.Errorf("memory: get questions page failed: %v", err)
+ break
+ }
+ if len(questions) == 0 {
+ break
+ }
+ e.bulkIndex(context.TODO(), questions)
+ page++
+ }
+ }
+
+ if e.Config.EmbeddingLevel == "answer" {
+ log.Info("memory: starting answer sync...")
+ page = 1
+ for {
+ log.Infof("memory: sync answers page %d", page)
+ answers, err := e.syncer.GetAnswersPage(context.TODO(), page, pageSize)
+ if err != nil {
+ log.Errorf("memory: get answers page failed: %v", err)
+ break
+ }
+ if len(answers) == 0 {
+ break
+ }
+ e.bulkIndex(context.TODO(), answers)
+ page++
+ }
+ }
+
+ log.Info("memory: sync complete")
+ }()
+}
+
+func (e *VectorSearchEngine) bulkIndex(ctx context.Context, contents []*plugin.VectorSearchContent) {
+ log.Debugf("memory: bulkIndex batch size=%d", len(contents))
+ success, failed := 0, 0
+ for _, c := range contents {
+ if err := e.UpdateContent(ctx, c); err != nil {
+ log.Warnf("memory: index %s failed: %v", c.ObjectID, err)
+ failed++
+ } else {
+ success++
+ }
+ }
+ log.Debugf("memory: bulkIndex batch done, success=%d failed=%d", success, failed)
+}