Vector Databases and LangChain

A vector database stores and queries high-dimensional vectors, representing data points in a mathematical space. Unlike traditional databases that work well with structured data (like SQL), vector databases handle unstructured data by leveraging vector embeddings.

What are vector embeddings?

Vector embeddings are?a way to convert words sentences and other data into numbers that capture their meaning and relationships. These embeddings capture the semantic meaning of the data, allowing for more nuanced and accurate retrievals. I liked the examples given in this link about creating vector embeddings. https://www.pinecone.io/learn/vector-embeddings/

Key Features of Vector Databases:

1. High-Dimensional Storage: Capable of storing vectors with hundreds or thousands of dimensions.
2. Similarity Search: Efficiently find vectors similar to a given query vector using metrics like Euclidean distance, cosine similarity, etc.
3. Scalability: Handle large volumes of data and perform real-time searches.
4. Versatility: Useful in various applications, including natural language processing (NLP), image recognition, recommendation systems, and more.

One of the critical components of LangChain is its retriever module, which leverages vector databases to enhance information retrieval.

I have created a sample program that shows Loading, Transforming, and embedding

1. how the data is ingested in the form of PDF, by using LangChain’s third-party PDF- loader
2. The further process is done using LangChain’s text splitter (Recursive text splitter) – Transform
3. Using the OllamaEmbeddings, I demonstrate how I used the transformed text to store into a vector DB- Embed
4. After successfully storing into the DB, I query using LangChain’s similarity_search

I am using the PDF in this link: https://main.icmr.nic.in/sites/default/files/upload_documents/ICMR_Guidelines_for_Management_of_Type_1_Diabetes.pdf

Visit here to see the output of the code

View the detailed post here

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