ci(node): run examples in CI (#1796)

This is done as setup for a PR that will fix the OpenAI dependency
issue.

 * [x] FTS examples
 * [x] Setup mock openai
 * [x] Ran `npm audit fix`
 * [x] sentences embeddings test
 * [x] Double check formatting of docs examples
This commit is contained in:
Will Jones
2024-11-13 11:10:56 -08:00
committed by GitHub
parent 9f228feb0e
commit 0fd8a50bd7
39 changed files with 6141 additions and 1705 deletions

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@@ -47,9 +47,9 @@ Let's implement `SentenceTransformerEmbeddings` class. All you need to do is imp
=== "TypeScript"
```ts
--8<--- "nodejs/examples/custom_embedding_function.ts:imports"
--8<--- "nodejs/examples/custom_embedding_function.test.ts:imports"
--8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
--8<--- "nodejs/examples/custom_embedding_function.test.ts:embedding_impl"
```
@@ -78,7 +78,7 @@ Now you can use this embedding function to create your table schema and that's i
=== "TypeScript"
```ts
--8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
--8<--- "nodejs/examples/custom_embedding_function.test.ts:call_custom_function"
```
!!! note

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@@ -94,8 +94,8 @@ the embeddings at all:
=== "@lancedb/lancedb"
```ts
--8<-- "nodejs/examples/embedding.ts:imports"
--8<-- "nodejs/examples/embedding.ts:embedding_function"
--8<-- "nodejs/examples/embedding.test.ts:imports"
--8<-- "nodejs/examples/embedding.test.ts:embedding_function"
```
=== "vectordb (deprecated)"
@@ -150,7 +150,7 @@ need to worry about it when you query the table:
.toArray()
```
=== "vectordb (deprecated)
=== "vectordb (deprecated)"
```ts
const results = await table

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@@ -51,8 +51,8 @@ LanceDB registers the OpenAI embeddings function in the registry as `openai`. Yo
=== "TypeScript"
```typescript
--8<--- "nodejs/examples/embedding.ts:imports"
--8<--- "nodejs/examples/embedding.ts:openai_embeddings"
--8<--- "nodejs/examples/embedding.test.ts:imports"
--8<--- "nodejs/examples/embedding.test.ts:openai_embeddings"
```
=== "Rust"
@@ -121,12 +121,10 @@ class Words(LanceModel):
vector: Vector(func.ndims()) = func.VectorField()
table = db.create_table("words", schema=Words)
table.add(
[
{"text": "hello world"},
{"text": "goodbye world"}
]
)
table.add([
{"text": "hello world"},
{"text": "goodbye world"}
])
query = "greetings"
actual = table.search(query).limit(1).to_pydantic(Words)[0]