feat(langchain): Session 10.2 — LangChain Vector Store (Python + JS)

- BaraDBStore for Python: add_texts, similarity_search, max_marginal_relevance_search, delete
- BaraDBStore for JS: addDocuments, addTexts, similaritySearch, maxMarginalRelevanceSearch, delete
- Both use hybrid_search() / hybrid_search_filtered() for vector+FTS+RRF
- Multi-tenant support via tenant_id session variable + metadata filter
- Embedding function is injected by user (OpenAI, sentence-transformers, etc.)
- MMR reranking for result diversity

clients/python/baradb/langchain_README.md

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+# BaraDB LangChain Integration
+
+## Python
+
+```python
+import asyncio
+from baradb import Client
+from baradb.langchain_store import BaraDBStore
+
+async def main():
+    client = Client("localhost", 9472)
+    await client.connect()
+
+    # Use OpenAI, sentence-transformers, or any embedder
+    def embed(text: str) -> list[float]:
+        # Replace with your embedding model
+        return [0.1, 0.2, 0.3]
+
+    store = BaraDBStore(
+        client=client,
+        table="knowledge",
+        embedding_function=embed,
+        tenant_id="tenant-a",
+        vector_dimension=3,
+    )
+
+    await store.add_texts(["BaraDB is fast", "Vector search in SQL"])
+    results = await store.similarity_search("fast database", k=5)
+    for doc, score in results:
+        print(doc.page_content, score)
+
+asyncio.run(main())
+```
+
+## JavaScript
+
+```javascript
+const { Client } = require('./baradb');
+const { BaraDBStore } = require('./baradb_langchain');
+
+async function main() {
+    const client = new Client('localhost', 9472);
+    await client.connect();
+
+    const store = new BaraDBStore({
+        client,
+        table: 'knowledge',
+        embeddingFunction: async (text) => [0.1, 0.2, 0.3],
+        tenantId: 'tenant-a',
+        vectorDimension: 3,
+    });
+
+    await store.addTexts(['BaraDB is fast', 'Vector search in SQL']);
+    const results = await store.similaritySearch('fast database', 5);
+    console.log(results);
+}
+
+main();
+```
+
+## Features
+
+- `add_texts()` / `addDocuments()` — auto-generate embeddings + INSERT
+- `similarity_search()` — uses `hybrid_search()` (vector + FTS + RRF)
+- `max_marginal_relevance_search()` — MMR reranking for diversity
+- `delete()` — remove by IDs
+- Multi-tenant — `tenant_id` sets session variable + metadata filter