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记忆配置新增字段

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This commit is contained in:
zhishi 2026-03-19 20:04:49 +08:00
parent ec8a459cd9
commit f7bfc05a53
4 changed files with 92 additions and 34 deletions
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47
src/lib/initDB.ts
View File

@ -48,8 +48,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.primary(["id"]);
table.unique(["id"]);
},
initData: async (knex) => {
},
initData: async (knex) => {},
},
//技能表
{
@ -61,7 +60,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.primary(["id"]);
table.unique(["id"]);
},
initData: async (knex) => { },
initData: async (knex) => {},
},
//Agent配置表
{
@ -153,16 +152,36 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
value: uuid().slice(0, 8),
},
{
key: "shortTermMemoryLength",
value: 10,
},
{
key: "searchTopK",
key: "messagesPerSummary",
value: 3,
},
{
key: "similarityThreshold",
value: 0.3,
key: "shortTermLimit",
value: 5,
},
{
key: "summaryMaxLength",
value: 500,
},
{
key: "summaryLimit",
value: 10,
},
{
key: "ragLimit",
value: 3,
},
{
key: "deepRetrieveSummaryLimit",
value: 5,
},
{
key: "modelOnnxFile",
value: '["all-MiniLM-L6-v2", "onnx", "model_fp16.onnx"]',
},
{
key: "modelDtype",
value: "fp16",
},
]);
},
@ -183,7 +202,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.primary(["id"]);
table.unique(["id"]);
},
initData: async (knex) => { },
initData: async (knex) => {},
},
//提示词表
{
@ -200,7 +219,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.unique(["id"]);
table.unique(["code"]); // 代号唯一
},
initData: async (knex) => { },
initData: async (knex) => {},
},
//资产表
{
@ -220,7 +239,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.primary(["id"]);
table.unique(["id"]);
},
initData: async (knex) => { },
initData: async (knex) => {},
},
//任务中心表
{
@ -238,7 +257,7 @@ export default async (knex: Knex, forceInit: boolean = false): Promise<void> =>
table.primary(["id"]);
table.unique(["id"]);
},
initData: async (knex) => { },
initData: async (knex) => {},
},
//小说原文表
{

23
src/routes/setting/memoryConfig/getMemory.ts
View File

@ -4,14 +4,31 @@ import u from "@/utils";
const router = express.Router();
export default router.get("/", async (req, res) => {
const settingData = await u.db("o_setting").whereIn("key", ["shortTermMemoryLength", "searchTopK", "similarityThreshold"]);
const settingData = await u
.db("o_setting")
.whereIn("key", [
"messagesPerSummary",
"shortTermLimit",
"summaryMaxLength",
"summaryLimit",
"ragLimit",
"deepRetrieveSummaryLimit",
"modelOnnxFile",
"modelDtype",
]);
if (!settingData) return res.status(400).send(error(`获取记忆配置失败`));
const memoryObj: Record<string, number> = {};
const memoryObj: Record<string, number | string | string[]> = {};
settingData.forEach((i) => {
if (i.key && i.value) {
memoryObj[i.key] = Number(i.value);
let value: number | string | string[] = i.value;
if (i.key == "modelOnnxFile") {
value = JSON.parse(i.value);
} else if (i.key != "modelDtype") {
value = Number(value);
}
memoryObj[i.key] = value;
}
});

42
src/routes/setting/memoryConfig/sureMemory.ts
View File

@ -9,21 +9,37 @@ const router = express.Router();
export default router.post(
"/",
validateFields({
shortTermMemoryLength: z.number(), //短期记忆长度
searchTopK: z.number(), //搜索记忆条数
similarityThreshold: z.number(), //记忆相似度阈值
messagesPerSummary: z.number(),
shortTermLimit: z.number(),
summaryMaxLength: z.number(),
summaryLimit: z.number(),
ragLimit: z.number(),
deepRetrieveSummaryLimit: z.number(),
modelOnnxFile: z.array(z.string()),
modelDtype: z.string(),
}),
async (req, res) => {
const { shortTermMemoryLength, searchTopK, similarityThreshold } = req.body;
await u.db("o_setting").where("key", "shortTermMemoryLength").update({
value: shortTermMemoryLength,
});
await u.db("o_setting").where("key", "searchTopK").update({
value: searchTopK,
});
await u.db("o_setting").where("key", "similarityThreshold").update({
value: similarityThreshold,
});
const { messagesPerSummary, shortTermLimit, summaryMaxLength, summaryLimit, ragLimit, deepRetrieveSummaryLimit, modelOnnxFile, modelDtype } =
req.body;
const upsert = async (key: string, value: string) => {
const exists = await u.db("o_setting").where("key", key).first();
if (exists) {
await u.db("o_setting").where("key", key).update({ value });
} else {
await u.db("o_setting").insert({ key, value });
}
};
await upsert("messagesPerSummary", messagesPerSummary);
await upsert("shortTermLimit", shortTermLimit);
await upsert("summaryMaxLength", summaryMaxLength);
await upsert("summaryLimit", summaryLimit);
await upsert("ragLimit", ragLimit);
await upsert("deepRetrieveSummaryLimit", deepRetrieveSummaryLimit);
await upsert("modelOnnxFile", JSON.stringify(modelOnnxFile));
await upsert("modelDtype", modelDtype);
res.status(200).send(success("保存设置成功"));
},
);

14
src/utils/agent/embedding.ts
View File

@ -2,18 +2,24 @@ import { pipeline, env as transformersEnv, FeatureExtractionPipeline } from "@hu
import path from "path";
import fs from "fs";
import getPath from "@/utils/getPath";
import db from "@/utils/db";
// ── 模型配置 ──
const modelOnnxFile = ["all-MiniLM-L6-v2", "onnx", "model_fp16.onnx"]; // 模型文件路径
const modelDtype = "fp16" as const; // 量化类型：fp32
// const modelOnnxFile = ["all-MiniLM-L6-v2", "onnx", "model_fp16.onnx"]; // 模型文件路径
// const modelDtype = "fp16" as const; // 量化类型：fp32
let extractor: FeatureExtractionPipeline | null = null;
export async function initEmbedding(): Promise<void> {
if (extractor) return;
//todo 模型配置放到这里
const modelConfigData = await db("o_setting").whereIn("key", ["modelOnnxFile", "modelDtype"]);
const modelObj: Record<string, string> = {};
Object.entries(modelConfigData).forEach(([key, value]) => {
modelObj[key] = value as string;
});
let modelOnnxFile = modelObj?.modelOnnxFile ? JSON.parse(modelObj.modelOnnxFile) : ["all-MiniLM-L6-v2", "onnx", "model_fp16.onnx"]; // 模型文件路径
let modelDtype = modelObj?.modelDtype ?? ("fp16" as const); // 量化类型：fp32
const onnxPath = path.join(getPath("models"), ...modelOnnxFile);
if (!fs.existsSync(onnxPath)) {
throw new Error(`Embedding 模型文件不存在: ${onnxPath}`);