Continue 源码分析 - 各种命令调用大模型的输入和输出


LLM 可观察性(LLM Observability)、提示管理(Prompt Management)、LLM 评估(LLM Evaluations)、数据集(Datasets)、LLM 指标(LLM Metrics)和提示游乐场(Prompt Playground)


# Clone repository
git clone https://github.com/langfuse/langfuse.git
cd langfuse
# Run server and database
docker compose up -d




安装
pip install langfuse>=2.0.0 litellm
main.py import os import litellm os.environ['LITELLM_LOG'] = 'DEBUG' # Langfuse os.environ["LANGFUSE_HOST"]="http://localhost:3000" os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-fd5d8fba-5134-4037-884d-d6780894a65a" os.
model_list:
- model_name: qwen-coder
litellm_params:
model: ollama/qwen2.5-coder:7b
- model_name: bge-m3
litellm_params:
model: ollama/bge-m3
- model_name: llava
litellm_params:
model: ollama/llava:7b
api_base: "http://localhost:11434"
# api_base: http://127.0.0.1:11434/v1 # ❌ 500 Internal Server Error
- model_name: gpt-4
litellm_params:
model: openai/gpt-4-32k
// ...
docker run --name litellm \
-v $(pwd)/litellm_config.yaml:/app/config.yaml \
-p 4000:4000 \
ghcr.io/berriai/litellm:main-stable \
--config /app/config.yaml \
--detailed_debug