wehub-resource-sync
183 lines
7.2 KiB
Markdown
183 lines
7.2 KiB
Markdown
# Vector Store RAG Demo
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This sample demonstrates how to ingest text from pdf files into a vector store and ask questions about the content
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using an LLM while using RAG to supplement the LLM with additional information from the vector store.
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## Configuring the Sample
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The sample can be configured in various ways:
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1. You can choose your preferred vector store by setting the `Rag:VectorStoreType` configuration setting in the `appsettings.json` file to one of the following values:
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1. AzureAISearch
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1. CosmosMongoDB
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1. CosmosNoSql
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1. InMemory
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1. Qdrant
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1. Redis
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1. Weaviate
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1. You can choose your preferred AI Chat service by settings the `Rag:AIChatService` configuration setting in the `appsettings.json` file to one of the following values:
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1. AzureOpenAI
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1. OpenAI
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1. You can choose your preferred AI Embedding service by settings the `Rag:AIEmbeddingService` configuration setting in the `appsettings.json` file to one of the following values:
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1. AzureOpenAIEmbeddings
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1. OpenAIEmbeddings
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1. You can choose whether to load data into the vector store by setting the `Rag:BuildCollection` configuration setting in the `appsettings.json` file to `true`. If you set this to `false`, the sample will assume that data was already loaded previously and it will go straight into the chat experience.
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1. You can choose the name of the collection to use by setting the `Rag:CollectionName` configuration setting in the `appsettings.json` file.
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1. You can choose the pdf file to load into the vector store by setting the `Rag:PdfFilePaths` array in the `appsettings.json` file.
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1. You can choose the number of records to process per batch when loading data into the vector store by setting the `Rag:DataLoadingBatchSize` configuration setting in the `appsettings.json` file.
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1. You can choose the number of milliseconds to wait between batches when loading data into the vector store by setting the `Rag:DataLoadingBetweenBatchDelayInMilliseconds` configuration setting in the `appsettings.json` file.
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## Dependency Setup
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To run this sample, you need to setup your source data, setup your vector store and AI services, and setup secrets for these.
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### Source PDF File
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You will need to supply some source pdf files to load into the vector store.
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Once you have a file ready, update the `PdfFilePaths` array in the `appsettings.json` file with the path to the file.
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```json
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{
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"Rag": {
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"PdfFilePaths": [ "sourcedocument.pdf" ],
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}
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}
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```
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Why not try the semantic kernel documentation as your input.
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You can download it as a PDF from the https://learn.microsoft.com/en-us/semantic-kernel/overview/ page.
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See the Download PDF button at the bottom of the page.
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### Azure OpenAI Chat Completion
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For Azure OpenAI Chat Completion, you need to add the following secrets:
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```cli
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dotnet user-secrets set "AIServices:AzureOpenAI:Endpoint" "https://<yourservice>.openai.azure.com"
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dotnet user-secrets set "AIServices:AzureOpenAI:ChatDeploymentName" "<your deployment name>"
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```
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Note that the code doesn't use an API Key to communicate with Azure OpenAI, but rather an `AzureCliCredential` so no api key secret is required.
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### OpenAI Chat Completion
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For OpenAI Chat Completion, you need to add the following secrets:
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```cli
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dotnet user-secrets set "AIServices:OpenAI:ModelId" "<your model id>"
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dotnet user-secrets set "AIServices:OpenAI:ApiKey" "<your api key>"
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```
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Optionally, you can also provide an Org Id
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```cli
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dotnet user-secrets set "AIServices:OpenAI:OrgId" "<your org id>"
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```
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### Azure OpenAI Embeddings
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For Azure OpenAI Embeddings, you need to add the following secrets:
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```cli
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dotnet user-secrets set "AIServices:AzureOpenAIEmbeddings:Endpoint" "https://<yourservice>.openai.azure.com"
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dotnet user-secrets set "AIServices:AzureOpenAIEmbeddings:DeploymentName" "<your deployment name>"
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```
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Note that the code doesn't use an API Key to communicate with Azure OpenAI, but rather an `AzureCliCredential` so no api key secret is required.
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### OpenAI Embeddings
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For OpenAI Embeddings, you need to add the following secrets:
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```cli
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dotnet user-secrets set "AIServices:OpenAIEmbeddings:ModelId" "<your model id>"
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dotnet user-secrets set "AIServices:OpenAIEmbeddings:ApiKey" "<your api key>"
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```
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Optionally, you can also provide an Org Id
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```cli
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dotnet user-secrets set "AIServices:OpenAIEmbeddings:OrgId" "<your org id>"
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```
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### Azure AI Search
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If you want to use Azure AI Search as your vector store, you will need to create an instance of Azure AI Search and add
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the following secrets here:
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```cli
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dotnet user-secrets set "VectorStores:AzureAISearch:Endpoint" "https://<yourservice>.search.windows.net"
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dotnet user-secrets set "VectorStores:AzureAISearch:ApiKey" "<yoursecret>"
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```
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### Azure CosmosDB MongoDB
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If you want to use Azure CosmosDB MongoDB as your vector store, you will need to create an instance of Azure CosmosDB MongoDB and add
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the following secrets here:
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```cli
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dotnet user-secrets set "VectorStores:CosmosMongoDB:ConnectionString" "<yourconnectionstring>"
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dotnet user-secrets set "VectorStores:CosmosMongoDB:DatabaseName" "<yourdbname>"
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```
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### Azure CosmosDB NoSQL
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If you want to use Azure CosmosDB NoSQL as your vector store, you will need to create an instance of Azure CosmosDB NoSQL and add
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the following secrets here:
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```cli
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dotnet user-secrets set "VectorStores:CosmosNoSql:ConnectionString" "<yourconnectionstring>"
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dotnet user-secrets set "VectorStores:CosmosNoSql:DatabaseName" "<yourdbname>"
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```
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### Qdrant
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If you want to use Qdrant as your vector store, you will need to have an instance of Qdrant available.
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You can use the following command to start a Qdrant instance in docker, and this will work with the default configured settings:
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```cli
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docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
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```
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If you want to use a different instance of Qdrant, you can update the appsettings.json file or add the following secrets to reconfigure:
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```cli
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dotnet user-secrets set "VectorStores:Qdrant:Host" "<yourservice>"
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dotnet user-secrets set "VectorStores:Qdrant:Port" "6334"
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dotnet user-secrets set "VectorStores:Qdrant:Https" "true"
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dotnet user-secrets set "VectorStores:Qdrant:ApiKey" "<yoursecret>"
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```
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### Redis
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If you want to use Redis as your vector store, you will need to have an instance of Redis available.
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You can use the following command to start a Redis instance in docker, and this will work with the default configured settings:
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```cli
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docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
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```
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If you want to use a different instance of Redis, you can update the appsettings.json file or add the following secret to reconfigure:
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```cli
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dotnet user-secrets set "VectorStores:Redis:ConnectionConfiguration" "<yourredisconnectionconfiguration>"
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```
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### Weaviate
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If you want to use Weaviate as your vector store, you will need to have an instance of Weaviate available.
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You can use the following command to start a Weaviate instance in docker, and this will work with the default configured settings:
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```cli
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docker run -d --name weaviate -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:1.26.4
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```
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If you want to use a different instance of Weaviate, you can update the appsettings.json file or add the following secret to reconfigure:
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```cli
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dotnet user-secrets set "VectorStores:Weaviate:Endpoint" "<yourweaviateurl>"
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```
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