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4
Commits
| Author | SHA1 | Date | |
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65f42bbf43 | ||
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2b8e4ea059 | ||
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a5dab4279b | ||
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08e9cc476c |
@@ -12,7 +12,11 @@ export class LMStudioEmbedder implements Embedder {
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private model: string;
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constructor(config: EmbeddingConfig) {
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const baseURL = config.baseURL ?? config.url ?? DEFAULT_BASE_URL;
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const baseURL =
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config.baseURL ??
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config.url ??
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process.env.LMSTUDIO_BASE_URL ??
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DEFAULT_BASE_URL;
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const apiKey = config.apiKey || DEFAULT_LMSTUDIO_API_KEY;
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this.openai = new OpenAI({ apiKey, baseURL: String(baseURL) });
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this.model = config.model || DEFAULT_MODEL;
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@@ -12,7 +12,8 @@ export class LMStudioLLM extends OpenAILLM {
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super({
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...config,
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apiKey: config.apiKey || DEFAULT_LMSTUDIO_API_KEY,
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baseURL: config.baseURL ?? DEFAULT_BASE_URL,
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baseURL:
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config.baseURL ?? process.env.LMSTUDIO_BASE_URL ?? DEFAULT_BASE_URL,
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model: config.model || DEFAULT_MODEL,
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});
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}
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@@ -10,14 +10,64 @@ const mockCreate = jest.fn().mockResolvedValue({
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data: [{ embedding: mockEmbedding }],
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});
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let capturedConstructorArgs: any;
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jest.mock("openai", () => {
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return jest.fn().mockImplementation(() => ({
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embeddings: { create: mockCreate },
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}));
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return jest.fn().mockImplementation((args: any) => {
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capturedConstructorArgs = args;
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return {
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embeddings: { create: mockCreate },
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};
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});
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});
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describe("LMStudioEmbedder (unit)", () => {
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beforeEach(() => mockCreate.mockClear());
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beforeEach(() => {
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mockCreate.mockClear();
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capturedConstructorArgs = undefined;
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delete process.env.LMSTUDIO_BASE_URL;
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});
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afterEach(() => {
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delete process.env.LMSTUDIO_BASE_URL;
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});
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it("uses the default base URL when neither config nor env var is set", async () => {
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const embedder = new LMStudioEmbedder({ model: "test-model" });
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await embedder.embed("hello");
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://localhost:1234/v1",
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});
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});
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it("uses LMSTUDIO_BASE_URL when config does not provide a base URL", async () => {
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process.env.LMSTUDIO_BASE_URL = "http://example.test:5678/v1";
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const embedder = new LMStudioEmbedder({ model: "test-model" });
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await embedder.embed("hello");
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://example.test:5678/v1",
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});
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});
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it("prefers an explicit config baseURL over LMSTUDIO_BASE_URL", async () => {
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process.env.LMSTUDIO_BASE_URL = "http://example.test:5678/v1";
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const embedder = new LMStudioEmbedder({
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model: "test-model",
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baseURL: "http://config.test:9999/v1",
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});
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await embedder.embed("hello");
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://config.test:9999/v1",
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});
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});
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it("embed() calls OpenAI with encoding_format float and returns vector", async () => {
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const embedder = new LMStudioEmbedder({
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@@ -5,16 +5,56 @@
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import { LMStudioLLM } from "../src/llms/lmstudio";
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let capturedConstructorArgs: any;
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const mockCreate = jest.fn();
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jest.mock("openai", () => {
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return jest.fn().mockImplementation(() => ({
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chat: { completions: { create: mockCreate } },
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}));
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return jest.fn().mockImplementation((args: any) => {
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capturedConstructorArgs = args;
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return {
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chat: { completions: { create: mockCreate } },
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};
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});
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});
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describe("LMStudioLLM (unit)", () => {
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beforeEach(() => mockCreate.mockClear());
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beforeEach(() => {
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mockCreate.mockClear();
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capturedConstructorArgs = undefined;
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delete process.env.LMSTUDIO_BASE_URL;
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});
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afterEach(() => {
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delete process.env.LMSTUDIO_BASE_URL;
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});
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it("uses the default base URL when neither config nor env var is set", () => {
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new LMStudioLLM({});
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://localhost:1234/v1",
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});
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});
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it("uses LMSTUDIO_BASE_URL when config does not provide a base URL", () => {
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process.env.LMSTUDIO_BASE_URL = "http://example.test:5678/v1";
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new LMStudioLLM({});
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://example.test:5678/v1",
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});
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});
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it("prefers an explicit config baseURL over LMSTUDIO_BASE_URL", () => {
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process.env.LMSTUDIO_BASE_URL = "http://example.test:5678/v1";
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new LMStudioLLM({ baseURL: "http://config.test:9999/v1" });
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expect(capturedConstructorArgs).toMatchObject({
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baseURL: "http://config.test:9999/v1",
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});
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});
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it("generateResponse() returns a text response", async () => {
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mockCreate.mockResolvedValueOnce({
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@@ -34,7 +34,7 @@ class BaseEmbedderConfig(ABC):
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# Gemini specific
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output_dimensionality: Optional[str] = None,
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# LM Studio specific
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lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
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lmstudio_base_url: Optional[str] = None,
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# AWS Bedrock specific
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aws_access_key_id: Optional[str] = None,
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aws_secret_access_key: Optional[str] = None,
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@@ -70,7 +70,8 @@ class BaseEmbedderConfig(ABC):
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:type memory_update_embedding_type: Optional[str], optional
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:param memory_search_embedding_type: The type of embedding to use for the search memory action, defaults to None
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:type memory_search_embedding_type: Optional[str], optional
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:param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
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:param lmstudio_base_url: LM Studio base URL to be use, defaults to None
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(resolved by the provider from LMSTUDIO_BASE_URL, then "http://localhost:1234/v1")
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:type lmstudio_base_url: Optional[str], optional
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"""
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@@ -39,6 +39,7 @@ class LMStudioConfig(BaseLlmConfig):
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vision_details: Vision detail level, defaults to "auto"
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http_client_proxies: HTTP client proxy settings, defaults to None
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lmstudio_base_url: LM Studio base URL, defaults to None
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(resolved by the provider from LMSTUDIO_BASE_URL, then "http://localhost:1234/v1")
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lmstudio_response_format: LM Studio response format, defaults to None
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"""
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# Initialize base parameters
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@@ -55,5 +56,5 @@ class LMStudioConfig(BaseLlmConfig):
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)
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# LM Studio-specific parameters
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self.lmstudio_base_url = lmstudio_base_url or "http://localhost:1234/v1"
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self.lmstudio_base_url = lmstudio_base_url
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self.lmstudio_response_format = lmstudio_response_format
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@@ -1,3 +1,4 @@
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import os
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from typing import Literal, Optional
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from openai import OpenAI
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@@ -13,6 +14,9 @@ class LMStudioEmbedding(EmbeddingBase):
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self.config.model = self.config.model or "nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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self.config.embedding_dims = self.config.embedding_dims or 1536
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self.config.api_key = self.config.api_key or "lm-studio"
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self.config.lmstudio_base_url = (
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self.config.lmstudio_base_url or os.getenv("LMSTUDIO_BASE_URL") or "http://localhost:1234/v1"
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)
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self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
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@@ -1,4 +1,5 @@
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import json
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import os
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from typing import Dict, List, Optional, Union
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from openai import OpenAI
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@@ -37,6 +38,9 @@ class LMStudioLLM(LLMBase):
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or "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf"
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)
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self.config.api_key = self.config.api_key or "lm-studio"
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self.config.lmstudio_base_url = (
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self.config.lmstudio_base_url or os.getenv("LMSTUDIO_BASE_URL") or "http://localhost:1234/v1"
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)
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self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
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@@ -6,7 +6,6 @@ from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.lmstudio import LMStudioEmbedding
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@pytest.fixture
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def mock_lm_studio_client():
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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@@ -80,3 +79,31 @@ def test_embed_batch_count_mismatch_raises(mock_lm_studio_client):
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with pytest.raises(ValueError, match="returned 1 embeddings for 2 texts"):
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embedder.embed_batch(["first text", "second text"])
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def test_base_url_honors_lmstudio_base_url_env(mock_lm_studio_client, monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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LMStudioEmbedding(BaseEmbedderConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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def test_base_url_prefers_explicit_config_over_env(mock_lm_studio_client, monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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LMStudioEmbedding(BaseEmbedderConfig(lmstudio_base_url="http://explicit:1234/v1"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"
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def test_base_url_falls_back_to_localhost_default(mock_lm_studio_client, monkeypatch):
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monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)
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with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
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embedder = LMStudioEmbedding(BaseEmbedderConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
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assert embedder.config.lmstudio_base_url == "http://localhost:1234/v1"
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@@ -2,6 +2,7 @@ from unittest.mock import Mock, patch
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import pytest
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.configs.llms.lmstudio import LMStudioConfig
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from mem0.llms.lmstudio import LMStudioLLM
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@@ -70,3 +71,40 @@ def test_generate_response_specifying_response_format(mock_lm_studio_client):
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)
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assert response == "I'm doing well, thank you for asking!"
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def test_base_url_honors_lmstudio_base_url_env(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(LMStudioConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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def test_base_url_prefers_explicit_config_over_env(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(LMStudioConfig(lmstudio_base_url="http://explicit:1234/v1"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"
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def test_base_url_falls_back_to_localhost_default(monkeypatch):
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monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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llm = LMStudioLLM(LMStudioConfig())
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assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
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assert llm.config.lmstudio_base_url == "http://localhost:1234/v1"
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def test_base_url_honors_env_via_base_llm_config(monkeypatch):
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monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")
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with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
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LMStudioLLM(BaseLlmConfig(model="some-model"))
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assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"
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