Add LanceDB custom destination example code

docs/examples/custom_destination_lancedb/.dlt/config.toml

@@ -0,0 +1,2 @@
+[lancedb]
+db_path = "spotify.db"

docs/examples/custom_destination_lancedb/.dlt/example.secrets.toml

@@ -0,0 +1,7 @@
+[spotify]
+client_id = ""
+client_secret = ""
+
+# provide the openai api key here
+[destination.lancedb.credentials]
+embedding_model_provider_api_key = ""

docs/examples/custom_destination_lancedb/.gitignore

@@ -0,0 +1 @@
+spotify.db

docs/examples/custom_destination_lancedb/custom_destination_lancedb.py

@@ -0,0 +1,155 @@
+"""
+---
+title: Custom Destination with LanceDB
+description: Learn how use the custom destination to load to LanceDB.
+keywords: [destination, credentials, example, lancedb, custom destination, vectorstore, AI, LLM]
+---
+
+This example showcases a Python script that demonstrates the integration of LanceDB, an open-source vector database,
+as a custom destination within the dlt ecosystem.
+The script illustrates the implementation of a custom destination as well as the population of the LanceDB vector
+store with data from various sources.
+This highlights the seamless interoperability between dlt and LanceDB.
+
+You can get a Spotify client ID and secret from https://developer.spotify.com/.
+
+We'll learn how to:
+- Use the [custom destination](../dlt-ecosystem/destinations/destination.md)
+- Delegate the embeddings to LanceDB using OpenAI Embeddings
+"""
+
+__source_name__ = "spotify"
+
+import datetime  # noqa: I251
+import os
+from dataclasses import dataclass, fields
+from pathlib import Path
+from typing import Any
+
+import lancedb  # type: ignore
+from lancedb.embeddings import get_registry  # type: ignore
+from lancedb.pydantic import LanceModel, Vector  # type: ignore
+
+import dlt
+from dlt.common.configuration import configspec
+from dlt.common.schema import TTableSchema
+from dlt.common.typing import TDataItems, TSecretStrValue
+from dlt.sources.helpers import requests
+from dlt.sources.helpers.rest_client import RESTClient, AuthConfigBase
+
+# access secrets to get openai key and instantiate embedding function
+openai_api_key: str = dlt.secrets.get("destination.lancedb.credentials.embedding_model_provider_api_key")
+func = get_registry().get("openai").create(name="text-embedding-3-small", api_key=openai_api_key)
+
+
+class EpisodeSchema(LanceModel):
+    id: str  # noqa: A003
+    name: str
+    description: str = func.SourceField()
+    vector: Vector(func.ndims()) = func.VectorField()  # type: ignore[valid-type]
+    release_date: datetime.date
+    href: str
+
+
+@dataclass(frozen=True)
+class Shows:
+    monday_morning_data_chat: str = "3Km3lBNzJpc1nOTJUtbtMh"
+    latest_space_podcast: str = "2p7zZVwVF6Yk0Zsb4QmT7t"
+    superdatascience_podcast: str = "1n8P7ZSgfVLVJ3GegxPat1"
+    lex_fridman: str = "2MAi0BvDc6GTFvKFPXnkCL"
+
+
+@configspec
+class SpotifyAuth(AuthConfigBase):
+    client_id: str = None
+    client_secret: TSecretStrValue = None
+
+    def __call__(self, request) -> Any:
+        if not hasattr(self, "access_token"):
+            self.access_token = self._get_access_token()
+        request.headers["Authorization"] = f"Bearer {self.access_token}"
+        return request
+
+    def _get_access_token(self) -> Any:
+        auth_url = "https://accounts.spotify.com/api/token"
+        auth_response = requests.post(
+            auth_url,
+            {
+                "grant_type": "client_credentials",
+                "client_id": self.client_id,
+                "client_secret": self.client_secret,
+            },
+        )
+        return auth_response.json()["access_token"]
+
+
+@dlt.source
+def spotify_shows(
+    client_id: str = dlt.secrets.value,
+    client_secret: str = dlt.secrets.value,
+):
+    spotify_base_api_url = "https://api.spotify.com/v1"
+    client = RESTClient(
+        base_url=spotify_base_api_url,
+        auth=SpotifyAuth(client_id=client_id, client_secret=client_secret),  # type: ignore[arg-type]
+    )
+
+    for show in fields(Shows):
+        show_name = show.name
+        show_id = show.default
+        url = f"/shows/{show_id}/episodes"
+        yield dlt.resource(
+            client.paginate(url, params={"limit": 50}),
+            name=show_name,
+            write_disposition="merge",
+            primary_key="id",
+            parallelized=True,
+            max_table_nesting=0,
+        )
+
+
+@dlt.destination(batch_size=250, name="lancedb")
+def lancedb_destination(items: TDataItems, table: TTableSchema) -> None:
+    db_path = Path(dlt.config.get("lancedb.db_path"))
+    db = lancedb.connect(db_path)
+
+    # since we are embedding the description field, we need to do some additional cleaning
+    # for openai. Openai will not accept empty strings or input with more than 8191 tokens
+    for item in items:
+        item["description"] = item.get("description") or "No Description"
+        item["description"] = item["description"][0:8000]
+    try:
+        tbl = db.open_table(table["name"])
+    except FileNotFoundError:
+        tbl = db.create_table(table["name"], schema=EpisodeSchema)
+    tbl.add(items)
+
+
+if __name__ == "__main__":
+    db_path = Path(dlt.config.get("lancedb.db_path"))
+    db = lancedb.connect(db_path)
+
+    for show in fields(Shows):
+        db.drop_table(show.name, ignore_missing=True)
+
+    pipeline = dlt.pipeline(
+        pipeline_name="spotify",
+        destination=lancedb_destination,
+        dataset_name="spotify_podcast_data",
+        progress="log",
+    )
+
+    load_info = pipeline.run(spotify_shows())
+    load_info.raise_on_failed_jobs()
+    print(load_info)
+
+    row_counts = pipeline.last_trace.last_normalize_info
+    print(row_counts)
+
+    query = "French AI scientist with Lex, talking about AGI and Meta and Llama"
+    table_to_query = "lex_fridman"
+
+    tbl = db.open_table(table_to_query)
+
+    results = tbl.search(query=query).to_list()
+    assert results

docs/tools/fix_grammar_gpt.py

@@ -120,7 +120,7 @@ def get_chunk_length(chunk: List[str]) -> int:
                 temperature=0,
             )
 
-            fixed_chunks.append(response.choices[0].message.content)
+            fixed_chunks.append(response.choices[0].message.content)  # type: ignore
 
         with open(file_path, "w", encoding="utf-8") as f:
             for c in fixed_chunks: