https://github.com/liteobject/embeddings_with_chromadb

https://github.com/liteobject/embeddings_with_chromadb

chromadb embeddings llm vector-database

Last synced: 6 days ago
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• Host: GitHub
• URL: https://github.com/liteobject/embeddings_with_chromadb
• Owner: LiteObject
• Created: 2024-05-15T02:41:55.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-07-09T19:25:51.000Z (6 months ago)
• Last Synced: 2024-11-07T22:13:09.864Z (about 2 months ago)
• Topics: chromadb, embeddings, llm, vector-database
• Language: Jupyter Notebook
• Homepage:
• Size: 36.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# Embeddings with Chromadb

Embeddings have revolutionized the way we represent and process data in machine learning and natural language processing tasks. They allow us to transform high-dimensional, unstructured data into compact, meaningful vector representations. ChromaDB, a powerful vector database, takes embeddings to the next level by providing efficient storage, retrieval, and similarity search capabilities. In this blog post, we'll explore how ChromaDB empowers developers to harness the full potential of embeddings.

## What is embeddings?

## Setup Chroma DB

### Install chromadb package

    pip install chromadb

### Get the chroma client

    import chromadb

    chroma_client = chromadb.Client()

### Create a collection

Collections are where you'll store your embeddings, documents, and any additional metadata. You can create a collection with a name:

    collection = chroma_client.create_collection(name="my_collection")

### Add some text documents to the collection

Chroma will store your text and handle embedding and indexing automatically. You can also customize the embedding model.

```python

    collection.add(

        documents=[

            "This is a document about pineapple",

            "This is a document about oranges"

        ],

        ids=["id1", "id2"]

    )

```

###  Query the collection

```python

results = collection.query(

    query_texts=["This is a query document about hawaii"], # Chroma will embed this for you

    n_results=2 # how many results to return

)

print(results)

```

### Inspect Results

## Links

- [ChromaDB: Getting Started](https://docs.trychroma.com/getting-started)