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Search Results for "embeddings"

Found 32 matching component(s)

  • class MyEmbeddingFunction_v1

    A custom embedding function class that generates embeddings for documents using OpenAI's API, with built-in text summarization for long documents and token management.

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG copy.py

    embeddings openai chromadb vector-database text-summarization

  • class SimpleDataHandle

    A data handler class that manages multiple data sources with different types (dataframes, vector stores, databases) and their associated processing configurations.

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG copy.py

    data-management registry vector-store RAG dataframe

  • class OneCo_hybrid_RAG

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG copy.py

    class oneco_hybrid_rag

  • class FixedProjectVictoriaGenerator

    Fixed Project Victoria Disclosure Generator that properly handles all warranty sections.

    File: /tf/active/vicechatdev/fixed_project_victoria_generator.py

    class fixedprojectvictoriagenerator

  • class OneCo_hybrid_RAG_v1

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG_old.py

    class oneco_hybrid_rag

  • class MyEmbeddingFunction_v2

    A custom embedding function class that generates embeddings for text documents using OpenAI's embedding models, with automatic text summarization and token management for large documents.

    File: /tf/active/vicechatdev/offline_docstore_multi_vice.py

    embeddings openai chromadb text-processing summarization

  • class DocumentProcessor_v4

    Process different document types for RAG context extraction

    File: /tf/active/vicechatdev/offline_docstore_multi_vice.py

    class documentprocessor

  • class ImprovedProjectVictoriaGenerator

    Improved Project Victoria Disclosure Generator with proper reference management.

    File: /tf/active/vicechatdev/improved_project_victoria_generator.py

    class improvedprojectvictoriagenerator

  • function main_v62

    Entry point function that instantiates an ImprovedProjectVictoriaGenerator and executes its complete pipeline to generate disclosure documents.

    File: /tf/active/vicechatdev/improved_project_victoria_generator.py

    entry-point main-function disclosure-generation RAG document-generation

  • class OneCo_hybrid_RAG_v2

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG.py

    class oneco_hybrid_rag

  • class ExtensiveSearchManager

    Manages extensive search functionality including full document retrieval, summarization, and enhanced context gathering.

    File: /tf/active/vicechatdev/OneCo_hybrid_RAG.py

    class extensivesearchmanager

  • class ProjectVictoriaDisclosureGenerator

    Main class for generating Project Victoria disclosures from warranty claims.

    File: /tf/active/vicechatdev/project_victoria_disclosure_generator.py

    class projectvictoriadisclosuregenerator

  • class MyEmbeddingFunction

    Custom embedding function class that integrates OpenAI's embedding API with Chroma DB for generating vector embeddings from text documents.

    File: /tf/active/vicechatdev/project_victoria_disclosure_generator.py

    embeddings openai chroma vector-database nlp

  • class MyEmbeddingFunction_v3

    A custom embedding function class that generates embeddings for text documents using OpenAI's embedding models, with automatic text summarization and token limit handling for large documents.

    File: /tf/active/vicechatdev/offline_docstore_multi.py

    embeddings openai vector-database chromadb text-processing

  • class DocumentProcessor_v5

    Process different document types for RAG context extraction

    File: /tf/active/vicechatdev/offline_docstore_multi.py

    class documentprocessor

  • function reset_collection

    Deletes an existing ChromaDB collection and logs the operation, requiring an application restart to recreate the collection.

    File: /tf/active/vicechatdev/docchat/reset_collection.py

    chromadb vector-database database-management collection-reset cleanup

  • class DocChatEmbeddingFunction

    A custom ChromaDB embedding function that generates OpenAI embeddings with automatic text summarization for documents exceeding token limits.

    File: /tf/active/vicechatdev/docchat/document_indexer.py

    embeddings chromadb openai text-processing summarization

  • class DocumentProcessor_v7

    Process different document types for indexing

    File: /tf/active/vicechatdev/docchat/document_indexer.py

    class documentprocessor

  • class DocumentIndexer

    A class for indexing documents into ChromaDB with support for multiple file formats (PDF, Word, PowerPoint, Excel, text files), smart incremental indexing, and document chunk management.

    File: /tf/active/vicechatdev/docchat/document_indexer.py

    document-indexing vector-database chromadb embeddings pdf-processing

  • function check_dependencies

    Validates the installation status of all required Python packages for the DocChat application by attempting to import each dependency and logging the results.

    File: /tf/active/vicechatdev/docchat/integration.py

    dependency-check validation installation package-management flask

  • function load_data_from_chromadb

    Connects to a ChromaDB instance and retrieves all documents from a specified collection, returning them as a list of dictionaries with document IDs, text content, embeddings, and metadata.

    File: /tf/active/vicechatdev/chromadb-cleanup/main.py

    chromadb vector-database data-loading document-retrieval embeddings

  • function save_data_to_chromadb_v1

    Saves a list of document dictionaries to a ChromaDB collection, with support for batch processing, embeddings, and metadata storage.

    File: /tf/active/vicechatdev/chromadb-cleanup/main.py

    chromadb vector-database document-storage embeddings batch-processing

  • function load_data_from_chromadb_v1

    Retrieves all documents from a specified ChromaDB collection, including their IDs, text content, embeddings, and metadata.

    File: /tf/active/vicechatdev/chromadb-cleanup/main copy.py

    chromadb database document-retrieval vector-database embeddings

  • function save_data_to_chromadb

    Saves a list of document dictionaries to a ChromaDB vector database collection, optionally including embeddings and metadata.

    File: /tf/active/vicechatdev/chromadb-cleanup/main copy.py

    chromadb vector-database document-storage embeddings persistence

  • class TextClusterer

    A class that clusters similar documents based on their embeddings using various clustering algorithms (K-means, Agglomerative, DBSCAN) and optionally generates summaries for each cluster.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/clustering/text_clusterer.py

    clustering document-clustering embeddings machine-learning kmeans

  • function calculate_similarity

    Computes the cosine similarity between two embedding vectors, returning a normalized score between 0 and 1 that measures their directional alignment.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/utils/similarity_utils.py

    cosine-similarity vector-comparison embeddings similarity-metric machine-learning

  • function build_similarity_matrix

    Computes a pairwise cosine similarity matrix for a collection of embedding vectors, where each cell (i,j) represents the similarity between embedding i and embedding j.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/utils/similarity_utils.py

    embeddings similarity cosine-similarity matrix nlp

  • function find_similar_documents

    Identifies pairs of similar documents by comparing their embeddings and returns those exceeding a specified similarity threshold, sorted by similarity score.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/utils/similarity_utils.py

    document-similarity embedding-comparison duplicate-detection cosine-similarity nlp

  • class SimilarityCleaner

    A document cleaning class that identifies and removes duplicate or highly similar documents based on embedding vector similarity, keeping only representative documents from each similarity group.

    File: /tf/active/vicechatdev/chromadb-cleanup/src/cleaners/similarity_cleaner.py

    document-processing deduplication similarity embeddings clustering

  • class OneCo_hybrid_RAG_v3

    A class named OneCo_hybrid_RAG

    File: /tf/active/vicechatdev/vice_ai/hybrid_rag_engine.py

    class oneco_hybrid_rag

  • class ExtensiveSearchManager_v1

    Manages extensive search functionality including full document retrieval, summarization, and enhanced context gathering.

    File: /tf/active/vicechatdev/vice_ai/hybrid_rag_engine.py

    class extensivesearchmanager

  • class ChromaManager

    ChromaManager is a class that manages interactions with a Chroma vector database, providing methods to create collections, add documents with embeddings, and query for similar documents.

    File: /tf/active/vicechatdev/QA_updater/knowledge_store/chroma_manager.py

    vector-database chromadb embeddings semantic-search document-retrieval

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