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AnushandMike Jang
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* docs: langchain4j, langchaingo * docs: removed " bedrock.md * Apply suggestions from code review Co-authored-by: Mike Jang <michael@linuxexam.com> * Rename LangchainGo.md to langchain-go.md * docs: URL description update Langchain4J * Update qdrant-landing/content/documentation/frameworks/langchain4j.md Co-authored-by: Mike Jang <michael@linuxexam.com> --------- Co-authored-by: Mike Jang <michael@linuxexam.com>
68 lines
1.5 KiB
Markdown
68 lines
1.5 KiB
Markdown
---
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title: Langchain Go
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weight: 120
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---
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# Langchain Go
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[Langchain Go](https://tmc.github.io/langchaingo/docs/) is a framework for developing data-aware applications powered by language models in Go.
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You can use Qdrant as a vector store in Langchain Go.
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## Setup
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Install the `langchain-go` project dependency
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```bash
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go get -u github.com/tmc/langchaingo
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```
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## Usage
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Before you use the following code sample, customize the following values for your configuration:
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- `YOUR_QDRANT_REST_URL`: If you've set up Qdrant using the [Quick Start](/documentation/quick-start/) guide,
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set this value to `http://localhost:6333`.
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- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/concepts/collections) guide to create or
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list collections.
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```go
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import (
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"fmt"
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"log"
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"github.com/tmc/langchaingo/embeddings"
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"github.com/tmc/langchaingo/llms/openai"
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"github.com/tmc/langchaingo/vectorstores"
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"github.com/tmc/langchaingo/vectorstores/qdrant"
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)
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llm, err := openai.New()
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if err != nil {
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log.Fatal(err)
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}
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e, err := embeddings.NewEmbedder(llm)
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if err != nil {
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log.Fatal(err)
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}
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url, err := url.Parse("YOUR_QDRANT_REST_URL")
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if err != nil {
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log.Fatal(err)
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}
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store, err := qdrant.New(
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qdrant.WithURL(*url),
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qdrant.WithCollectionName("YOUR_COLLECTION_NAME"),
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qdrant.WithEmbedder(e),
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)
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if err != nil {
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log.Fatal(err)
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}
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```
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## Further Reading
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- You can find usage examples of Langchain Go [here](https://github.com/tmc/langchaingo/tree/main/examples).
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