mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 21:37:20 +08:00
deed92169f
* 新功能： - 支持在线 Embeddings：zhipu-api, qwen-api, minimax-api, qianfan-api - API 增加 /other/embed_texts 接口 - init_database.py 增加 --embed-model 参数，可以指定使用的嵌入模型（本地或在线均可） 问题修复： - API 中 list_config_models 会删除 ONLINE_LLM_MODEL 中的敏感信息，导致第二轮API请求错误 开发者： - 优化 kb_service 中 Embeddings 操作： - 统一加载接口： server.utils.load_embeddings，利用全局缓存避免各处 Embeddings 传参 - 统一文本嵌入接口：server.embedding_api.[embed_texts, embed_documents]
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import sys
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from pathlib import Path
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root_path = Path(__file__).parent.parent
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sys.path.append(str(root_path))
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from configs import ONLINE_LLM_MODEL
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from server.model_workers.base import *
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from server.utils import get_model_worker_config, list_config_llm_models
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from pprint import pprint
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import pytest
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workers = []
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for x in list_config_llm_models()["online"]:
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if x in ONLINE_LLM_MODEL and x not in workers:
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workers.append(x)
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print(f"all workers to test: {workers}")
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# workers = ["qianfan-api"]
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@pytest.mark.parametrize("worker", workers)
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def test_chat(worker):
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params = ApiChatParams(
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messages = [
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{"role": "user", "content": "你是谁"},
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],
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)
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print(f"\nchat with {worker} \n")
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worker_class = get_model_worker_config(worker)["worker_class"]
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for x in worker_class().do_chat(params):
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pprint(x)
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assert isinstance(x, dict)
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assert x["error_code"] == 0
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@pytest.mark.parametrize("worker", workers)
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def test_embeddings(worker):
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params = ApiEmbeddingsParams(
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texts = [
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"LangChain-Chatchat (原 Langchain-ChatGLM): 基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。",
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"一种利用 langchain 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。",
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]
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)
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worker_class = get_model_worker_config(worker)["worker_class"]
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if worker_class.can_embedding():
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print(f"\embeddings with {worker} \n")
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resp = worker_class().do_embeddings(params)
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pprint(resp, depth=2)
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assert resp["code"] == 200
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assert "data" in resp
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embeddings = resp["data"]
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assert isinstance(embeddings, list) and len(embeddings) > 0
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assert isinstance(embeddings[0], list) and len(embeddings[0]) > 0
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assert isinstance(embeddings[0][0], float)
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print("向量长度：", len(embeddings[0]))
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# @pytest.mark.parametrize("worker", workers)
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# def test_completion(worker):
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# params = ApiCompletionParams(prompt="五十六个民族")
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# print(f"\completion with {worker} \n")
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# worker_class = get_model_worker_config(worker)["worker_class"]
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# resp = worker_class().do_completion(params)
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# pprint(resp)
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