Modify the apply endpoint in the REST API to an asynchronous interface

CHANGELOG.md

@@ -79,6 +79,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add sql table length exceed limit and uuid truncation.
 - Add ci workflow to test templates
 - Add deploy flag in model.
+- Modify the apply endpoint in the REST API to an asynchronous interface.
 
 #### Bug Fixes
 

superduper/base/config_settings.py

@@ -18,10 +18,7 @@
 USER_CONFIG: str = (
     str(Path(CONFIG_FILE).expanduser())
     if CONFIG_FILE
-    else (
-        f'{HOME}/.superduper/config.yaml' if HOME
-        else None
-    )
+    else (f'{HOME}/.superduper/config.yaml' if HOME else None)
 )
 PREFIX = 'SUPERDUPER_'
 ROOT = Path(__file__).parents[2]

superduper/rest/build.py

@@ -12,7 +12,7 @@
     import magic
 except ImportError:
     magic = None
-from fastapi import File, Response
+from fastapi import BackgroundTasks, File, Response
 from fastapi.responses import JSONResponse
 
 from superduper import logging
@@ -158,7 +158,10 @@ def test_log():
         os.remove(log_file)
         return {'status': 'ok'}
 
-    def _process_db_apply(db, info):
+    def _process_db_apply(db, component):
+        db.apply(component, force=True)
+
+    def _process_apply_info(db, info):
         if '_variables' in info:
             assert {'_variables', 'identifier'}.issubset(info.keys())
             variables = info.pop('_variables')
@@ -177,25 +180,28 @@ def _process_db_apply(db, info):
                 db=db,
                 **variables,
             )
-            db.apply(component, force=True)
-            return {'status': 'ok'}
+            return component
         component = Document.decode(info, db=db).unpack()
         # TODO this shouldn't be necessary to do twice
         component.unpack()
-        db.apply(component, force=True)
-        return {'status': 'ok'}
+        return component
 
     @app.add('/db/apply', method='post')
-    def db_apply(
-        info: t.Dict, id: str | None = 'test', db: 'Datalayer' = DatalayerDependency()
+    async def db_apply(
+        info: t.Dict,
+        background_tasks: BackgroundTasks,
+        id: str | None = 'test',
+        db: 'Datalayer' = DatalayerDependency(),
     ):
         if id:
             log_file = f"/tmp/{id}.log"
             with redirect_stdout_to_file(log_file):
-                out = _process_db_apply(db, info)
+                component = _process_apply_info(db, info)
+                background_tasks.add_task(_process_db_apply, db, component)
         else:
-            out = _process_db_apply(db, info)
-        return out
+            component = _process_apply_info(db, info)
+            background_tasks.add_task(_process_db_apply, db, component)
+        return {'status': 'ok'}
 
     import subprocess
     import time

templates/llm_finetuning/build.ipynb

@@ -297,15 +297,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from superduper import Template, Table, Schema\n",
+    "from superduper import Template, Table, Schema, Application\n",
     "from superduper.components.dataset import RemoteData\n",
     "\n",
     "llm.trainer.use_lora = \"<var:use_lora>\"\n",
     "llm.trainer.num_train_epochs = \"<var:num_train_epochs>\"\n",
     "\n",
+    "app = Application(identifier=\"llm\", components=[llm])\n",
+    "\n",
     "t = Template(\n",
     "    'llm-finetune',\n",
-    "    template=llm,\n",
+    "    template=app,\n",
     "    substitutions={\n",
     "        TABLE_NAME: 'table_name',\n",
     "        model_name: 'model_name',\n",
@@ -400,7 +402,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

templates/llm_finetuning/component.json

@@ -24,19 +24,19 @@
       "method": "dill",
       "encodable": "artifact"
     },
-    "913bc7838286b1f1fa6f4f90969bd9acf4dc205f": {
+    "a026ecb590e04810d1e0f9d4ef7195e74c3614c3": {
       "_path": "superduper.components.datatype.Artifact",
       "datatype": "?datatype:dill",
       "uri": null,
-      "blob": "&:blob:913bc7838286b1f1fa6f4f90969bd9acf4dc205f"
+      "blob": "&:blob:a026ecb590e04810d1e0f9d4ef7195e74c3614c3"
     },
     "dataset:llm_finetuning": {
       "_path": "superduper.components.dataset.RemoteData",
       "upstream": null,
       "plugins": null,
       "cache": true,
       "status": null,
-      "getter": "?913bc7838286b1f1fa6f4f90969bd9acf4dc205f"
+      "getter": "?a026ecb590e04810d1e0f9d4ef7195e74c3614c3"
     },
     "table:sample_llm_finetuning": {
       "_path": "superduper.components.table.Table",
@@ -236,7 +236,7 @@
             "uri": null,
             "blob": "&:blob:4a8dc14137b3a79a81256a795b266fe82bda52d9"
           },
-          "llm": {
+          "model:llm": {
             "_path": "superduper_transformers.model.LLM",
             "upstream": null,
             "plugins": null,
@@ -253,6 +253,7 @@
             "num_workers": 0,
             "serve": false,
             "trainer": "?trainer:llm-finetune-trainer",
+            "deploy": false,
             "prompt": "{input}",
             "prompt_func": null,
             "max_batch_size": 4,
@@ -261,15 +262,29 @@
             "model_kwargs": "?4a8dc14137b3a79a81256a795b266fe82bda52d9",
             "tokenizer_kwargs": "?4a8dc14137b3a79a81256a795b266fe82bda52d9",
             "prompt_template": "{input}"
+          },
+          "llm": {
+            "_path": "superduper.components.application.Application",
+            "upstream": null,
+            "plugins": null,
+            "cache": true,
+            "status": null,
+            "components": [
+              "?model:llm"
+            ],
+            "namespace": null,
+            "link": null,
+            "_literals": [
+              "template"
+            ]
           }
         }
       },
       "template_variables": [
         "table_name",
         "model_name",
         "use_lora",
-        "num_train_epochs",
-        "output_prefix"
+        "num_train_epochs"
       ],
       "types": {
         "collection": {

templates/simple_rag/build.ipynb

@@ -527,7 +527,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

templates/simple_rag/component.json

@@ -20,19 +20,19 @@
       "method": "dill",
       "encodable": "artifact"
     },
-    "3cf26512c472a982bfddddd931f59a82c03f74e9": {
+    "60ca8cae14f69a82da08cb69135b087ad78d5203": {
       "_path": "superduper.components.datatype.Artifact",
       "datatype": "?datatype:dill",
       "uri": null,
-      "blob": "&:blob:3cf26512c472a982bfddddd931f59a82c03f74e9"
+      "blob": "&:blob:60ca8cae14f69a82da08cb69135b087ad78d5203"
     },
     "dataset:superduper-docs": {
       "_path": "superduper.components.dataset.RemoteData",
       "upstream": null,
       "plugins": null,
       "cache": true,
       "status": null,
-      "getter": "?3cf26512c472a982bfddddd931f59a82c03f74e9"
+      "getter": "?60ca8cae14f69a82da08cb69135b087ad78d5203"
     },
     "table:sample_simple_rag": {
       "_path": "superduper.components.table.Table",
@@ -58,14 +58,14 @@
             "method": "dill",
             "encodable": "artifact"
           },
-          "adc059378de6bb27a2f6b02b8bf0e92f2c97e1e8": {
+          "039aed30ddb0b6d251775302b5055579eec0c7f8": {
             "_path": "superduper.components.datatype.Artifact",
             "datatype": "?datatype:dill",
             "uri": null,
-            "blob": "&:blob:adc059378de6bb27a2f6b02b8bf0e92f2c97e1e8"
+            "blob": "&:blob:039aed30ddb0b6d251775302b5055579eec0c7f8"
           },
           "model:chunker": {
-            "_object": "?adc059378de6bb27a2f6b02b8bf0e92f2c97e1e8",
+            "_object": "?039aed30ddb0b6d251775302b5055579eec0c7f8",
             "upstream": null,
             "plugins": null,
             "cache": true,
@@ -81,12 +81,13 @@
             "num_workers": 0,
             "serve": false,
             "trainer": null,
+            "deploy": false,
             "chunk_size": 200
           },
-          "<var:table_name>-select-id-x": {
+          "var-table-name-select-var-id-field-x": {
             "_path": "superduper_<var:databackend>.query.parse_query",
             "documents": [],
-            "query": "<var:table_name>.select(\"id\", \"x\")"
+            "query": "<var:table_name>.select(\"<var:id_field>\", \"x\")"
           },
           "listener:chunker": {
             "_path": "superduper.components.listener.Listener",
@@ -98,7 +99,7 @@
             "key": "x",
             "model": "?model:chunker",
             "predict_kwargs": {},
-            "select": "?<var:table_name>-select-id-x",
+            "select": "?var-table-name-select-var-id-field-x",
             "flatten": true
           },
           "datatype:sqlvector[1536]": {
@@ -124,6 +125,7 @@
             "num_workers": 0,
             "serve": false,
             "trainer": null,
+            "deploy": false,
             "model": "text-embedding-ada-002",
             "max_batch_size": 8,
             "openai_api_key": null,
@@ -193,6 +195,7 @@
             "num_workers": 0,
             "serve": false,
             "trainer": null,
+            "deploy": false,
             "model": "gpt-4-turbo",
             "max_batch_size": 8,
             "openai_api_key": null,
@@ -218,6 +221,7 @@
             "num_workers": 0,
             "serve": false,
             "trainer": null,
+            "deploy": false,
             "prompt_template": "Use the following context snippets, these snippets are not ordered!, Answer the question based on this context.\nThese snippets are samples from our internal data-repositories, and should be used exclusively and as a matter of priority to answer the question\n\n{context}\n\nHere's the question: {query}",
             "select": "?outputs-chunker-?(listener:chunker.uuid)-select-like-outputs-chunker-?(listener:chunker.uuid)-var-query-vector-index-vectorindex-n-5",
             "key": "<var:output_prefix>chunker__?(listener:chunker.uuid)",