Phase 1: Data Ingestion & Preparation

This phase focuses on converting unstructured data into a standardized, machine-readable format.

Step 1: Source Identification

Identification of knowledge assets, including PDFs, websites, Notion documents, and internal APIs.

Step 2: Data Ingestion

Extraction of raw text using specialized libraries such as Unstructured.io, BeautifulSoup, or PyMuPDF.

Step 3: Preprocessing

Data cleaning procedures including HTML tag removal, whitespace normalization, and encoding standardizations.

Step 4: Document Chunking

Segmentation of text into semantically meaningful units using recursive or semantic splitting to maintain context.

Phase 2: Embedding & Vector Storage

This phase transforms text into mathematical representations for high-speed retrieval.

Step 5: Metadata Tagging

Assigning identifiers to each chunk, such as Document ID, Section Name, and Timestamps, to enable filtered searching.

Step 6: Text Embedding

Converting text chunks into high-dimensional vectors using models like OpenAI (text-embedding-3-small) or Cohere.

Step 7: Vector Storage

Storing embeddings in specialized vector databases (e.g., Pinecone, ChromaDB, or Weaviate) for efficient similarity lookups.

Step 8: Index Optimization

Configuring database distance metrics (like Cosine Similarity) and hybrid search filters to ensure retrieval accuracy.

Phase 3: Retrieval & Query Processing

The system identifies the most relevant information based on a user’s specific request.

Step 9: Query Handling

Processing the user's natural language input, including optional rephrasing or expansion via an LLM.

Step 10: Query Embedding

Converting the user's question into a vector using the same model applied to the original document chunks.

Step 11: Top-K Similarity Search

Executing a mathematical search to find the "Top-K" most relevant chunks from the vector store.

Step 12: Context Filtering & Reranking

(Optional) Utilizing cross-encoders to re-rank retrieved chunks based on confidence, recency, or specific keyword matches.

Phase 4: Generation & Post-Processing

The final phase synthesizes the retrieved information into a coherent human response.

Step 13: Prompt Construction

Assembling a "contextual prompt" that includes the retrieved chunks, system instructions, and the user’s original query.

Step 14: LLM Completion

Sending the prompt to a Large Language Model (like GPT-4o, Claude 3, or Llama 3) to generate a response grounded in the provided data.

Step 15: Output Post-Processing

(Optional) Formatting the final response, adding source citations, or delivering the result via UI or API.