ollama + ollama web + fastapi app (langchain) demo

ollama + ollama web + fastapi app (langchain) demo

https://github.com/fanqingsong/ollama-docker

Welcome to the Ollama Docker Compose Setup! This project simplifies the deployment of Ollama using Docker Compose, making it easy to run Ollama with all its dependencies in a containerized environment.

 

ollama

https://python.langchain.com/docs/integrations/llms/ollama/

Ollama

Ollama allows you to run open-source large language models, such as Llama 2, locally.

Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.

It optimizes setup and configuration details, including GPU usage.

For a complete list of supported models and model variants, see the Ollama model library.

 

同义模型

https://ollama.com/library/qwen

 

meta模型

https://ollama.com/library/llama2

 

ollama镜像预置模型：

https://github.com/FultonBrowne/ollama-docker

 

ollama + ollamaweb 容器化部署方案

https://github.com/lgdd/chatollama

 

LangChain

https://python.langchain.com/docs/get_started/introduction

 

LangChain is a framework for developing applications powered by large language models (LLMs).

LangChain simplifies every stage of the LLM application lifecycle:

• Development: Build your applications using LangChain's open-source building blocks and components. Hit the ground running using third-party integrations and Templates.
• Productionization: Use LangSmith to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
• Deployment: Turn any chain into an API with LangServe.

Concretely, the framework consists of the following open-source libraries:

• langchain-core: Base abstractions and LangChain Expression Language.
• langchain-community: Third party integrations.
  • Partner packages (e.g. langchain-openai, langchain-anthropic, etc.): Some integrations have been further split into their own lightweight packages that only depend on langchain-core.
• langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
• langgraph: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
• langserve: Deploy LangChain chains as REST APIs.

The broader ecosystem includes:

• LangSmith: A developer platform that lets you debug, test, evaluate, and monitor LLM applications and seamlessly integrates with LangChain.

 

API

https://api.python.langchain.com/en/latest/langchain_api_reference.html#

 

integration of ollama and langchain

https://python.langchain.com/docs/integrations/llms/ollama/#via-langchain

from langchain_community.llms import Ollama

llm = Ollama(model="llama3")

llm.invoke("Tell me a joke")

 

 

RAG

https://zhuanlan.zhihu.com/p/695140853

 

https://github.com/fanqingsong/ollama-docker/blob/main/src/rag.py

 

https://github.com/fanqingsong/DocQA/blob/main/app.py