https://github.com/abhirockzz/langchain-go-postgresql-vectorstore

langchain Vector Store implementation based for PostgreSQL pgvector
https://github.com/abhirockzz/langchain-go-postgresql-vectorstore

generative-ai golang langchain postgresql

Last synced: 8 months ago
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langchain Vector Store implementation based for PostgreSQL pgvector

• Host: GitHub
• URL: https://github.com/abhirockzz/langchain-go-postgresql-vectorstore
• Owner: abhirockzz
• Created: 2023-10-03T04:18:49.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-10-06T10:34:11.000Z (over 2 years ago)
• Last Synced: 2025-03-24T00:24:26.213Z (over 1 year ago)
• Topics: generative-ai, golang, langchain, postgresql
• Language: Go
• Homepage:
• Size: 24.4 KB
• Stars: 9
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# PostgreSQL Vector Database implementation for LangChain Go

[langchaingo](https://github.com/tmc/langchaingo) extension to use [pgvector](https://github.com/pgvector/pgvector) as a vector database for your Go applications. It uses the [pgvector-go](https://github.com/pgvector/pgvector-go) library along with [pgx](https://github.com/jackc/pgx) driver.

You can use this in your LangChain applications as a standalone vector database or more likely, as part of a chain. For example, in a RAG implementation:

```go

    import(

        "github.com/abhirockzz/langchain-go-postgresql-vectorstore/pgvector"

         //...

    )

    func ragToRiches(){

        

        bedrockClaudeLLM, err := claude.New("us-east-1")

        tableName := "test_table"

        textColumnName := "text_data"

        embeddingStoreColumnName := "embedding_data"

        amazonTitanEmbedder, err := titan_embedding.New("us-east-1")

        pgVectorStore, err := pgvector.New(pgConnString, 

                                        tableName, 

                                        embeddingStoreColumnName, 

                                        textColumnName, 

                                        false, 

                                        amazonTitanEmbedder)

        result, err := chains.Run(

            context.Background(),

            chains.NewRetrievalQAFromLLM(

                bedrockClaudeLLM,

                vectorstores.ToRetriever(pgVectorStore, numOfResults),

            ),

            question,

            chains.WithMaxTokens(8091),

        )

    }

```