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* docs: Reorder integrations * docs: Formatting langchain-go.md * docs: Title for index * docs: Redpanda docs (#1092)
72 lines
1.6 KiB
Markdown
72 lines
1.6 KiB
Markdown
---
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title: Langchain Go
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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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package main
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import (
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"log"
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"net/url"
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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/qdrant"
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)
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func main() {
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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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```
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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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- [Source Code](https://github.com/tmc/langchaingo/tree/main/vectorstores/qdrant)
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