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# Retrievers

:::info
[Conceptual Guide](https://python.langchain.com/docs/modules/data_connection/retrievers/)
:::
The concept of a "retriever" within a language or framework, particularly in blockchain contexts, refers to a mechanism designed to extract or fetch data from a designated source. In the realm of blockchain, this could involve retrieving transaction details, block information, or the states of smart contracts from the blockchain's ledger.

## Reasons for Using a Retriever:
- **Data Accessibility**: Provides a gateway for accessing data stored on the blockchain, crucial for applications needing to present this information to users or leverage it for further processing.

- **Efficiency**: Optimizes the process of fetching data, reducing latency and enhancing the performance of blockchain applications.

- **Abstraction**: Simplifies querying the blockchain by hiding its underlying complexity, offering developers a more straightforward API.

- **Integration**: Enables the seamless incorporation of blockchain data into other applications or services, broadening potential use cases and functionalities.

- **Security**: Allows applications to access blockchain data safely without direct ledger interactions, minimizing exposure to security risks.

## How To
The implementation of a retriever varies depending on the blockchain platform and the specific data requirements. However, the general process involves the following steps:

You need use a embedder, can you ollama, huggingface ..
```go
	llm, err := ollama.New(ollama.WithModel("llama2"))

	if err != nil {
		log.Fatal(err)
	}

	embedder, err := embeddings.NewEmbedder(llm)
	if err != nil {
		log.Fatal(err)
	}
```

After it chose a storage vector like pinecone, postgres, Qdrant, in example I'll use qdrant
```go
	url, err := url.Parse("http://localhost:6333")
	if err != nil {
		log.Fatal(err)
	}

	store, err := qdrant.New(
		qdrant.WithURL(*url),
		qdrant.WithCollectionName("youtube_transcript"),
		qdrant.WithEmbedder(embedder),
	)
	if err != nil {
		log.Fatal(err)
	}


```

Now Create a retriever
```go
    searchQuery := "how to make a cake"
 	optionsVector := []vectorstores.Option{
		vectorstores.WithScoreThreshold(0.80), // use for precision, when you want to get only the most relevant documents
		//vectorstores.WithNameSpace(""),            // use for set a namespace in the storage
		//vectorstores.WithFilters(map[string]interface{}{"language": "en"}), // use for filter the documents
		//vectorstores.WithEmbedder(embedder), // use when you want add documents or doing similarity search
		//vectorstores.WithDeduplicater(vectorstores.NewSimpleDeduplicater()), //  This is useful to prevent wasting time on creating an embedding
	}

	retriever := vectorstores.ToRetriever(store, 10, optionsVector...)
	// search
	resDocs, err := retriever.GetRelevantDocuments(context.Background(), searchQuery)

	if err != nil {
		log.Fatal(err)
	}
```

This is a simple example of how to use a retriever, you can use it in a lot of ways, like a chatbot, a search engine, a recommendation system, etc.

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