feat(apisix): add Cloudron package

- Implements Apache APISIX packaging for Cloudron platform.
- Includes Dockerfile, CloudronManifest.json, and start.sh.
- Configured to use Cloudron's etcd addon.

🤖 Generated with Gemini CLI
Co-Authored-By: Gemini <noreply@google.com>

CloudronPackages/APISIX/apisix-source/apisix/plugins/ai-rag.lua

@@ -0,0 +1,156 @@
+--
+-- 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.
+--
+local next    = next
+local require = require
+local ngx_req = ngx.req
+
+local http     = require("resty.http")
+local core     = require("apisix.core")
+
+local azure_openai_embeddings = require("apisix.plugins.ai-rag.embeddings.azure_openai").schema
+local azure_ai_search_schema = require("apisix.plugins.ai-rag.vector-search.azure_ai_search").schema
+
+local HTTP_INTERNAL_SERVER_ERROR = ngx.HTTP_INTERNAL_SERVER_ERROR
+local HTTP_BAD_REQUEST = ngx.HTTP_BAD_REQUEST
+
+local schema = {
+    type = "object",
+    properties = {
+        type = "object",
+        embeddings_provider = {
+            type = "object",
+            properties = {
+                azure_openai = azure_openai_embeddings
+            },
+            -- ensure only one provider can be configured while implementing support for
+            -- other providers
+            required = { "azure_openai" },
+            maxProperties = 1,
+        },
+        vector_search_provider = {
+            type = "object",
+            properties = {
+                azure_ai_search = azure_ai_search_schema
+            },
+            -- ensure only one provider can be configured while implementing support for
+            -- other providers
+            required = { "azure_ai_search" },
+            maxProperties = 1
+        },
+    },
+    required = { "embeddings_provider", "vector_search_provider" }
+}
+
+local request_schema = {
+    type = "object",
+    properties = {
+        ai_rag = {
+            type = "object",
+            properties = {
+                vector_search = {},
+                embeddings = {},
+            },
+            required = { "vector_search", "embeddings" }
+        }
+    }
+}
+
+local _M = {
+    version = 0.1,
+    priority = 1060,
+    name = "ai-rag",
+    schema = schema,
+}
+
+
+function _M.check_schema(conf)
+    return core.schema.check(schema, conf)
+end
+
+
+function _M.access(conf, ctx)
+    local httpc = http.new()
+    local body_tab, err = core.request.get_json_request_body_table()
+    if not body_tab then
+        return HTTP_BAD_REQUEST, err
+    end
+    if not body_tab["ai_rag"] then
+        core.log.error("request body must have \"ai-rag\" field")
+        return HTTP_BAD_REQUEST
+    end
+
+    local embeddings_provider = next(conf.embeddings_provider)
+    local embeddings_provider_conf = conf.embeddings_provider[embeddings_provider]
+    local embeddings_driver = require("apisix.plugins.ai-rag.embeddings." .. embeddings_provider)
+
+    local vector_search_provider = next(conf.vector_search_provider)
+    local vector_search_provider_conf = conf.vector_search_provider[vector_search_provider]
+    local vector_search_driver = require("apisix.plugins.ai-rag.vector-search." ..
+                                        vector_search_provider)
+
+    local vs_req_schema = vector_search_driver.request_schema
+    local emb_req_schema = embeddings_driver.request_schema
+
+    request_schema.properties.ai_rag.properties.vector_search = vs_req_schema
+    request_schema.properties.ai_rag.properties.embeddings = emb_req_schema
+
+    local ok, err = core.schema.check(request_schema, body_tab)
+    if not ok then
+        core.log.error("request body fails schema check: ", err)
+        return HTTP_BAD_REQUEST
+    end
+
+    local embeddings, status, err = embeddings_driver.get_embeddings(embeddings_provider_conf,
+                                                        body_tab["ai_rag"].embeddings, httpc)
+    if not embeddings then
+        core.log.error("could not get embeddings: ", err)
+        return status, err
+    end
+
+    local search_body = body_tab["ai_rag"].vector_search
+    search_body.embeddings = embeddings
+    local res, status, err = vector_search_driver.search(vector_search_provider_conf,
+                                                        search_body, httpc)
+    if not res then
+        core.log.error("could not get vector_search result: ", err)
+        return status, err
+    end
+
+    -- remove ai_rag from request body because their purpose is served
+    -- also, these values will cause failure when proxying requests to LLM.
+    body_tab["ai_rag"] = nil
+
+    if not body_tab.messages then
+        body_tab.messages = {}
+    end
+
+    local augment = {
+        role = "user",
+        content = res
+    }
+    core.table.insert_tail(body_tab.messages, augment)
+
+    local req_body_json, err = core.json.encode(body_tab)
+    if not req_body_json then
+        return HTTP_INTERNAL_SERVER_ERROR, err
+    end
+
+    ngx_req.set_body_data(req_body_json)
+end
+
+
+return _M