Embedding Pipelines

## Building an Embedding Pipeline

### The Full Pipeline

1. Ingest — Collect documents from various sources
2. Chunk — Split documents into searchable segments
3. Embed — Convert chunks into vectors
4. Index — Store vectors in a vector database
5. Serve — Handle search queries in real-time

### Chunking Strategies

Chunking is the most impactful decision in your pipeline:

Fixed-size chunking: ```python def chunk_fixed(text, chunk_size=512, overlap=50): tokens = tokenize(text) chunks = [] for i in range(0, len(tokens), chunk_size - overlap): chunks.append(detokenize(tokens[i:i + chunk_size])) return chunks ```

Semantic chunking: - Split at paragraph/section boundaries - Keep related content together - Preserve headers and context

Recursive chunking: - Try splitting by sections first - Then paragraphs, then sentences - Stop when chunks are target size

### Embedding Model Selection

Consider these factors: - Accuracy: How well does it capture semantic meaning? - Speed: Latency per embedding (local vs API) - Dimensions: Higher = more precise but more storage - Cost: Free local models vs paid APIs - Language: Multilingual support if needed

### Metadata Enrichment

Attach metadata during indexing for filtering: - Source document, URL, title - Creation/modification dates - Categories, tags, authors - Chunk position (start, middle, end of document)