A simple local agent that uses text injestion and creates a latent knowledge space that considers sentiment and semantic density before knowledge retrieval.

Semantic Density:

1. Corpus Vectors:

   • You start by creating corpus vectors for each text chunk using the trained Word2Vec model.
   • For each chunk, you calculate the mean of the word vectors for all the words in the chunk that are present in the Word2Vec vocabulary.
   • This results in a single vector representation for each chunk.

2. Smoothing Corpus Vectors:

   • You apply vector smoothing to the corpus vectors using the smooth_vectors function.
   • The smoothing process involves multiplying each corpus vector by its corresponding sentiment score and then calculating the average of the weighted vectors within a specified window size.
   • This incorporates sentiment information and local context into the vector representations.

3. Interpolation Points:

   • You define a set of interpolation points using np.linspace to create a grid of points in a two-dimensional space.
   • These points serve as the basis for the semantic density mapping.

4. Kernel Density Estimation:

   • You use the KernelDensity estimator from scikit-learn to estimate the probability density function of the smoothed corpus vectors.
   • The estimator is fitted on the smoothed corpus vectors using a Gaussian kernel with a specified bandwidth.

5. Density Map:

   • You create a density map by evaluating the fitted KernelDensity estimator on the grid of interpolation points.
   • The density map represents the semantic density of the text chunks in the two-dimensional space.
   • Higher density values indicate regions of high semantic similarity.

Sentiment:

1. Sentiment Analysis:

   • You use the TextBlob library to perform sentiment analysis on each text chunk.
   • The analyze_sentiment function calculates the sentiment polarity of each chunk, ranging from -1 (negative sentiment) to 1 (positive sentiment).
   • The sentiment scores are stored in the sentiments array.

2. Sentiment in Vector Smoothing:

   • During the vector smoothing process, you multiply the corpus vectors by their corresponding sentiment scores.
   • This incorporates sentiment information into the vector representations, giving higher weight to chunks with stronger sentiment.

Effect on Retrieval Results:

1. Semantic Search:

   • The semantic density and sentiment information are used in the semantic_search function to retrieve the most relevant text chunks based on the user's query.
   • The function calculates the cosine similarity between the query vector and the smoothed corpus vectors.
   • The top-k chunks with the highest similarity scores are returned as the search results.

2. Sentiment-Aware Results:

   • By incorporating sentiment information into the vector representations, the search results become sentiment-aware.
   • Chunks with similar sentiment to the query are more likely to be retrieved, as they have higher similarity scores due to the sentiment-weighted vectors.

3. Contextual Relevance:

   • The vector smoothing process takes into account the local context of each chunk by averaging the weighted vectors within a window.
   • This helps to capture the semantic and sentiment flow of the text, leading to more contextually relevant search results.

4. Semantic Density Influence:

   • The semantic density map provides insights into the semantic landscape of the text chunks.
   • Regions with higher density indicate areas of high semantic similarity.
   • The search results are influenced by the semantic density, as chunks in denser regions are more likely to be semantically related and relevant to the query.

By incorporating semantic density and sentiment information, your chatbot's retrieval results become more semantically meaningful and contextually relevant. The sentiment-aware vectors help to prioritize chunks with similar emotional tone, while the semantic density map guides the search towards regions of high semantic similarity.

These mechanisms enhance the chatbot's ability to understand the user's intent and provide more accurate and coherent responses based on the semantic and sentiment context of the text chunks.