As the capabilities of LLMs (Large Language Models) and adjacent tools like embedding models grew significantly over the past year, more and more developers are considering integrating LLMs into their applications. Since LLMs often require dedicated hardware and significant compute resources, they are most commonly packaged as network services that provide APIs for access. This is how the APIs for leading LLMs like OpenAI or Google Gemini work; even run-your-own-LLM tools like Ollama wrap the LLM in a REST API for local consumption. Moreover, developers who take advantage of LLMs in their applications often require supplementary tools like Vector Databases, which are most commonly deployed as network services as well. In other words, LLM-powered applications are a lot like other modern cloud-native applications: they require excellent support for REST and RPC protocols, concurrency and performance. These just so happen to be the areas where Go excels, making it a fantastic language for writing LLM-powered applications. This blog post works through an example of using Go for a simple LLM-powered application. It starts by describing the problem the demo application is solving, and proceeds by presenting several variants of the application that all accomplish the same task, but use different packages to implement it. All the code for the demos of this post is available online. A RAG server for Q&A A common LLM-powered application technique is RAG - Retrieval Augmented Generation. RAG is one of the most scalable ways of customizing an LLM’s knowledge base for domain-specific interactions. We’re going to build a RAG server in Go. This is an HTTP server that provides two operations to users: Add a document to the knowledge base
Ask an LLM a question about this knowledge base In a typical real-world scenario, users would add a corpus of documents to the server, and proceed to ask it questions. For example, a company can fill up the RAG server’s knowledge base with internal documentation and use it to provide LLM-powered Q&A capabilities to internal users. Here’s a diagram showing the interactions of our server with the external world: In addition to the user sending HTTP requests (the two operations described above), the server interacts with: An embedding model to calculate vector embeddings for the submitted documents and for user questions.
A Vector Database for storing and retrieving embeddings efficiently.
An LLM for asking questions based on context collected from the knowledge base. Concretely, the server exposes two HTTP endpoints to users: /add/: POST {"documents": [{"text": "..."}, {"text": "..."}, ...]}: submits a sequence of text documents to the server, to be added to its knowledge base. For this request, the server: Calculates a vector embedding for each document using the embedding model.
Stores the documents along with their vector embeddings in the vector DB. /query/: POST {"content": "..."}: submits a question to the server. For this request, the server: Calculates the question’s vector embedding using the embedding model.
Uses the vector DB’s similarity search to find the most relevant documents to the question in the knowledge database.
Uses simple prompt engineering to reformulate the question with the most relevant documents found in step (2) as context, and sends it to the LLM, returning its answer to the user. The services used by our demo are: Google Gemini API for the LLM and embedding model.
Weaviate for a locally-hosted vector DB; Weaviate is an open-source vector database implemented in Go. It should be very simple to replace these by other, equivalent services. In fact, this is what the second and third variants of the server are all about! We’ll start with the first variant which uses these tools directly. Using the Gemini API and Weaviate directly Both the Gemini API and Weaviate have convenient Go SDKs (client libraries), and our first server variant uses these directly. The full code of this variant is in this directory. We won’t reproduce the entire code in this blog post, but here are some notes to keep in mind while reading it: Structure: the code structure will be familiar to anyone who’s written an HTTP server in Go. Client libraries for Gemini and for Weaviate are initialized and the clients are stored in a state value that’s passed to HTTP handlers. Route registration: the HTTP routes for our server are trivial to set up using the routing enhancements introduced in Go 1.22: mux := http.NewServeMux()
mux.HandleFunc("POST /add/", server.addDocumentsHandler)
mux.HandleFunc("POST /query/", server.queryHandler) Concurrency: the HTTP handlers of our server reach out to other services over the network and wait for a response. This isn’t a problem for Go, since each HTTP handler runs concurrently in its own goroutine. This RAG server can handle a large number of concurrent requests, and the code of each handler is linear and synchronous. Batch APIs: since an /add/ request may provide a large number of documents to add to the knowledge base, the server leverages batch APIs for both embeddings (embModel.BatchEmbedContents) and the Weaviate DB (rs.wvClient.Batch) for efficiency. Using LangChain for Go Our second RAG server variant uses LangChainGo to accomplish the same task. LangChain is a popular Python framework for building LLM-powered applications. LangChainGo is its Go equivalent. The framework has some tools to build applications out of modular components, and supports many LLM providers and vector databases in a common API. This allows developers to write code that may work with any provider and change providers very easily. The full code for this variant is in this directory. You’ll notice two things when reading the code: First, it’s somewhat shorter than the previous variant. LangChainGo takes care of wrapping the full APIs of vector databases in common interfaces, and less code is needed to initialize and deal with Weaviate. Second, the LangChainGo API makes it fairly easy to switch providers. Let’s say we want to replace Weaviate by another vector DB; in our previous variant, we’d have to rewrite all the code interfacing the vector DB to use a new API. With a framework like LangChainGo, we no longer need to do so. As long as LangChainGo supports the new vector DB we’re interested in, we should be able to replace just a few lines of code in our server, since all the DBs implement a common interface: type VectorStore interface {
    AddDocuments(ctx context.Context, docs []schema.Document, options ...Option) ([]string, error)
    SimilaritySearch(ctx context.Context, query string, numDocuments int, options ...Option) ([]schema.Document, error)
} Using Genkit for Go Earlier this year, Google introduced Genkit for Go - a new open-source framework for building LLM-powered applications. Genkit shares some characteristics with LangChain, but diverges in other aspects. Like LangChain, it provides common interfaces that may be implemented by different providers (as plugins), and thus makes switching from one to the other simpler. However, it doesn’t try to prescribe how different LLM components interact; instead, it focuses on production features like prompt management and engineering, and deployment with integrated developer tooling. Our third RAG server variant uses Genkit for Go to accomplish the same task. Its full code is in this directory. This variant is fairly similar to the LangChainGo one - common interfaces for LLMs, embedders and vector DBs are used instead of direct provider APIs, making it easier to switch from one to another. In addition, deploying an LLM-powered application to production is much easier with Genkit; we don’t implement this in our variant, but feel free to read the documentation if you’re interested. Summary - Go for LLM-powered applications The samples in this post provide just a taste of what’s possible for building LLM-powered applications in Go. It demonstrates how simple it is to build a powerful RAG server with relatively little code; most important, the samples pack a significant degree of production readiness because of some fundamental Go features. Working with LLM services often means sending REST or RPC requests to a network service, waiting for the response, sending new requests to other services based on that and so on. Go excels at all of these, providing great tools for managing concurrency and the complexity of juggling network services. In addition, Go’s great performance and reliability as a Cloud-native language makes it a natural choice for implementing the more fundamental building blocks of the LLM ecosystem. For some examples, see projects like Ollama, LocalAI, Weaviate or Milvus. Credits: Eli Bendersky Photo by Verne Ho on Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. As the capabilities of LLMs (Large Language Models) and adjacent tools like embedding models grew significantly over the past year, more and more developers are considering integrating LLMs into their applications. Since LLMs often require dedicated hardware and significant compute resources, they are most commonly packaged as network services that provide APIs for access. This is how the APIs for leading LLMs like OpenAI or Google Gemini work; even run-your-own-LLM tools like Ollama wrap the LLM in a REST API for local consumption. Moreover, developers who take advantage of LLMs in their applications often require supplementary tools like Vector Databases, which are most commonly deployed as network services as well. Ollama In other words, LLM-powered applications are a lot like other modern cloud-native applications: they require excellent support for REST and RPC protocols, concurrency and performance. These just so happen to be the areas where Go excels, making it a fantastic language for writing LLM-powered applications. This blog post works through an example of using Go for a simple LLM-powered application. It starts by describing the problem the demo application is solving, and proceeds by presenting several variants of the application that all accomplish the same task, but use different packages to implement it. All the code for the demos of this post is available online . is available online A RAG server for Q&A A common LLM-powered application technique is RAG - Retrieval Augmented Generation . RAG is one of the most scalable ways of customizing an LLM’s knowledge base for domain-specific interactions. Retrieval Augmented Generation We’re going to build a RAG server in Go. This is an HTTP server that provides two operations to users: RAG server Add a document to the knowledge base Ask an LLM a question about this knowledge base Add a document to the knowledge base Ask an LLM a question about this knowledge base In a typical real-world scenario, users would add a corpus of documents to the server, and proceed to ask it questions. For example, a company can fill up the RAG server’s knowledge base with internal documentation and use it to provide LLM-powered Q&A capabilities to internal users. Here’s a diagram showing the interactions of our server with the external world: In addition to the user sending HTTP requests (the two operations described above), the server interacts with: An embedding model to calculate vector embeddings for the submitted documents and for user questions. A Vector Database for storing and retrieving embeddings efficiently. An LLM for asking questions based on context collected from the knowledge base. An embedding model to calculate vector embeddings for the submitted documents and for user questions. vector embeddings A Vector Database for storing and retrieving embeddings efficiently. An LLM for asking questions based on context collected from the knowledge base. Concretely, the server exposes two HTTP endpoints to users: /add/: POST {"documents": [{"text": "..."}, {"text": "..."}, ...]} : submits a sequence of text documents to the server, to be added to its knowledge base. For this request, the server: /add/: POST {"documents": [{"text": "..."}, {"text": "..."}, ...]} Calculates a vector embedding for each document using the embedding model. Stores the documents along with their vector embeddings in the vector DB. Calculates a vector embedding for each document using the embedding model. Stores the documents along with their vector embeddings in the vector DB. /query/: POST {"content": "..."} : submits a question to the server. For this request, the server: /query/: POST {"content": "..."} Calculates the question’s vector embedding using the embedding model. Uses the vector DB’s similarity search to find the most relevant documents to the question in the knowledge database. Uses simple prompt engineering to reformulate the question with the most relevant documents found in step (2) as context, and sends it to the LLM, returning its answer to the user. Calculates the question’s vector embedding using the embedding model. Uses the vector DB’s similarity search to find the most relevant documents to the question in the knowledge database. Uses simple prompt engineering to reformulate the question with the most relevant documents found in step (2) as context, and sends it to the LLM, returning its answer to the user. The services used by our demo are: Google Gemini API for the LLM and embedding model. Weaviate for a locally-hosted vector DB; Weaviate is an open-source vector database implemented in Go. Google Gemini API for the LLM and embedding model. Google Gemini API Weaviate for a locally-hosted vector DB; Weaviate is an open-source vector database implemented in Go . Weaviate implemented in Go It should be very simple to replace these by other, equivalent services. In fact, this is what the second and third variants of the server are all about! We’ll start with the first variant which uses these tools directly. Using the Gemini API and Weaviate directly Both the Gemini API and Weaviate have convenient Go SDKs (client libraries), and our first server variant uses these directly. The full code of this variant is in this directory . in this directory We won’t reproduce the entire code in this blog post, but here are some notes to keep in mind while reading it: Structure : the code structure will be familiar to anyone who’s written an HTTP server in Go. Client libraries for Gemini and for Weaviate are initialized and the clients are stored in a state value that’s passed to HTTP handlers. Structure Route registration : the HTTP routes for our server are trivial to set up using the routing enhancements introduced in Go 1.22: Route registration routing enhancements mux := http.NewServeMux()
mux.HandleFunc("POST /add/", server.addDocumentsHandler)
mux.HandleFunc("POST /query/", server.queryHandler) mux := http.NewServeMux()
mux.HandleFunc("POST /add/", server.addDocumentsHandler)
mux.HandleFunc("POST /query/", server.queryHandler) Concurrency : the HTTP handlers of our server reach out to other services over the network and wait for a response. This isn’t a problem for Go, since each HTTP handler runs concurrently in its own goroutine. This RAG server can handle a large number of concurrent requests, and the code of each handler is linear and synchronous. Concurrency Batch APIs : since an /add/ request may provide a large number of documents to add to the knowledge base, the server leverages batch APIs for both embeddings ( embModel.BatchEmbedContents ) and the Weaviate DB ( rs.wvClient.Batch ) for efficiency. Batch APIs /add/ batch APIs embModel.BatchEmbedContents rs.wvClient.Batch Using LangChain for Go Our second RAG server variant uses LangChainGo to accomplish the same task. LangChain is a popular Python framework for building LLM-powered applications. LangChainGo is its Go equivalent. The framework has some tools to build applications out of modular components, and supports many LLM providers and vector databases in a common API. This allows developers to write code that may work with any provider and change providers very easily. LangChain LangChainGo The full code for this variant is in this directory . You’ll notice two things when reading the code: in this directory First, it’s somewhat shorter than the previous variant. LangChainGo takes care of wrapping the full APIs of vector databases in common interfaces, and less code is needed to initialize and deal with Weaviate. Second, the LangChainGo API makes it fairly easy to switch providers. Let’s say we want to replace Weaviate by another vector DB; in our previous variant, we’d have to rewrite all the code interfacing the vector DB to use a new API. With a framework like LangChainGo, we no longer need to do so. As long as LangChainGo supports the new vector DB we’re interested in, we should be able to replace just a few lines of code in our server, since all the DBs implement a common interface : common interface type VectorStore interface {
    AddDocuments(ctx context.Context, docs []schema.Document, options ...Option) ([]string, error)
    SimilaritySearch(ctx context.Context, query string, numDocuments int, options ...Option) ([]schema.Document, error)
} type VectorStore interface {
    AddDocuments(ctx context.Context, docs []schema.Document, options ...Option) ([]string, error)
    SimilaritySearch(ctx context.Context, query string, numDocuments int, options ...Option) ([]schema.Document, error)
} Using Genkit for Go Earlier this year, Google introduced Genkit for Go - a new open-source framework for building LLM-powered applications. Genkit shares some characteristics with LangChain, but diverges in other aspects. Genkit for Go Like LangChain, it provides common interfaces that may be implemented by different providers (as plugins), and thus makes switching from one to the other simpler. However, it doesn’t try to prescribe how different LLM components interact; instead, it focuses on production features like prompt management and engineering, and deployment with integrated developer tooling. Our third RAG server variant uses Genkit for Go to accomplish the same task. Its full code is in this directory . in this directory This variant is fairly similar to the LangChainGo one - common interfaces for LLMs, embedders and vector DBs are used instead of direct provider APIs, making it easier to switch from one to another. In addition, deploying an LLM-powered application to production is much easier with Genkit; we don’t implement this in our variant, but feel free to read the documentation if you’re interested. the documentation Summary - Go for LLM-powered applications The samples in this post provide just a taste of what’s possible for building LLM-powered applications in Go. It demonstrates how simple it is to build a powerful RAG server with relatively little code; most important, the samples pack a significant degree of production readiness because of some fundamental Go features. Working with LLM services often means sending REST or RPC requests to a network service, waiting for the response, sending new requests to other services based on that and so on. Go excels at all of these, providing great tools for managing concurrency and the complexity of juggling network services. In addition, Go’s great performance and reliability as a Cloud-native language makes it a natural choice for implementing the more fundamental building blocks of the LLM ecosystem. For some examples, see projects like Ollama , LocalAI , Weaviate or Milvus . Ollama LocalAI Weaviate Milvus Credits: Eli Bendersky Credits: Eli Bendersky Photo by Verne Ho on Unsplash Photo by Verne Ho on Unsplash Verne Ho Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. This article is available on The Go Blog under a CC BY 4.0 DEED license. The Go Blog The Go Blog

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