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examples/LangChain.Samples.LocalRAG/LangChain.Samples.LocalRAG.csproj
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| <Project Sdk="Microsoft.NET.Sdk"> | ||
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| <PropertyGroup> | ||
| <OutputType>Exe</OutputType> | ||
| <TargetFramework>net8.0</TargetFramework> | ||
| <ImplicitUsings>enable</ImplicitUsings> | ||
| <Nullable>enable</Nullable> | ||
| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <ProjectReference Include="..\..\src\Databases\Sqlite\src\LangChain.Databases.Sqlite.csproj" /> | ||
| <ProjectReference Include="..\..\src\DocumentLoaders\Pdf\src\LangChain.DocumentLoaders.Pdf.csproj" /> | ||
| <ProjectReference Include="..\..\src\Meta\src\LangChain.csproj" /> | ||
| <ProjectReference Include="..\..\src\Providers\Ollama\src\LangChain.Providers.Ollama.csproj" /> | ||
| </ItemGroup> | ||
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| </Project> |
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| using LangChain.Databases.Sqlite; | ||
| using LangChain.DocumentLoaders; | ||
| using LangChain.Providers.Ollama; | ||
| using LangChain.Extensions; | ||
| using Ollama; | ||
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| var provider = new OllamaProvider(options: new RequestOptions | ||
| { | ||
| Stop = ["\n"], | ||
| Temperature = 0.0f, | ||
| }); | ||
| var embeddingModel = new OllamaEmbeddingModel(provider, id: "all-minilm"); | ||
| var llm = new OllamaChatModel(provider, id: "llama3"); | ||
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| var vectorDatabase = new SqLiteVectorDatabase(dataSource: "vectors.db"); | ||
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| var vectorCollection = await vectorDatabase.AddDocumentsFromAsync<PdfPigPdfLoader>( | ||
| embeddingModel, // Used to convert text to embeddings | ||
| dimensions: 1536, // Should be 1536 for TextEmbeddingV3SmallModel | ||
| dataSource: DataSource.FromUrl("https://canonburyprimaryschool.co.uk/wp-content/uploads/2016/01/Joanne-K.-Rowling-Harry-Potter-Book-1-Harry-Potter-and-the-Philosophers-Stone-EnglishOnlineClub.com_.pdf"), | ||
| collectionName: "harrypotter", // Can be omitted, use if you want to have multiple collections | ||
| textSplitter: null, | ||
| behavior: AddDocumentsToDatabaseBehavior.JustReturnCollectionIfCollectionIsAlreadyExists); | ||
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| const string question = "What is Harry's Address?"; | ||
| var similarDocuments = await vectorCollection.GetSimilarDocuments(embeddingModel, question, amount: 5); | ||
| // Use similar documents and LLM to answer the question | ||
| var answer = await llm.GenerateAsync( | ||
| $""" | ||
| Use the following pieces of context to answer the question at the end. | ||
| If the answer is not in context then just say that you don't know, don't try to make up an answer. | ||
| Keep the answer as short as possible. | ||
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| {similarDocuments.AsString()} | ||
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| Question: {question} | ||
| Helpful Answer: | ||
| """).ConfigureAwait(false); | ||
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| Console.WriteLine($"LLM answer: {answer}"); |