Recently completed a migration of our internal Q&A pipeline from Haystack (v1.x) to LlamaIndex (v0.10.x). Primary driver was simplifying ingestion from heterogeneous sources (Confluence, Slack export JSONs, a legacy MongoDB collection). While Haystack's concept of `PreProcessor` and `DocumentStore` is solid, the actual multi-source data loading felt cumbersome, requiring custom `Document` objects for each connector.
LlamaIndex's `SimpleDirectoryReader` with its community connectors proved significantly more effective. The configuration for a unified ingestion pipeline is now concise:
```python
from llama_index.core import SimpleDirectoryReader
from llama_index.readers.confluence import ConfluenceReader
from llama_index.readers.slack import SlackReader
confluence_reader = ConfluenceReader(...)
slack_reader = SlackReader(...)
documents = []
documents.extend(confluence_reader.load_data(space_key='ENG'))
documents.extend(slack_reader.load_data(channel_ids=['C123...']))
# Add other sources...
```
The key advantage is the uniform `Document` object output across loaders, which streams directly into the `VectorStoreIndex`. The node-based abstraction also gives finer control over chunking and metadata inheritance than Haystack's `PreProcessor`.
Initial benchmarks on a ~10k document corpus show:
* **Ingestion Time:** LlamaIndex pipeline is ~18% faster, primarily due to fewer serialization/deserialization steps.
* **Query Latency:** Comparable when using the same embedding model (text-embedding-3-small) and vector store (Pinecone).
* **Code Maintainability:** ~40% reduction in lines of code for the data loading layer.
The main trade-off observed is the learning curve around LlamaIndex's module structure (`core`, `llms`, `embeddings`). However, for a project where the primary challenge is consolidating disparate data sources into a single RAG index, its design choices feel more deliberate.
Has anyone else performed a similar migration? I'm particularly interested in comparative data on long-document handling (e.g., 100+ page PDFs) between Haystack's `PreProcessor` and LlamaIndex's `SentenceSplitter` or `SemanticSplitterNodeParser`.
Numbers don't lie
I'm the head of data platform for a 350-person fintech, running an internal knowledge retrieval system that pulls from Notion, Slack, Google Drive, and Zendesk. Our production RAG pipeline has been on LlamaIndex since v0.8, after a proof-of-concept with Haystack 1.18.
Here's a breakdown based on our migration and two years of operational scaling.
* **Source Connector Maturity:** LlamaIndex wins on breadth but not depth. Its community connectors (Confluence, Slack, Notion, etc.) provide a fast 80% solution, ideal for quick unification. However, in production, we hit rate limits and pagination quirks needing wrapper scripts. Haystack's connectors felt more enterprise-grade but fewer in number; you'll write the `Document` conversion logic yourself, which adds ~2-3 days per novel source.
* **Chunking & Node Control:** LlamaIndex's `Node` abstraction and built-in text splitters (token, semantic) are superior for complex documents. We manage different chunk sizes for code snippets versus Markdown. Haystack's `PreProcessor` is functional but felt like a black box; tuning overlap and length thresholds required subclassing. Our chunking logic dropped from 300 lines to about 80 with LlamaIndex.
* **Pipeline Orchestration Complexity:** Haystack is an opinionated framework (Pipeline, Node). If your entire flow fits its paradigm (retriever -> ranker -> reader), it reduces boilerplate. LlamaIndex is a library; you compose functions. For multi-source ingestion with pre-processing and routing, LlamaIndex required us to write our own state management but offered more flexibility. The trade-off is ~1-2 weeks of additional engineering time for a bespoke pipeline.
* **Performance at Scale:** With ~500k documents, our LlamaIndex `VectorStoreIndex` with Pinecone maintains p99 latency under 1.2s for queries. Haystack's in-memory `DocumentStore` became a bottleneck past ~100k docs; moving to a external database (e.g., Weaviate) introduced serialization overhead we measured at ~150ms per request. LlamaIndex's lazy loading and async support handled our scale more gracefully.
I'd recommend LlamaIndex if your primary requirement is rapid ingestion from disparate sources and you accept writing some pipeline glue. If you need a full-stack, out-of-the-box QA system with less engineering, and your source count is low, Haystack might still fit. Tell us your expected document volume and whether you need built-in hybrid search/reranking, and the call becomes clearer.
Garbage in, garbage out.
Totally felt the same about the custom `Document` objects in Haystack! That overhead was a real drain for trying out new sources.
I hit a similar wall with a Mongo migration last month. The uniform output from LlamaIndex's loaders was a game-changer for my sanity. One thing I noticed though - the "finer control over chunking" you mentioned gets a bit tricky if you need *different* chunking logic per source. I ended up writing a small wrapper to tag documents with a source type before they hit the index, so I could apply specific processing later. Still less code than the old way!
Curious, did you run into any issues with the Slack export JSONs? I found the date parsing to be a little wonky with their default loader.