descartcan

Name	descartcan JSON
Version	2025.7.22.1 JSON
	download
home_page	None
Summary	A Python toolkit for advanced data processing and API interactions
upload_time	2025-07-22 12:31:30
maintainer	None
docs_url	None
author	DescartCan
requires_python	>=3.10
license	None
keywords	api toolkit
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            
# descartcan

### LLM

#### Step1, llm_config.yaml

```yaml
openai:
  keys:
    - name: "openai_key1"
      api_key: "XXX"
  models:
    gpt4: "gpt-4-0125-preview"
    gpt40: "gpt-4o"

bedrock:
  keys:
    - name: "bedrock"
      api_key: "XXX"
      api_secret: "XXXXX"
  models:
      haiku35: "us.anthropic.claude-3-5-haiku-20241022-v1:0"

```

#### Step2, load models

```python
from descartcan.llm.factory import LLModelFactory
model_factory = LLModelFactory.from_config(config="llm_config.yaml")
model = model_factory.get_model("openai.gpt4")

# 单轮对话
response = await model.chat(
    question="Show Python",
    system="你是一个编程专家"
)
print(f"回复: {response.content}")
print(f"Token统计: 提示词{response.prompt_tokens}, 生成{response.completion_tokens}, 总计{response.total_tokens}")

# 多轮对话
history = [
    {"role": "user", "content": "Python和Java的区别是什么？"},
    {"role": "assistant", "content": "Python和Java有以下主要区别：..."}
]
response = await model.chat(
    question="哪个更适合初学者？",
    system="你是一个编程专家",
    history=history
)
print(f"多轮对话回复: {response.content}")
```

            

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    "description": "\n# descartcan\n\n### LLM\n\n#### Step1, llm_config.yaml\n\n```yaml\nopenai:\n  keys:\n    - name: \"openai_key1\"\n      api_key: \"XXX\"\n  models:\n    gpt4: \"gpt-4-0125-preview\"\n    gpt40: \"gpt-4o\"\n\nbedrock:\n  keys:\n    - name: \"bedrock\"\n      api_key: \"XXX\"\n      api_secret: \"XXXXX\"\n  models:\n      haiku35: \"us.anthropic.claude-3-5-haiku-20241022-v1:0\"\n\n```\n\n#### Step2, load models\n\n```python\nfrom descartcan.llm.factory import LLModelFactory\nmodel_factory = LLModelFactory.from_config(config=\"llm_config.yaml\")\nmodel = model_factory.get_model(\"openai.gpt4\")\n\n# \u5355\u8f6e\u5bf9\u8bdd\nresponse = await model.chat(\n    question=\"Show Python\",\n    system=\"\u4f60\u662f\u4e00\u4e2a\u7f16\u7a0b\u4e13\u5bb6\"\n)\nprint(f\"\u56de\u590d: {response.content}\")\nprint(f\"Token\u7edf\u8ba1: \u63d0\u793a\u8bcd{response.prompt_tokens}, \u751f\u6210{response.completion_tokens}, \u603b\u8ba1{response.total_tokens}\")\n\n# \u591a\u8f6e\u5bf9\u8bdd\nhistory = [\n    {\"role\": \"user\", \"content\": \"Python\u548cJava\u7684\u533a\u522b\u662f\u4ec0\u4e48\uff1f\"},\n    {\"role\": \"assistant\", \"content\": \"Python\u548cJava\u6709\u4ee5\u4e0b\u4e3b\u8981\u533a\u522b\uff1a...\"}\n]\nresponse = await model.chat(\n    question=\"\u54ea\u4e2a\u66f4\u9002\u5408\u521d\u5b66\u8005\uff1f\",\n    system=\"\u4f60\u662f\u4e00\u4e2a\u7f16\u7a0b\u4e13\u5bb6\",\n    history=history\n)\nprint(f\"\u591a\u8f6e\u5bf9\u8bdd\u56de\u590d: {response.content}\")\n```\n",
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