adding lancedb to langchain vectorstores

comps/vectorstores/langchain/lancedb/README.md

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+# LanceDB
+
+LanceDB is an embedded vector database for AI applications. It is open source and distributed with an Apache-2.0 license.
+
+LanceDB datasets are persisted to disk and can be shared in Python.
+
+## Setup
+
+```bash
+npm install -S vectordb
+```
+
+## Usage
+
+### Create a new index from texts
+
+```python
+import os
+import tempfile
+from langchain.vectorstores import LanceDB
+from langchain.embeddings.openai import OpenAIEmbeddings
+from vectordb import connect
+
+
+async def run():
+    dir = tempfile.mkdtemp(prefix="lancedb-")
+    db = await connect(dir)
+    table = await db.create_table("vectors", [{"vector": [0] * 1536, "text": "sample", "id": 1}])
+
+    vector_store = await LanceDB.from_texts(
+        ["Hello world", "Bye bye", "hello nice world"],
+        [{"id": 2}, {"id": 1}, {"id": 3}],
+        OpenAIEmbeddings(),
+        table=table,
+    )
+
+    result_one = await vector_store.similarity_search("hello world", 1)
+    print(result_one)
+    # [ Document(page_content='hello nice world', metadata={'id': 3}) ]
+
+
+# Run the function
+import asyncio
+
+asyncio.run(run())
+```
+
+API Reference:
+
+- `LanceDB` from `@langchain/community/vectorstores/lancedb`
+- `OpenAIEmbeddings` from `@langchain/openai`
+
+### Create a new index from a loader
+
+```python
+import os
+import tempfile
+from langchain.vectorstores import LanceDB
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.document_loaders.fs import TextLoader
+from vectordb import connect
+
+# Create docs with a loader
+loader = TextLoader("src/document_loaders/example_data/example.txt")
+docs = loader.load()
+
+
+async def run():
+    dir = tempfile.mkdtemp(prefix="lancedb-")
+    db = await connect(dir)
+    table = await db.create_table("vectors", [{"vector": [0] * 1536, "text": "sample", "source": "a"}])
+
+    vector_store = await LanceDB.from_documents(docs, OpenAIEmbeddings(), table=table)
+
+    result_one = await vector_store.similarity_search("hello world", 1)
+    print(result_one)
+    # [
+    #   Document(page_content='Foo\nBar\nBaz\n\n', metadata={'source': 'src/document_loaders/example_data/example.txt'})
+    # ]
+
+
+# Run the function
+import asyncio
+
+asyncio.run(run())
+```
+
+API Reference:
+
+- `LanceDB` from `@langchain/community/vectorstores/lancedb`
+- `OpenAIEmbeddings` from `@langchain/openai`
+- `TextLoader` from `langchain/document_loaders/fs/text`
+
+### Open an existing dataset
+
+```python
+import os
+import tempfile
+from langchain.vectorstores import LanceDB
+from langchain.embeddings.openai import OpenAIEmbeddings
+from vectordb import connect
+
+
+async def run():
+    uri = await create_test_db()
+    db = await connect(uri)
+    table = await db.open_table("vectors")
+
+    vector_store = LanceDB(OpenAIEmbeddings(), table=table)
+
+    result_one = await vector_store.similarity_search("hello world", 1)
+    print(result_one)
+    # [ Document(page_content='Hello world', metadata={'id': 1}) ]
+
+
+async def create_test_db():
+    dir = tempfile.mkdtemp(prefix="lancedb-")
+    db = await connect(dir)
+    await db.create_table(
+        "vectors",
+        [
+            {"vector": [0] * 1536, "text": "Hello world", "id": 1},
+            {"vector": [0] * 1536, "text": "Bye bye", "id": 2},
+            {"vector": [0] * 1536, "text": "hello nice world", "id": 3},
+        ],
+    )
+    return dir
+
+
+# Run the function
+import asyncio
+
+asyncio.run(run())
+```
+
+API Reference:
+
+- `LanceDB` from `@langchain/community/vectorstores/lancedb`
+- `OpenAIEmbeddings` from `@langchain/openai`