mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 15:22:30 +00:00
Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
47 lines
13 KiB
Plaintext
47 lines
13 KiB
Plaintext
{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}
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export const PyAsyncOpenaiEmbeddings = "db = await lancedb.connect_async(uri)\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = await db.create_table(\"words\", schema=Words, mode=\"overwrite\")\nawait table.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = await (await table.search(query)).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
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export const PyCreateEmbeddingFunction = "func = get_registry().get(\"openai\").create(\n name=\"text-embedding-3-small\",\n max_retries=7,\n)\n";
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export const PyEmbeddingFunction = "from functools import cached_property\n\nfrom lancedb.embeddings import TextEmbeddingFunction, register\n\nclass MyEmbeddingModel:\n def __init__(self, model_name: str):\n self.model_name = model_name\n\n def encode(self, texts: list[str]) -> list[list[float]]:\n return [[1.0, 2.0, 3.0] for _ in texts]\n\n@register(\"my-embedder\")\nclass MyTextEmbedder(TextEmbeddingFunction):\n model_name: str = \"my-model\"\n\n def generate_embeddings(self, texts: list[str]) -> list[list[float]]:\n # Your embedding logic here\n return self._model.encode(texts)\n\n def ndims(self) -> int:\n # Return the dimensionality of the embeddings\n return len(self.generate_embeddings([\"test\"])[0])\n\n @cached_property\n def _model(self) -> MyEmbeddingModel:\n # Initialize your model once\n return MyEmbeddingModel(self.model_name)\n";
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export const PyImports = "from lancedb.pydantic import LanceModel, Vector\nfrom lancedb.embeddings import get_registry\n";
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export const PyManualQueryEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery_vector = func.generate_embeddings([\"greetings\"])[0]\n# --8<-- [start:manual_query_search]\n# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n# --8<-- [end:manual_query_search]\n";
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export const PyManualQuerySearch = "# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
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export const PyOpenaiEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n";
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export const PyRegisterDevice = "import torch\n\nregistry = get_registry()\nif torch.cuda.is_available():\n registry.set_var(\"device\", \"cuda\")\n\nfunc = registry.get(\"huggingface\").create(device=\"$var:device:cpu\")\n";
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export const PyRegisterSecret = "registry = get_registry()\nregistry.set_var(\"api_key\", \"sk-...\")\n\nfunc = registry.get(\"openai\").create(api_key=\"$var:api_key\")\n";
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export const TsCreateEmbeddingFunction = "const func = getRegistry().get(\"openai\")!.create({\n model: \"text-embedding-3-small\",\n});\n";
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export const TsEmbeddingFunction = "const db = await lancedb.connect(databaseDir);\n\n@register(\"my_embedding\")\nclass MyEmbeddingFunction extends EmbeddingFunction<string> {\n constructor(optionsRaw = {}) {\n super();\n const options = this.resolveVariables(optionsRaw);\n // Initialize using options\n }\n ndims() {\n return 3;\n }\n protected getSensitiveKeys(): string[] {\n return [];\n }\n embeddingDataType(): Float {\n return new Float32();\n }\n async computeQueryEmbeddings(_data: string) {\n // This is a placeholder for a real embedding function\n return [1, 2, 3];\n }\n async computeSourceEmbeddings(data: string[]) {\n // This is a placeholder for a real embedding function\n return Array.from({ length: data.length }).fill([\n 1, 2, 3,\n ]) as number[][];\n }\n}\n\nconst func = new MyEmbeddingFunction();\n\nconst data = [{ text: \"pepperoni\" }, { text: \"pineapple\" }];\n\n// Option 1: manually specify the embedding function\nconst table = await db.createTable(\"vectors\", data, {\n embeddingFunction: {\n function: func,\n sourceColumn: \"text\",\n vectorColumn: \"vector\",\n },\n mode: \"overwrite\",\n});\n\n// Option 2: provide the embedding function through a schema\n\nconst schema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\n\nconst table2 = await db.createTable(\"vectors2\", data, {\n schema,\n mode: \"overwrite\",\n});\n";
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export const TsImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport \"@lancedb/lancedb/embedding/openai\";\nimport { LanceSchema, getRegistry, register } from \"@lancedb/lancedb/embedding\";\nimport { EmbeddingFunction } from \"@lancedb/lancedb/embedding\";\nimport { type Float, Float32, Utf8 } from \"apache-arrow\";\n";
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export const TsManualQueryEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n .get(\"openai\")\n ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst queryVector = await func.computeQueryEmbeddings(\"greetings\");\n// --8<-- [start:manual_query_search]\n// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n// --8<-- [end:manual_query_search]\n";
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export const TsManualQuerySearch = "// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n";
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export const TsOpenaiEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n .get(\"openai\")\n ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst query = \"greetings\";\nconst actual = (await tbl.search(query).limit(1).toArray())[0];\n";
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export const TsRegisterModelFallback = "const registry = getRegistry();\nregistry.setVar(\"openai_model\", \"text-embedding-3-large\");\n\nconst func = registry.get(\"openai\")!.create({\n model: \"$var:openai_model:text-embedding-3-small\",\n});\n";
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export const TsRegisterSecret = "const registry = getRegistry();\nregistry.setVar(\"api_key\", \"sk-...\");\n\nconst func = registry.get(\"openai\")!.create({\n apiKey: \"$var:api_key\",\n});\n";
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export const RsCreateEmbeddingFunction = "use std::sync::Arc;\n\nuse lancedb::embeddings::openai::OpenAIEmbeddingFunction;\n\nlet api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\nlet embedding = Arc::new(\n OpenAIEmbeddingFunction::new_with_model(api_key, \"text-embedding-3-small\")\n .expect(\"failed to create OpenAI embedding function\"),\n);\n";
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export const RsEmbeddingFunction = "use std::{borrow::Cow, sync::Arc};\n\nuse arrow_array::{Array, FixedSizeListArray, Float32Array};\nuse arrow_schema::{DataType, Field, Schema};\nuse lancedb::{\n connect,\n embeddings::{EmbeddingDefinition, EmbeddingFunction},\n Result,\n};\n\n#[derive(Debug, Clone)]\nstruct MyTextEmbedder {\n dim: usize,\n}\n\nimpl EmbeddingFunction for MyTextEmbedder {\n fn name(&self) -> &str {\n \"my-embedder\"\n }\n\n fn source_type(&self) -> Result<Cow<'_, DataType>> {\n Ok(Cow::Owned(DataType::Utf8))\n }\n\n fn dest_type(&self) -> Result<Cow<'_, DataType>> {\n Ok(Cow::Owned(DataType::new_fixed_size_list(\n DataType::Float32,\n self.dim as i32,\n true,\n )))\n }\n\n fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {\n let values = Arc::new(Float32Array::from(vec![1.0f32; source.len() * self.dim]));\n let field = Arc::new(Field::new(\"item\", DataType::Float32, true));\n Ok(Arc::new(FixedSizeListArray::new(\n field,\n self.dim as i32,\n values,\n None,\n )))\n }\n\n fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {\n unimplemented!()\n }\n}\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n db.embedding_registry()\n .register(\"my-embedder\", Arc::new(MyTextEmbedder { dim: 3 }))?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n db.create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\n \"text\",\n \"my-embedder\",\n Some(\"vector\"),\n ))?\n .execute()\n .await?;\n\n Ok(())\n}\n";
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export const RsManualQueryEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n connect,\n embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n query::{ExecutableQuery, QueryBase},\n Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n api_key,\n \"text-embedding-3-large\",\n )?);\n db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n let table = db\n .create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n .execute()\n .await?;\n\n table\n .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n .execute()\n .await?;\n\n // Manually generate embeddings for the query (Enterprise path)\n let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n let query_vector = embedding.compute_query_embeddings(query)?;\n // --8<-- [start:manual_query_search]\n // query_vector is assumed to already be generated by your embedding function\n let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n while let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n }\n // --8<-- [end:manual_query_search]\n\n Ok(())\n}\n";
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export const RsManualQuerySearch = "// query_vector is assumed to already be generated by your embedding function\nlet mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\nwhile let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n}\n";
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export const RsOpenaiEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n connect,\n embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n query::{ExecutableQuery, QueryBase},\n Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n api_key,\n \"text-embedding-3-large\",\n )?);\n\n db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n let table = db\n .create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n .execute()\n .await?;\n\n table\n .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n .execute()\n .await?;\n\n let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n let query_vector = embedding.compute_query_embeddings(query)?;\n let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n while let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n }\n\n Ok(())\n}\n";
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