dimgigov
55bc3e862a
- 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
68 lines
1.7 KiB
Markdown
68 lines
1.7 KiB
Markdown
# BaraDB LangChain Integration
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## Python
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```python
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import asyncio
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from baradb import Client
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from baradb.langchain_store import BaraDBStore
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async def main():
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client = Client("localhost", 9472)
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await client.connect()
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# Use OpenAI, sentence-transformers, or any embedder
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def embed(text: str) -> list[float]:
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# Replace with your embedding model
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return [0.1, 0.2, 0.3]
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store = BaraDBStore(
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client=client,
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table="knowledge",
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embedding_function=embed,
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tenant_id="tenant-a",
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vector_dimension=3,
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)
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await store.add_texts(["BaraDB is fast", "Vector search in SQL"])
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results = await store.similarity_search("fast database", k=5)
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for doc, score in results:
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print(doc.page_content, score)
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asyncio.run(main())
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```
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## JavaScript
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```javascript
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const { Client } = require('./baradb');
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const { BaraDBStore } = require('./baradb_langchain');
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async function main() {
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const client = new Client('localhost', 9472);
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await client.connect();
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const store = new BaraDBStore({
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client,
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table: 'knowledge',
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embeddingFunction: async (text) => [0.1, 0.2, 0.3],
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tenantId: 'tenant-a',
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vectorDimension: 3,
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});
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await store.addTexts(['BaraDB is fast', 'Vector search in SQL']);
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const results = await store.similaritySearch('fast database', 5);
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console.log(results);
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}
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main();
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```
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## Features
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- `add_texts()` / `addDocuments()` — auto-generate embeddings + INSERT
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- `similarity_search()` — uses `hybrid_search()` (vector + FTS + RRF)
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- `max_marginal_relevance_search()` — MMR reranking for diversity
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- `delete()` — remove by IDs
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- Multi-tenant — `tenant_id` sets session variable + metadata filter
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