Blog Post URL: TBD
File: basic_embedding_generator.py
Purpose: Using the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1, this code takes an example sentence and converts the text into a 384-dimensional vector embedding.
File: metrics_comparison.py
Purpose: This code utilizes the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1 to encode a list of sentences into 384-dimensional vector embeddings. It then calculates and compares the cosine similarity, Euclidean distance, and dot product between each unique pair of sentences, providing insights into their semantic relationships.
File: insert_vectors.py
Purpose: This code connects to a MongoDB database and utilizes the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1 to encode a list of sentences into 384-dimensional vector embeddings. It then inserts these embeddings, along with their corresponding texts, into a specified MongoDB collection for later retrieval.
File: create_search_index.py
Purpose: This code establishes a vector search index within a MongoDB collection, defining the search configurations for 384-dimensional vector embeddings. By creating a SearchIndexModel with cosine similarity, it allows the collection to efficiently search the vector space.
File: search_vectors.py
Purpose: This code encodes a user query into a 384-dimensional vector embedding using the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1. It then executes a vector search pipeline on the MongoDB collection to retrieve the top five most similar documents based on the query, displaying the results along with their cosine similarity scores.
File: insert_vectors.py
Purpose: This code connects to a local MongoDB database and utilizes the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1 to encode a set of example company policies into 384-dimensional vector embeddings. It then inserts these embeddings along with the corresponding policy texts into the MongoDB collection for future retrieval and analysis.
File: generate_response.py
Purpose: This code encodes a user query regarding company policies into a 384-dimensional vector embedding using the Sentence Transformers model multi-qa-MiniLM-L6-cos-v1. It retrieves all stored policies from the MongoDB database, computes the cosine similarity between the query and each policy embedding, and identifies the most similar policy. The relevant policy context is then used to generate a context-specific response using the Llama 3.2 model, ensuring data privacy by running the model locally. Alternatively, the code includes commented-out sections for using Google Gemini, providing an option for users who may prefer to utilize external API services for response generation.