havendv/LangChain.Databases.Weaviatev0.10.2
.NET FW4.6.2.NET 8.0.NET 9.0.NET Std2.0
Weaviate for LangChain.
Get Started
$ dotnet add package LangChain.Databases.WeaviateReadme
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
C# implementation of LangChain. We try to be as close to the original as possible in terms of abstractions, but are open to new entities.
While the SemanticKernel is good and we will use it wherever possible, we believe that it has many limitations and based on Microsoft technologies.
We proceed from the position of the maximum choice of available options and are open to using third-party libraries within individual implementations.
❤️ Our project includes https://github.com/jeastham1993/langchain-dotnet and tries to be updated with the latest changes there ❤️
Usage
const string apiKey = "API_KEY";
using var httpClient = new HttpClient();
var model = new Gpt4Model(apiKey, httpClient);
var response = await model.GenerateAsync("Hello, World of AI!");
var numberOfTokens = model.CountTokens("Hello, World of AI!");
Chains
const string apiKey = "API_KEY";
using var httpClient = new HttpClient();
var model = new Gpt4Model(apiKey, httpClient);
var template = "What is a good name for a company that makes {product}?";
var prompt = new PromptTemplate(new PromptTemplateInput(template, new List<string>(1){"product"}));
var chain = new LlmChain(new LlmChainInput(model, prompt));
var result = await chain.CallAsync(new ChainValues(new Dictionary<string, object>(1)
{
{ "product", "colourful socks" }
}));
// The result is an object with a `text` property.
Console.WriteLine(result.Value["text"]);
// Since the LLMChain is a single-input, single-output chain, we can also call it with `run`.
// This takes in a string and returns the `text` property.
var result2 = await chain.Run("colourful socks");
Console.WriteLine(result2);