人工智能 | 搭建企业内部的大语言模型系统

大纲

  • 开源大语言模型
  • 大语言模型管理
  • 私有大语言模型服务部署方案


开源大语言模型


担心安全与隐私？可私有部署的开源大模型

  • 商业大模型，不支持私有部署
  • ChatGPT
  • Claude
  • Google Gemini
  • 百度问心一言
  • 开源大模型，支持私有部署
  • Mistral
  • Meta Llama
  • ChatGLM
  • 阿里通义千问

常用开源大模型列表

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开源大模型分支

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大语言模型管理


大语言模型管理工具

  • HuggingFace全面的大语言模型管理平台
  • Ollama在本地管理大语言模型，下载速度超快
  • llama.cpp在本地和云端的各种硬件上以最少的设置和最先进的性能实现 LLM 推理
  • GPT4All 一个免费使用、本地运行、具有隐私意识的聊天机器人。无需 GPU 或互联网


Ollama速度最快的大语言模型管理工具

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Ollama 的命令


ollama pull llama2ollama listollama run llama2 "Summarize this file: $(cat README.md)"
ollama serve
curl http://localhost:11434/api/generate -d '{  "model": "llama2",  "prompt":"Why is the sky blue?"}'curl http://localhost:11434/api/chat -d '{  "model": "mistral",  "messages": [    { "role": "user", "content": "why is the sky blue?" }  ]}'
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大语言模型的前端


大语言模型的应用前端

  • 开源平台 ollama-chatbot、PrivateGPT、gradio
  • 开源服务 hugging face TGI、langchain-serve
  • 开源框架 langchain llama-index


ollama chatbot


docker run -p 3000:3000 ghcr.io/ivanfioravanti/chatbot-ollama:main## http://localhost:3000

ollama chatbot

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PrivateGPT

PrivateGPT 提供了一个 API，其中包含构建私有的、上下文感知的 AI 应用程序所需的所有构建块。该 API 遵循并扩展了 OpenAI API 标准，支持普通响应和流响应。这意味着，如果您可以在您的工具之一中使用 OpenAI API，则可以使用您自己的 PrivateGPT API，无需更改代码，并且如果您在本地模式下运行 privateGPT，则免费。

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PrivateGPT 架构

  • FastAPI
  • LLamaIndex
  • 支持本地 LLM，比如 ChatGLM llama Mistral
  • 支持远程 LLM，比如 OpenAI Claud
  • 支持嵌入 embeddings，比如 ollama embeddings-huggingface
  • 支持向量存储，比如 Qdrant, ChromaDB and Postgres


PrivateGPT 环境准备


git clone https://github.com/imartinez/privateGPTcd privateGPT#不支持3.11之前的版本python3.11 -m venv .venvsource .venv/bin/activatepip install --upgrade pip poetry
#虽然官网只说了要安装少部分的依赖，但是那些依赖管理不是那么完善，容易有遗漏#所以我们的策略就是全都要。poetry install --extras "ui llms-llama-cpp llms-openai llms-openai-like llms-ollama llms-sagemaker llms-azopenai embeddings-ollama embeddings-huggingface embeddings-openai embeddings-sagemaker embeddings-azopenai vector-stores-qdrant vector-stores-chroma vector-stores-postgres storage-nodestore-postgres"
#或者用这个安装脚本#poetry install --extras "$(sed -n '/tool.poetry.extras/,/^$/p'  pyproject.toml | awk -F= 'NR>1{print $1}' | xargs)"


ollama 部署方式


ollama pull mistralollama pull nomic-embed-textollama serve
#官方这个依赖不够，还需要额外安装torch，所以尽量采用上面提到的全部安装的策略poetry install --extras "ui llms-ollama embeddings-ollama vector-stores-qdrant"PGPT_PROFILES=ollama poetry run python -m private_gpt


setting-ollama.yaml


server:  env_name: ${APP_ENV:ollama}
llm:  mode: ollama  max_new_tokens: 512  context_window: 3900  temperature: 0.1 #The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual. (Default: 0.1)
embedding:  mode: ollama
ollama:  llm_model: mistral  embedding_model: nomic-embed-text  api_base: http://localhost:11434  tfs_z: 1.0 ## Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.  top_k: 40 ## Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)  top_p: 0.9 ## Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)  repeat_last_n: 64 ## Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)  repeat_penalty: 1.2 ## Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)
vectorstore:  database: qdrant
qdrant:  path: local_data/private_gpt/qdrant

启动


PGPT_PROFILES=ollama poetry run python -m private_gpt
poetry run python -m private_gpt02:36:06.928 [INFO    ] private_gpt.settings.settings_loader - Starting application with profiles=['default', 'ollama']02:36:46.567 [INFO    ] private_gpt.components.llm.llm_component - Initializing the LLM in mode=ollama02:36:47.405 [INFO    ] private_gpt.components.embedding.embedding_component - Initializing the embedding model in mode=ollama02:36:47.414 [INFO    ] llama_index.core.indices.loading - Loading all indices.02:36:47.571 [INFO    ]         private_gpt.ui.ui - Mounting the gradio UI, at path=/02:36:47.620 [INFO    ]             uvicorn.error - Started server process [72677]02:36:47.620 [INFO    ]             uvicorn.error - Waiting for application startup.02:36:47.620 [INFO    ]             uvicorn.error - Application startup complete.02:36:47.620 [INFO    ]             uvicorn.error - Uvicorn running on http://0.0.0.0:8001 (Press CTRL+C to quit)

PrivateGPT UI

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local 部署模式


#todo: 需要安装llama-cpp，每个平台的安装方式都不同，参考官方文档
poetry run python scripts/setupPGPT_PROFILES=local poetry run python -m private_gpt


setting-local.yaml


server:  env_name: ${APP_ENV:local}
llm:  mode: llamacpp  ## Should be matching the selected model  max_new_tokens: 512  context_window: 3900  tokenizer: mistralai/Mistral-7B-Instruct-v0.2
llamacpp:  prompt_style: "mistral"  llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.2-GGUF  llm_hf_model_file: mistral-7b-instruct-v0.2.Q4_K_M.gguf
embedding:  mode: huggingface
huggingface:  embedding_hf_model_name: BAAI/bge-small-en-v1.5
vectorstore:  database: qdrant
qdrant:  path: local_data/private_gpt/qdrant


非私有 OpenAI-powered 部署


poetry install --extras "ui llms-openai embeddings-openai vector-stores-qdrant"PGPT_PROFILES=openai poetry run python -m private_gpt


setting-openai.yaml


server:  env_name: ${APP_ENV:openai}
llm:  mode: openai
embedding:  mode: openai
openai:  api_key: ${OPENAI_API_KEY:}  model: gpt-3.5-turbo


openai 风格的 API 调用

  • The API is built using FastAPI and follows OpenAI's API scheme.
  • The RAG pipeline is based on LlamaIndex.

curl -X POST http://localhost:8000/v1/completions \     -H "Content-Type: application/json" \     -d '{  "prompt": "string",  "stream": true
}'


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