diff --git a/README.md b/README.md
index b8a787e..4e06874 100644
--- a/README.md
+++ b/README.md
@@ -33,13 +33,21 @@ raglite query "example text" --db knowledge.db -k 8 --hybrid 0.6
   ```bash
   raglite init --db knowledge.db
   ```
-- Index a directory recursively with tags:
+- Index a directory recursively with tags and progress reporting:
   ```bash
-  raglite index docs --db knowledge.db --tags project,internal --recursive
+  raglite index docs --db knowledge.db --tags project,internal --recursive --chunk-size 400 --overlap 50
+  ```
+- Re-index only changed files by disabling recursion and pointing at a single file:
+  ```bash
+  raglite index docs/faq.md --db knowledge.db --no-recursive --skip-unchanged
   ```
 - Query for relevant chunks:
   ```bash
-  raglite query "How do I deploy?" --db knowledge.db -k 5
+  raglite query "How do I deploy?" --db knowledge.db -k 5 --hybrid 0.7
+  ```
+- Filter by tag or doc id:
+  ```bash
+  raglite query "release checklist" --db knowledge.db --filter tag=internal --filter doc_id=release-notes
   ```
 - Inspect statistics:
   ```bash
@@ -49,21 +57,123 @@ raglite query "example text" --db knowledge.db -k 8 --hybrid 0.6
   ```bash
   raglite export --db knowledge.db --to export.ndjson --include-vectors
   ```
+- Vacuum the database to reclaim space:
+  ```bash
+  raglite vacuum --db knowledge.db
+  ```
 
 ## Python API
 
+The high-level API is built around `raglite_sqlite.api.RagLite`. Below is a minimal end-to-end script that indexes a directory,
+queries it, and prints rich metadata for each hit:
+
 ```python
 from raglite_sqlite import RagLite
 from raglite_sqlite.embeddings.sentence_transformers_backend import SentenceTransformersBackend
 
 rag = RagLite("knowledge.db")
-backend = SentenceTransformersBackend()
-rag.index(["tests/data"], tags="demo", embedding_backend=backend)
-results = rag.search("example text", embedding_backend=backend)
+backend = SentenceTransformersBackend(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+rag.index(
+    paths=["tests/data"],
+    tags="demo",
+    embedding_backend=backend,
+    chunker="recursive",
+    chunk_size_tokens=384,
+    chunk_overlap_tokens=48,
+)
+
+results = rag.search(
+    query="example text",
+    embedding_backend=backend,
+    k=5,
+    hybrid_weight=0.6,
+    max_per_doc=2,
+)
+
 for item in results:
-    print(item["score"], item["snippet"])
+    print(f"{item['score']:.3f} | {item['doc_id']} | {item['section'] or 'No section'}")
+    print(item["snippet"])
+    print("-" * 40)
 ```
 
+### Advanced ingestion tips
+
+- **Custom parsers and options** – pass `parser_opts` to `RagLite.index` to control parser behaviour (e.g. CSV column filters).
+- **Chunking strategies** – choose between `fixed_tokens` (uniform splits) and `recursive` (paragraph-aware) chunkers.
+- **Embedding reuse** – keep `skip_unchanged=True` (default) to leverage hashing + cache table for faster re-index runs.
+- **Multiple models** – provide different `model_name` values per run; embeddings are cached per `(sha256, model)` pair.
+- **Export/backup** – `raglite export` produces NDJSON that can be restored or version-controlled for auditing.
+
+### Operating the database
+
+- SQLite is safe to sync via file-sharing tools (Dropbox, Syncthing) when only one process writes at a time.
+- Use `raglite vacuum` periodically after large deletions to compact the file.
+- WAL mode is enabled automatically; consider copying the `.db` + `-wal` file pair while the app is running.
+- For cloud backups, store the DB file and optionally NDJSON exports in object storage.
+
+## Ecosystem Integrations
+
+RagLite ships with lightweight adapters so you can plug the SQLite-backed retriever into popular orchestration frameworks.
+
+### LangChain
+
+```python
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+
+from raglite_sqlite import RagLite
+from raglite_sqlite.adapters.langchain import RagLiteRetriever
+from raglite_sqlite.embeddings.sentence_transformers_backend import SentenceTransformersBackend
+
+rag = RagLite("knowledge.db")
+backend = SentenceTransformersBackend()
+
+# Ensure documents are indexed before wiring up the retriever.
+rag.index(["tests/data"], embedding_backend=backend)
+
+retriever = RagLiteRetriever(rag, embedding_backend=backend, k=6, hybrid_weight=0.5)
+
+qa_chain = RetrievalQA.from_chain_type(
+    llm=OpenAI(),
+    retriever=retriever,
+    chain_type="stuff",
+)
+
+response = qa_chain.run("Summarize the sample docs")
+print(response)
+```
+
+The adapter defers LangChain imports until used, keeping the core package lightweight. You can also supply filters (`tag=...`)
+via the retriever call (`retriever.get_relevant_documents(query, filters={"tags": "internal"})`).
+
+### LlamaIndex
+
+```python
+from llama_index.core import ServiceContext, VectorStoreIndex
+from llama_index.llms import OpenAI
+
+from raglite_sqlite import RagLite
+from raglite_sqlite.adapters.llamaindex import RagLiteVectorStore
+from raglite_sqlite.embeddings.sentence_transformers_backend import SentenceTransformersBackend
+
+rag = RagLite("knowledge.db")
+backend = SentenceTransformersBackend()
+
+rag.index(["tests/data"], embedding_backend=backend)
+
+service_context = ServiceContext.from_defaults(llm=OpenAI())
+vector_store = RagLiteVectorStore(rag, embedding_backend=backend)
+
+index = VectorStoreIndex.from_vector_store(vector_store, service_context=service_context)
+query_engine = index.as_query_engine(similarity_top_k=5)
+
+answer = query_engine.query("What is contained in the sample docs?")
+print(answer)
+```
+
+The vector store wrapper exposes RagLite search semantics to LlamaIndex while leaving indexing/ingest under your control.
+
 ## Design Notes
 
 - Uses SQLite with WAL mode and FTS5 for zero-config deployment.