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implementing long term memory with sqlite-vec
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-113
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‎.DS_Store‎

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‎.gitignore‎

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@@ -128,3 +128,6 @@ dist
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.yarn/build-state.yml
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.yarn/install-state.gz
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.pnp.*
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# Database
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long-term-memory.db

‎configs/.gitignore‎

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*
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!.gitignore
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!*example
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!*template
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!*template

‎configs/config.yaml-template‎

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@@ -56,8 +56,7 @@ plugins:
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vector_model: text-embedding-3-small
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dimensions: 100
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max_query_results: 3
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persist_db: true
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db_file: configs/long-term-memory.db
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persist_db: false
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discord:
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bot_token: your-discord-bot-token
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allowed_channel_ids:

‎package.json‎

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@@ -9,7 +9,7 @@
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"scripts": {
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"dev": "tsx watch --include ./src src/main.ts configs/config.yaml",
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"start": "npm run build && npm run fast-start",
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"fast-start": "node dist/main.js configs/config.yaml",
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"fast-start": "node --experimental-sqlite dist/main.js configs/config.yaml",
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"build": "npm run clean && npm run fast-build",
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"fast-build": "swc src -d dist --strip-leading-paths",
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"clean": "rm -rf dist",

‎src/plugins/long-term-memory/init.ts‎

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@@ -47,134 +47,115 @@ export default class LongTermMemory extends PluginBase {
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defaultHeaders: openaiDefaultHeaders,
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});
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athena.registerTool(
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{
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name: "ltm/store",
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desc: "Store some data to your long-term memory.",
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args: {
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desc: {
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type: "string",
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desc: "A description of the data.",
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required: true,
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},
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data: {
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type: "object",
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desc: "The data to store.",
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required: true,
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},
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athena.registerTool({
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name: "ltm/store",
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desc: "Store some data to your long-term memory.",
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args: {
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desc: {
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type: "string",
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desc: "A description of the data.",
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required: true,
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},
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retvals: {
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status: {
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type: "string",
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desc: "The status of the operation.",
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required: true,
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},
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data: {
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type: "object",
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desc: "The data to store.",
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required: true,
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},
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},
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{
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fn: async (args: Dict<any>) => {
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const embedding = await this.openai.embeddings.create({
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model: this.config.vector_model,
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dimensions: this.config.dimensions,
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input: args.desc,
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encoding_format: "float",
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});
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insertStmt.run(
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Float32Array.from(embedding.data[0].embedding),
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args.desc,
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JSON.stringify(args.data),
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);
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return { status: "success" };
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retvals: {
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status: {
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type: "string",
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desc: "The status of the operation.",
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required: true,
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},
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},
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);
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// TODO: Implement remove
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athena.registerTool(
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{
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name: "ltm/list",
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desc: "List your long-term memory.",
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args: {},
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retvals: {
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list: {
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type: "array",
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desc: "The list of metadata of the long-term memory.",
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required: true,
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fn: async (args: Dict<any>) => {
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const embedding = await this.openai.embeddings.create({
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model: this.config.vector_model,
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dimensions: this.config.dimensions,
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input: args.desc,
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encoding_format: "float",
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});
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insertStmt.run(
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Float32Array.from(embedding.data[0].embedding),
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args.desc,
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JSON.stringify(args.data),
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);
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return { status: "success" };
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},
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});
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athena.registerTool({
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name: "ltm/list",
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desc: "List your long-term memory.",
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args: {},
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retvals: {
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list: {
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type: "array",
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desc: "The list of metadata of the long-term memory.",
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required: true,
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of: {
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type: "object",
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desc: "The metadata of the long-term memory.",
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required: false,
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of: {
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type: "object",
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desc: "The metadata of the long-term memory.",
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required: false,
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of: {
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desc: {
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type: "string",
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desc: "The description of the data.",
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required: true,
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},
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desc: {
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type: "string",
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desc: "The description of the data.",
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required: true,
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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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fn: async (args: Dict<any>) => {
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const list = this.db
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.prepare("SELECT desc, data FROM vec_items")
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.all();
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return {
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list: list.map((item) => ({
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desc: String(item.desc),
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data: JSON.parse(String(item.data)),
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})),
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};
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},
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fn: async (args: Dict<any>) => {
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const list = this.db.prepare("SELECT desc, data FROM vec_items").all();
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return { list: list };
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},
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);
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athena.registerTool(
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{
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name: "ltm/retrieve",
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desc: "Retrieve data from your long-term memory.",
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args: {
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query: {
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type: "string",
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desc: "The query to retrieve the data.",
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required: true,
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},
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});
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athena.registerTool({
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name: "ltm/retrieve",
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desc: "Retrieve data from your long-term memory.",
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args: {
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query: {
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type: "string",
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desc: "The query to retrieve the data.",
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required: true,
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},
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retvals: {
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list: {
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type: "array",
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desc: "Query results list of metadata of the long-term memory.",
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required: true,
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},
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retvals: {
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list: {
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type: "array",
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desc: "Query results list of metadata of the long-term memory.",
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required: true,
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of: {
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type: "object",
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desc: "The desc and data of the long-term memory.",
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required: false,
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of: {
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type: "object",
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desc: "The desc and data of the long-term memory.",
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required: false,
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of: {
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desc: {
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type: "string",
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desc: "The description of the data.",
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required: true,
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},
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data: {
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type: "object",
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desc: "The data.",
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required: true,
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},
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desc: {
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type: "string",
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desc: "The description of the data.",
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required: true,
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},
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data: {
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type: "object",
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desc: "The data.",
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required: true,
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},
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},
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},
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},
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},
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{
168-
fn: async (args) => {
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const embedding = await this.openai.embeddings.create({
170-
model: this.config.vector_model,
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dimensions: this.config.dimensions,
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input: args.query,
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encoding_format: "float",
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});
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const results = this.db
176-
.prepare(
177-
`SELECT
149+
fn: async (args: Dict<any>) => {
150+
const embedding = await this.openai.embeddings.create({
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model: this.config.vector_model,
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dimensions: this.config.dimensions,
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input: args.query,
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encoding_format: "float",
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});
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const results = this.db
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.prepare(
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`SELECT
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distance,
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desc,
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data

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