Semantic Search with Chroma DB: Open-Source Embedding Database

📚 Chroma DB is an open-source Vector DB used for semantic search and document embedding.

🔍 To use Chroma DB, you first need to import the library and create a collection to store your documents.

📂 You can add documents to the collection with additional metadata for better organization and retrieval.

To implement semantic search with a vector DB, metadata is used to categorize each document.

IDs are assigned to each document in the vector DB for identification.

Queries can be made to the vector DB to retrieve the most relevant document based on semantic matching.

The video discusses the process of indexing and querying documents using an open-source vector database called Chroma DB.

The speaker explains how to index multiple documents by iterating through the files, reading their content, and storing it in a dictionary with metadata.

The video demonstrates the creation of a list of documents, metadata, and IDs for the indexed files, which can then be used for querying relevant information.

🔍 Semantic search with open-source Vector DB allows users to search for relevant documents based on query keywords.

🔧 Users can customize the embedding model used for semantic search, such as using OpenAI's paraphrase model.

🐾 The implementation involves creating a collection of documents and adding metadata and IDs for efficient retrieval.

🔍 Using an open-source vector database, such as Chroma DB, allows for semantic search by converting text into vector representations or embeddings.

📚 The process involves downloading a specific model, encoding the document content into embeddings, and storing the embeddings along with metadata in a collection.

💡 With the embeddings stored, queries can be performed using the vector representations, which provides accurate and relevant results.

💡 Adding filters to improve search results by specifying conditions.

🔍 Using keyword conditions to retrieve relevant document chunks.

📄 Applying metadata conditions to search within specific document sections.

💡 Chroma DB allows for semantic search using metadata keys and values.

🔎 You can use Chroma DB to search for specific chunks of data within files.

💾 Chroma DB can be saved and downloaded for later use.