This Langflow flow implements a comprehensive local RAG pipeline that processes documents, stores embeddings, and generates answers entirely on-premises.

The workflow begins by accepting local files through file uploads or directory scanning. Document loader components process various file formats including PDFs, Word documents, text files, Markdown, and other structured documents. Advanced parsing bundles like Docling or Unstructured extract text content while preserving document structure and metadata. All file processing occurs locally without sending data to external services.

Text splitting components break documents into manageable chunks suitable for embedding generation. Chunking strategies can be configured based on document type, size, and semantic boundaries. Split Text components handle chunk size, overlap, and separation logic to ensure optimal retrieval performance while maintaining context.

Embedding generation components use local embedding models to convert document chunks into vector representations. The system can use open-source embedding models that run entirely on local hardware, ensuring that document content never leaves the organization's infrastructure. Embedding models are loaded and executed locally, generating vector representations for all document chunks.

ChromaDB integration components store the generated embeddings in a local vector database. ChromaDB runs as a local service, maintaining all vector data and metadata on-premises. The vector store indexes document chunks with their embeddings, enabling efficient similarity search and retrieval operations. All database operations occur locally without network communication to external services.

Query processing components handle user questions and convert them into search queries. When users submit questions, the system generates embeddings for the queries using the same local embedding model, ensuring consistency with document embeddings.

Retrieval components perform similarity search in ChromaDB to find the most relevant document chunks for each query. The system retrieves top-k most similar chunks based on vector similarity scores, ensuring that answers are grounded in the actual knowledge base content.

Ollama integration components provide local LLM inference for answer generation. Ollama runs locally and hosts open-source language models that can be executed entirely on-premises. The system sends retrieved document chunks and user queries to the local Ollama instance, which generates answers based on the provided context. All LLM processing occurs locally without data transmission to external AI services.

Prompt template components structure the RAG prompts, combining retrieved context with user questions to guide the local LLM in generating accurate, contextually relevant answers. The prompts instruct the model to base answers solely on the provided context and indicate when information is not available in the knowledge base.

Response formatting components process the LLM outputs and deliver final answers to users. The system can format responses in various ways including plain text, structured JSON, or Markdown, depending on application requirements. All responses are generated locally without any external API calls.

Vector store management components enable ongoing maintenance of the knowledge base, including adding new documents, updating existing content, and managing document collections. The system supports incremental updates, allowing organizations to continuously expand their local knowledge base without reprocessing all documents.

The flow can be extended using additional Langflow components to enhance local RAG capabilities. You can integrate additional local vector databases like FAISS or Qdrant for different storage requirements or performance characteristics. Local embedding models can be swapped or fine-tuned for domain-specific applications. Batch processing components enable efficient indexing of large document collections, while webhook integrations can trigger automatic updates when new documents are added to local directories. Structured Output components can format responses for integration with local applications, while API Request nodes can connect to on-premises systems for document ingestion. Advanced implementations might incorporate local fine-tuning capabilities, multi-model ensemble approaches, or integration with local authentication and authorization systems for secure access control.