From 6799cee72413736a9209141dfe9ce66162e23581 Mon Sep 17 00:00:00 2001 From: Dennis Hume Date: Tue, 31 Dec 2024 14:21:48 -0600 Subject: [PATCH 1/4] Pinecone tutorial --- docs/docs-beta/docs/tutorial/pinecone.md | 311 ++++++++++++++++++ .../tutorials/pinecone/pinecone_dag.png | Bin 0 -> 111431 bytes .../tutorials/pinecone/pinecone_job.png | Bin 0 -> 82070 bytes examples/tutorial_pinecone/.env.example | 1 + examples/tutorial_pinecone/.gitignore | 9 + examples/tutorial_pinecone/Makefile | 9 + examples/tutorial_pinecone/README.md | 1 + .../_static/architecture-diagram.png | Bin 0 -> 90858 bytes .../_static/dagster-modal-banner.png | Bin 0 -> 259347 bytes .../_static/screenshot_dagster_lineage.png | Bin 0 -> 650714 bytes .../screenshot_dagster_summary_asset.png | Bin 0 -> 616075 bytes .../_static/screenshot_r2_storage.png | Bin 0 -> 256672 bytes examples/tutorial_pinecone/pyproject.toml | 7 + examples/tutorial_pinecone/setup.cfg | 2 + examples/tutorial_pinecone/setup.py | 15 + .../tutorial_pinecone/__init__.py | 1 + .../tutorial_pinecone/assets.py | 171 ++++++++++ .../tutorial_pinecone/definitions.py | 15 + .../tutorial_pinecone/jobs.py | 13 + .../tutorial_pinecone/resources.py | 60 ++++ .../tutorial_pinecone/schedules.py | 8 + .../tutorial_pinecone_tests/__init__.py | 1 + .../tutorial_pinecone_tests/test_assets.py | 1 + 23 files changed, 625 insertions(+) create mode 100644 docs/docs-beta/docs/tutorial/pinecone.md create mode 100644 docs/docs-beta/static/images/tutorials/pinecone/pinecone_dag.png create mode 100644 docs/docs-beta/static/images/tutorials/pinecone/pinecone_job.png create mode 100644 examples/tutorial_pinecone/.env.example create mode 100644 examples/tutorial_pinecone/.gitignore create mode 100644 examples/tutorial_pinecone/Makefile create mode 100644 examples/tutorial_pinecone/README.md create mode 100644 examples/tutorial_pinecone/_static/architecture-diagram.png create mode 100644 examples/tutorial_pinecone/_static/dagster-modal-banner.png create mode 100644 examples/tutorial_pinecone/_static/screenshot_dagster_lineage.png create mode 100644 examples/tutorial_pinecone/_static/screenshot_dagster_summary_asset.png create mode 100644 examples/tutorial_pinecone/_static/screenshot_r2_storage.png create mode 100644 examples/tutorial_pinecone/pyproject.toml create mode 100644 examples/tutorial_pinecone/setup.cfg create mode 100644 examples/tutorial_pinecone/setup.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/__init__.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/assets.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/definitions.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/jobs.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/resources.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone/schedules.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py create mode 100644 examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py diff --git a/docs/docs-beta/docs/tutorial/pinecone.md b/docs/docs-beta/docs/tutorial/pinecone.md new file mode 100644 index 0000000000000..c4df7ad672b0d --- /dev/null +++ b/docs/docs-beta/docs/tutorial/pinecone.md @@ -0,0 +1,311 @@ +--- +title: Pinecone +description: Use Dagster to upload data into Pinecone +last_update: + date: 2024-12-30 + author: Dennis Hume +--- + +## Overview +Many AI applications are data applications. Organizations want to leverage existing LLMs rather than build their own. But in order to take advantage of all the models that exist, you need to supplement them with your own data to produce more accurate and contextually aware results. We will demonstrate how to use Dagster to extract data, generate embeddings, and store the results within a vector database ([Pinecone](https://www.pinecone.io/)) which we can then use to power AI models to craft far more detailed answers. + +### Dagster Concepts +- [resources]() +- [run configurations]() + +### Services +- [DuckDB]() +- [Pinecone]() + +## Code +![Pinecone asset graph](/images/tutorials/pinecone/pinecone_dag.png) + +### Setup +All the code for this tutorial can be found at [project_dagster_pinecone](). + +Install the project dependencies: +``` +pip install -e ".[dev]" +``` + +Run Dagster: +``` +dagster dev +``` + +Open [http://localhost:3000](http://localhost:3000) in your browser. + +### Loading Data +We will be working with review data from Goodreads. These reviews exist as a collection of JSON files categorized by different genres. We will focus on just the files for graphic novels to limit the size of the files we will process. Within this domain, the files we will be working with are `goodreads_books_comics_graphic.json.gz` and `goodreads_reviews_comics_graphic.json.gz`. Since the data is normalized across these two files, we will want to combine information before feeding it into our vector database. + +One way to handle preprocessing of the data is with [DuckDB](https://duckdb.org/). DuckDB is an in-process database, similar to SQLite, optimized for analytical workloads. We will start by creating two Dagster assets to load in the data. Each will load one of the files and create a DuckDB table (`graphic_novels` and `reviews`): +```python +# asssets.py +@dg.asset( + kinds={"duckdb"}, + group_name="ingestion", + deps=[goodreads], +) +def graphic_novels(duckdb_resource: dg_duckdb.DuckDBResource): + url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_comics_graphic.json.gz" + query = f""" + create table if not exists graphic_novels as ( + select * + from read_json( + '{url}', + ignore_errors = true + ) + ); + """ + with duckdb_resource.get_connection() as conn: + conn.execute(query) + +@dg.asset( + kinds={"duckdb"}, + group_name="ingestion", + deps=[goodreads], +) +def reviews(duckdb_resource: dg_duckdb.DuckDBResource): + url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_reviews_comics_graphic.json.gz" + query = f""" + create table if not exists reviews as ( + select * + from read_json( + '{url}', + ignore_errors = true + ) + ); + """ + with duckdb_resource.get_connection() as conn: + conn.execute(query) +``` + +With our DuckDB tables created, we can now query them like any other SQL table. Our third asset will join and filter the data and then return a DataFrame (we will also `LIMIT` the results to 500): +```python +# asssets.py +@dg.asset( + kinds={"duckdb"}, + group_name="ingestion", +) +def enriched_reviews( + duckdb_resource: dg_duckdb.DuckDBResource, + graphic_novels, + reviews, +) -> pd.DataFrame: + query = f""" + select + reviews.review_id as review_id, + graphic_novels.title as title, + reviews.rating as rating, + reviews.review_text as text + from graphic_novels + left join reviews + on graphic_novels.book_id = reviews.book_id + where graphic_novels.language_code = 'eng' + limit 500 + """ + with duckdb_resource.get_connection() as conn: + return conn.execute(query).fetch_df() +``` + +Now that the data has been prepared, we are ready to work with our vector database. + +### Vector Database +We can begin by creating the index within our vector database. A vector database is a specialized database designed to store, manage, and retrieve high-dimensional vector embeddings, enabling efficient similarity search and machine learning tasks. There are many different vector databases available. For this demo we will use Pinecone which is a cloud based vector database that offers a [free tier](https://app.pinecone.io/) that can help us get started. + +:::note +After you have your API key from Pinecone. You will want to set it to the environment variable `PINECONE_API_KEY`: +``` +export PINECONE_API_KEY={YOUR PINECONE API KEY} +``` +::: + +Now that we have a Pinecone account we can incorporate Pinecone into our code. To help make everything consistent, we will create a Pinecone resource similar to the Dagster maintained DuckDB resource we used to ingest the Goodreads data. Creating a custom resource is fairly simple, the only information we need to authenticate with the Pinecone client is their API key: +```python +#resources.py +class PineconeResource(ConfigurableResource): + api_key: str = Field( + description=( + "Pinecone API key. See https://docs.pinecone.io/reference/api/introduction" + ) + ) + + _client: Pinecone = PrivateAttr() + + def setup_for_execution(self, context: InitResourceContext) -> None: + self._client = Pinecone( + api_key=self.api_key, + ) + ... +``` + +For our methods we will want the ability to create an index, retrieve a index so we can upsert records, and the ability to embed inputs. You can see all the details in the repo but this is what the `create_index` method looks like: +```python +#resources.py + def create_index( + self, + index_name: str, + dimension: int, + metric: str, + serverless_spec: ServerlessSpec, + ): + existing_indexes = [ + index_info["name"] for index_info in self._client.list_indexes() + ] + + if index_name not in existing_indexes: + self._client.create_index( + index_name, + dimension=dimension, + metric=metric, + spec=serverless_spec, + ) + while not self._client.describe_index(index_name).status["ready"]: + time.sleep(1) +``` + +With our Pinecone resource complete, we can initialize it using the API key that we set the environment variable for: +```python +# resources.py +pinecone_resource = PineconeResource(api_key=dg.EnvVar("PINECONE_API_KEY")) +``` + +Using our Pinecone resource it is easy enough to create an asset to manage the index where we will upload our embeddings: +```python +# assets.py +@dg.asset( + kinds={"Pinecone"}, + group_name="processing", +) +def pinecone_index( + pinecone_resource: PineconeResource, +): + spec = ServerlessSpec(cloud="aws", region="us-east-1") + pinecone_resource.create_index( + INDEX_NAME, + dimension=1024, + metric="cosine", + serverless_spec=spec, + ) +``` + +### Generate Embeddings +Between our data and the index, we are ready to create and upload our embeddings. One final asset will bring this all together. We will iterate through DataFrame from `enriched_reviews` and generate embeddings for each review. Then we can include additional metadata that will be the final vectors we upload to Pinecone using our Pinecone resource. + +```python +# assets.py +@dg.asset( + kinds={"Pinecone"}, + group_name="processing", +) +def pinecone_embeddings( + pinecone_resource: PineconeResource, + enriched_reviews: pd.DataFrame, + pinecone_index, +): + data = enriched_reviews.to_dict("records") + + vectors = [] + for chunk in _chunks(data, 50): + + embeddings = pinecone_resource.embed( + inputs=[r["text"] for r in chunk], + input_type="passage", + ) + + for d, e in zip(chunk, embeddings): + vectors.append( + { + "id": str(d["review_id"]), + "values": e["values"], + "metadata": { + "source": "goodreads", + "rating": d["rating"], + "title": d["title"], + "text": d["text"], + }, + } + ) + + pinecone_resource.index(INDEX_NAME).upsert( + vectors=vectors, + namespace=NAMESPACE, + ) +``` + +Those 5 assets are all we need to create and populate our index within Pinecone. We can combine these assets together into a job and execute them all together: +```python +# jobs.py +goodreads_pinecone = dg.define_asset_job( + name="goodreads_pinecone", + selection=[ + assets.graphic_novels, + assets.reviews, + assets.enriched_reviews, + assets.pinecone_index, + assets.pinecone_embeddings, + ], +) +``` + +![Pinecone job](/images/tutorials/pinecone/pinecone_job.png) + +In the Dagster UI go to **Job** and select **goodreads_pinecone** and click the **Materialize all** button. If this is the first time running the job, it may take a minute or so to process the data. + +### Building AI Assets +Populating our vector database is only one part of our workflow. We can also build assets that query that index. We will look at one final asset that is separate from the `goodreads_pinecone` job. This asset is called `graphic_novel_search` which allows us to run queries and get the most similar results based on the reviews. We might want to ask many different questions using this asset so we will include a run configuration so we can execute many different questions: +```python +#assets.py +class GoodreadsQuestion(dg.Config): + question: str + top_k: int + +@dg.asset( + kinds={"Pinecone"}, + group_name="retrieval", +) +def graphic_novel_search( + context: dg.AssetExecutionContext, + config: GoodreadsQuestion, + pinecone_resource: PineconeResource, + pinecone_embeddings, +): + query_embedding = pinecone_resource.embed( + inputs=[config.question], + input_type="query", + ) + index = pinecone_resource.index(INDEX_NAME) + results = index.query( + namespace=NAMESPACE, + vector=query_embedding[0].values, + top_k=config.top_k, + include_values=False, + include_metadata=True, + ) + for result in results["matches"]: + context.log.info(result) +``` + +Again we are using the Pinecone resource, though this time we are querying rather than uploading results. Within the Dagster UI we can execute this asset by itself. Since this asset has a run configuration, we will have to set that before we can materialize a run. Feel free to experiment but we will ask it to find a scary graphic novel: +``` +ops: + graphic_novel_search: + config: + question: A scary graphic novel + top_k: 1 +``` + +After materializing the asset you can view the logs to see the output of what was the best match: +``` +{'id': '37b6d106-2223-f7b9-c9b7-c4bfc4867dc1', + 'metadata': {'rating': 4.0, + 'source': 'goodreads', + 'text': 'Dark and disturbing, yet many of the panels are ' + 'gorgeously illustrated. A promising start!', + 'title': 'Wytches, Volume 1'}, + 'score': 0.852409363, + 'values': []} +``` + +# Going Forward +This was a relatively simple example but you see the amount of coordination that is needed to power realworld AI applications. 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duckdb_resource.get_connection() as conn: + return conn.execute(query).fetch_df() + + +@dg.asset( + kinds={"Pinecone"}, + group_name="processing", +) +def pinecone_index( + pinecone_resource: PineconeResource, +): + spec = ServerlessSpec(cloud="aws", region="us-east-1") + pinecone_resource.create_index( + INDEX_NAME, + dimension=1024, + metric="cosine", + serverless_spec=spec, + ) + + +@dg.asset( + kinds={"Pinecone"}, + group_name="processing", +) +def pinecone_embeddings( + pinecone_resource: PineconeResource, + enriched_reviews: pd.DataFrame, + pinecone_index, +): + data = enriched_reviews.to_dict("records") + + vectors = [] + for chunk in _chunks(data, 50): + + embeddings = pinecone_resource.embed( + inputs=[r["text"] for r in chunk], + input_type="passage", + ) + + for d, e in zip(chunk, embeddings): + vectors.append( + { + "id": str(d["review_id"]), + "values": e["values"], + "metadata": { + "source": "goodreads", + "rating": d["rating"], + "title": d["title"], + "text": d["text"], + }, + } + ) + + pinecone_resource.index(INDEX_NAME).upsert( + vectors=vectors, + namespace=NAMESPACE, + ) + + +class GoodreadsQuestion(dg.Config): + question: str + top_k: int + + +@dg.asset( + kinds={"Pinecone"}, + group_name="retrieval", +) +def graphic_novel_search( + context: dg.AssetExecutionContext, + config: GoodreadsQuestion, + pinecone_resource: PineconeResource, + pinecone_embeddings, +): + query_embedding = pinecone_resource.embed( + inputs=[config.question], + input_type="query", + ) + index = pinecone_resource.index(INDEX_NAME) + results = index.query( + namespace=NAMESPACE, + vector=query_embedding[0].values, + top_k=config.top_k, + include_values=False, + include_metadata=True, + ) + for result in results["matches"]: + context.log.info(result) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/definitions.py b/examples/tutorial_pinecone/tutorial_pinecone/definitions.py new file mode 100644 index 0000000000000..6d0e138d89b21 --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone/definitions.py @@ -0,0 +1,15 @@ +import dagster as dg +from . import assets, resources, schedules, jobs + +all_assets = dg.load_assets_from_modules([assets]) +external_assets = [assets.goodreads] + +defs = dg.Definitions( + assets=all_assets + external_assets, + resources={ + "duckdb_resource": resources.duckdb_resouce, + "pinecone_resource": resources.pinecone_resource, + }, + jobs=[jobs.goodreads_pinecone], + schedules=[schedules.goodreads_pinecone_schedule], +) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/jobs.py b/examples/tutorial_pinecone/tutorial_pinecone/jobs.py new file mode 100644 index 0000000000000..bccaf4258a0b2 --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone/jobs.py @@ -0,0 +1,13 @@ +import dagster as dg +from . import assets + +goodreads_pinecone = dg.define_asset_job( + name="goodreads_pinecone", + selection=[ + assets.graphic_novels, + assets.reviews, + assets.enriched_reviews, + assets.pinecone_index, + assets.pinecone_embeddings, + ], +) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/resources.py b/examples/tutorial_pinecone/tutorial_pinecone/resources.py new file mode 100644 index 0000000000000..14c535628e840 --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone/resources.py @@ -0,0 +1,60 @@ +import dagster as dg +from pinecone import Pinecone, ServerlessSpec +from pinecone.core.openapi.control.model.embeddings_list import EmbeddingsList +from pydantic import Field, PrivateAttr +import dagster_duckdb as dg_duckdb +import time + + +class PineconeResource(dg.ConfigurableResource): + api_key: str = Field( + description=( + "Pinecone API key. See https://docs.pinecone.io/reference/api/introduction" + ) + ) + + _client: Pinecone = PrivateAttr() + + def setup_for_execution(self, context: dg.InitResourceContext) -> None: + self._client = Pinecone( + api_key=self.api_key, + ) + + def create_index( + self, + index_name: str, + dimension: int, + metric: str, + serverless_spec: ServerlessSpec, + ): + existing_indexes = [ + index_info["name"] for index_info in self._client.list_indexes() + ] + + if index_name not in existing_indexes: + self._client.create_index( + index_name, + dimension=dimension, + metric=metric, + spec=serverless_spec, + ) + while not self._client.describe_index(index_name).status["ready"]: + time.sleep(1) + + def index(self, index_name: str) -> Pinecone.Index: + return self._client.Index(index_name) + + def embed( + self, + inputs: list[str], + input_type: str, + ) -> EmbeddingsList: + return self._client.inference.embed( + model="multilingual-e5-large", + inputs=inputs, + parameters={"input_type": input_type}, + ) + + +duckdb_resouce = dg_duckdb.DuckDBResource(database="data/data.duckdb") +pinecone_resource = PineconeResource(api_key=dg.EnvVar("PINECONE_API_KEY")) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/schedules.py b/examples/tutorial_pinecone/tutorial_pinecone/schedules.py new file mode 100644 index 0000000000000..0a3df7fce9d49 --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone/schedules.py @@ -0,0 +1,8 @@ +import dagster as dg +from . import jobs + +goodreads_pinecone_schedule = dg.ScheduleDefinition( + name="pinecone_schedule", + target=jobs.goodreads_pinecone, + cron_schedule="0 12 * * *", +) diff --git a/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py b/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py new file mode 100644 index 0000000000000..8b137891791fe --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py @@ -0,0 +1 @@ + diff --git a/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py b/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py new file mode 100644 index 0000000000000..8b137891791fe --- /dev/null +++ b/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py @@ -0,0 +1 @@ + From 2274dc0867f63e586d37d025ebc5eb0166636f13 Mon Sep 17 00:00:00 2001 From: Dennis Hume Date: Tue, 31 Dec 2024 14:22:43 -0600 Subject: [PATCH 2/4] Remove static dir --- .../_static/architecture-diagram.png | Bin 90858 -> 0 bytes .../_static/dagster-modal-banner.png | Bin 259347 -> 0 bytes .../_static/screenshot_dagster_lineage.png | Bin 650714 -> 0 bytes .../screenshot_dagster_summary_asset.png | Bin 616075 -> 0 bytes .../_static/screenshot_r2_storage.png | Bin 256672 -> 0 bytes 5 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/tutorial_pinecone/_static/architecture-diagram.png delete mode 100644 examples/tutorial_pinecone/_static/dagster-modal-banner.png delete mode 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Date: Thu, 2 Jan 2025 15:10:38 -0500 Subject: [PATCH 3/4] [docs-beta] support path prefix for tutorial snippets --- docs/docs-beta/docs/tutorial/pinecone.md | 61 +++++-------------- docs/docs-beta/src/components/CodeExample.tsx | 10 +-- 2 files changed, 21 insertions(+), 50 deletions(-) diff --git a/docs/docs-beta/docs/tutorial/pinecone.md b/docs/docs-beta/docs/tutorial/pinecone.md index c4df7ad672b0d..a3c96e5df5ca9 100644 --- a/docs/docs-beta/docs/tutorial/pinecone.md +++ b/docs/docs-beta/docs/tutorial/pinecone.md @@ -10,18 +10,22 @@ last_update: Many AI applications are data applications. Organizations want to leverage existing LLMs rather than build their own. But in order to take advantage of all the models that exist, you need to supplement them with your own data to produce more accurate and contextually aware results. We will demonstrate how to use Dagster to extract data, generate embeddings, and store the results within a vector database ([Pinecone](https://www.pinecone.io/)) which we can then use to power AI models to craft far more detailed answers. ### Dagster Concepts -- [resources]() -- [run configurations]() + +- [resources](/todo) +- [run configurations](/todo) ### Services -- [DuckDB]() -- [Pinecone]() + +- [DuckDB](/todo) +- [Pinecone](/todo) ## Code + ![Pinecone asset graph](/images/tutorials/pinecone/pinecone_dag.png) ### Setup -All the code for this tutorial can be found at [project_dagster_pinecone](). + +All the code for this tutorial can be found at [project_dagster_pinecone](/todo). Install the project dependencies: ``` @@ -39,46 +43,13 @@ Open [http://localhost:3000](http://localhost:3000) in your browser. We will be working with review data from Goodreads. These reviews exist as a collection of JSON files categorized by different genres. We will focus on just the files for graphic novels to limit the size of the files we will process. Within this domain, the files we will be working with are `goodreads_books_comics_graphic.json.gz` and `goodreads_reviews_comics_graphic.json.gz`. Since the data is normalized across these two files, we will want to combine information before feeding it into our vector database. One way to handle preprocessing of the data is with [DuckDB](https://duckdb.org/). DuckDB is an in-process database, similar to SQLite, optimized for analytical workloads. We will start by creating two Dagster assets to load in the data. Each will load one of the files and create a DuckDB table (`graphic_novels` and `reviews`): -```python -# asssets.py -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", - deps=[goodreads], -) -def graphic_novels(duckdb_resource: dg_duckdb.DuckDBResource): - url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_comics_graphic.json.gz" - query = f""" - create table if not exists graphic_novels as ( - select * - from read_json( - '{url}', - ignore_errors = true - ) - ); - """ - with duckdb_resource.get_connection() as conn: - conn.execute(query) -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", - deps=[goodreads], -) -def reviews(duckdb_resource: dg_duckdb.DuckDBResource): - url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_reviews_comics_graphic.json.gz" - query = f""" - create table if not exists reviews as ( - select * - from read_json( - '{url}', - ignore_errors = true - ) - ); - """ - with duckdb_resource.get_connection() as conn: - conn.execute(query) -``` + With our DuckDB tables created, we can now query them like any other SQL table. Our third asset will join and filter the data and then return a DataFrame (we will also `LIMIT` the results to 500): ```python @@ -308,4 +279,4 @@ After materializing the asset you can view the logs to see the output of what wa ``` # Going Forward -This was a relatively simple example but you see the amount of coordination that is needed to power realworld AI applications. As you add more and more sources of unstructured data that updates on different cadences and powers multiple downstream tasks you will want to think of operational details for building around AI. \ No newline at end of file +This was a relatively simple example but you see the amount of coordination that is needed to power realworld AI applications. As you add more and more sources of unstructured data that updates on different cadences and powers multiple downstream tasks you will want to think of operational details for building around AI. diff --git a/docs/docs-beta/src/components/CodeExample.tsx b/docs/docs-beta/src/components/CodeExample.tsx index a0dfc2af1e769..1ac75e331960e 100644 --- a/docs/docs-beta/src/components/CodeExample.tsx +++ b/docs/docs-beta/src/components/CodeExample.tsx @@ -7,8 +7,10 @@ interface CodeExampleProps { title?: string; lineStart?: number; lineEnd?: number; + pathPrefix?: string; } + /** * Removes content below the `if __name__` block for the given `lines`. */ @@ -28,20 +30,18 @@ function filterNoqaComments(lines: string[]): string[] { const CodeExample: React.FC = ({ filePath, - language, title, lineStart, lineEnd, + language = 'python', + pathPrefix = 'docs_beta_snippets/docs_beta_snippets', ...props }) => { const [content, setContent] = React.useState(''); const [error, setError] = React.useState(null); - language = language || 'python'; - React.useEffect(() => { - // Adjust the import path to start from the docs directory - import(`!!raw-loader!/../../examples/docs_beta_snippets/docs_beta_snippets/${filePath}`) + import(`!!raw-loader!/../../examples/${pathPrefix}/${filePath}`) .then((module) => { var lines = module.default.split('\n'); From 727706822b3cd175417a933a535c8e63e78d1463 Mon Sep 17 00:00:00 2001 From: Colton Padden Date: Fri, 3 Jan 2025 09:38:16 -0500 Subject: [PATCH 4/4] rm tutorial_pinecone --- docs/docs-beta/docs/tutorial/pinecone.md | 282 ------------------ .../tutorials/pinecone/pinecone_dag.png | Bin 111431 -> 0 bytes .../tutorials/pinecone/pinecone_job.png | Bin 82070 -> 0 bytes examples/tutorial_pinecone/.env.example | 1 - examples/tutorial_pinecone/.gitignore | 9 - examples/tutorial_pinecone/Makefile | 9 - examples/tutorial_pinecone/README.md | 1 - examples/tutorial_pinecone/pyproject.toml | 7 - examples/tutorial_pinecone/setup.cfg | 2 - examples/tutorial_pinecone/setup.py | 15 - .../tutorial_pinecone/__init__.py | 1 - .../tutorial_pinecone/assets.py | 171 ----------- .../tutorial_pinecone/definitions.py | 15 - .../tutorial_pinecone/jobs.py | 13 - .../tutorial_pinecone/resources.py | 60 ---- .../tutorial_pinecone/schedules.py | 8 - .../tutorial_pinecone_tests/__init__.py | 1 - .../tutorial_pinecone_tests/test_assets.py | 1 - 18 files changed, 596 deletions(-) delete mode 100644 docs/docs-beta/docs/tutorial/pinecone.md delete mode 100644 docs/docs-beta/static/images/tutorials/pinecone/pinecone_dag.png delete mode 100644 docs/docs-beta/static/images/tutorials/pinecone/pinecone_job.png delete mode 100644 examples/tutorial_pinecone/.env.example delete mode 100644 examples/tutorial_pinecone/.gitignore delete mode 100644 examples/tutorial_pinecone/Makefile delete mode 100644 examples/tutorial_pinecone/README.md delete mode 100644 examples/tutorial_pinecone/pyproject.toml delete mode 100644 examples/tutorial_pinecone/setup.cfg delete mode 100644 examples/tutorial_pinecone/setup.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/__init__.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/assets.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/definitions.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/jobs.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/resources.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone/schedules.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py delete mode 100644 examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py diff --git a/docs/docs-beta/docs/tutorial/pinecone.md b/docs/docs-beta/docs/tutorial/pinecone.md deleted file mode 100644 index a3c96e5df5ca9..0000000000000 --- a/docs/docs-beta/docs/tutorial/pinecone.md +++ /dev/null @@ -1,282 +0,0 @@ ---- -title: Pinecone -description: Use Dagster to upload data into Pinecone -last_update: - date: 2024-12-30 - author: Dennis Hume ---- - -## Overview -Many AI applications are data applications. Organizations want to leverage existing LLMs rather than build their own. But in order to take advantage of all the models that exist, you need to supplement them with your own data to produce more accurate and contextually aware results. We will demonstrate how to use Dagster to extract data, generate embeddings, and store the results within a vector database ([Pinecone](https://www.pinecone.io/)) which we can then use to power AI models to craft far more detailed answers. - -### Dagster Concepts - -- [resources](/todo) -- [run configurations](/todo) - -### Services - -- [DuckDB](/todo) -- [Pinecone](/todo) - -## Code - -![Pinecone asset graph](/images/tutorials/pinecone/pinecone_dag.png) - -### Setup - -All the code for this tutorial can be found at [project_dagster_pinecone](/todo). - -Install the project dependencies: -``` -pip install -e ".[dev]" -``` - -Run Dagster: -``` -dagster dev -``` - -Open [http://localhost:3000](http://localhost:3000) in your browser. - -### Loading Data -We will be working with review data from Goodreads. These reviews exist as a collection of JSON files categorized by different genres. We will focus on just the files for graphic novels to limit the size of the files we will process. Within this domain, the files we will be working with are `goodreads_books_comics_graphic.json.gz` and `goodreads_reviews_comics_graphic.json.gz`. Since the data is normalized across these two files, we will want to combine information before feeding it into our vector database. - -One way to handle preprocessing of the data is with [DuckDB](https://duckdb.org/). DuckDB is an in-process database, similar to SQLite, optimized for analytical workloads. We will start by creating two Dagster assets to load in the data. Each will load one of the files and create a DuckDB table (`graphic_novels` and `reviews`): - - - -With our DuckDB tables created, we can now query them like any other SQL table. Our third asset will join and filter the data and then return a DataFrame (we will also `LIMIT` the results to 500): -```python -# asssets.py -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", -) -def enriched_reviews( - duckdb_resource: dg_duckdb.DuckDBResource, - graphic_novels, - reviews, -) -> pd.DataFrame: - query = f""" - select - reviews.review_id as review_id, - graphic_novels.title as title, - reviews.rating as rating, - reviews.review_text as text - from graphic_novels - left join reviews - on graphic_novels.book_id = reviews.book_id - where graphic_novels.language_code = 'eng' - limit 500 - """ - with duckdb_resource.get_connection() as conn: - return conn.execute(query).fetch_df() -``` - -Now that the data has been prepared, we are ready to work with our vector database. - -### Vector Database -We can begin by creating the index within our vector database. A vector database is a specialized database designed to store, manage, and retrieve high-dimensional vector embeddings, enabling efficient similarity search and machine learning tasks. There are many different vector databases available. For this demo we will use Pinecone which is a cloud based vector database that offers a [free tier](https://app.pinecone.io/) that can help us get started. - -:::note -After you have your API key from Pinecone. You will want to set it to the environment variable `PINECONE_API_KEY`: -``` -export PINECONE_API_KEY={YOUR PINECONE API KEY} -``` -::: - -Now that we have a Pinecone account we can incorporate Pinecone into our code. To help make everything consistent, we will create a Pinecone resource similar to the Dagster maintained DuckDB resource we used to ingest the Goodreads data. Creating a custom resource is fairly simple, the only information we need to authenticate with the Pinecone client is their API key: -```python -#resources.py -class PineconeResource(ConfigurableResource): - api_key: str = Field( - description=( - "Pinecone API key. See https://docs.pinecone.io/reference/api/introduction" - ) - ) - - _client: Pinecone = PrivateAttr() - - def setup_for_execution(self, context: InitResourceContext) -> None: - self._client = Pinecone( - api_key=self.api_key, - ) - ... -``` - -For our methods we will want the ability to create an index, retrieve a index so we can upsert records, and the ability to embed inputs. You can see all the details in the repo but this is what the `create_index` method looks like: -```python -#resources.py - def create_index( - self, - index_name: str, - dimension: int, - metric: str, - serverless_spec: ServerlessSpec, - ): - existing_indexes = [ - index_info["name"] for index_info in self._client.list_indexes() - ] - - if index_name not in existing_indexes: - self._client.create_index( - index_name, - dimension=dimension, - metric=metric, - spec=serverless_spec, - ) - while not self._client.describe_index(index_name).status["ready"]: - time.sleep(1) -``` - -With our Pinecone resource complete, we can initialize it using the API key that we set the environment variable for: -```python -# resources.py -pinecone_resource = PineconeResource(api_key=dg.EnvVar("PINECONE_API_KEY")) -``` - -Using our Pinecone resource it is easy enough to create an asset to manage the index where we will upload our embeddings: -```python -# assets.py -@dg.asset( - kinds={"Pinecone"}, - group_name="processing", -) -def pinecone_index( - pinecone_resource: PineconeResource, -): - spec = ServerlessSpec(cloud="aws", region="us-east-1") - pinecone_resource.create_index( - INDEX_NAME, - dimension=1024, - metric="cosine", - serverless_spec=spec, - ) -``` - -### Generate Embeddings -Between our data and the index, we are ready to create and upload our embeddings. One final asset will bring this all together. We will iterate through DataFrame from `enriched_reviews` and generate embeddings for each review. Then we can include additional metadata that will be the final vectors we upload to Pinecone using our Pinecone resource. - -```python -# assets.py -@dg.asset( - kinds={"Pinecone"}, - group_name="processing", -) -def pinecone_embeddings( - pinecone_resource: PineconeResource, - enriched_reviews: pd.DataFrame, - pinecone_index, -): - data = enriched_reviews.to_dict("records") - - vectors = [] - for chunk in _chunks(data, 50): - - embeddings = pinecone_resource.embed( - inputs=[r["text"] for r in chunk], - input_type="passage", - ) - - for d, e in zip(chunk, embeddings): - vectors.append( - { - "id": str(d["review_id"]), - "values": e["values"], - "metadata": { - "source": "goodreads", - "rating": d["rating"], - "title": d["title"], - "text": d["text"], - }, - } - ) - - pinecone_resource.index(INDEX_NAME).upsert( - vectors=vectors, - namespace=NAMESPACE, - ) -``` - -Those 5 assets are all we need to create and populate our index within Pinecone. We can combine these assets together into a job and execute them all together: -```python -# jobs.py -goodreads_pinecone = dg.define_asset_job( - name="goodreads_pinecone", - selection=[ - assets.graphic_novels, - assets.reviews, - assets.enriched_reviews, - assets.pinecone_index, - assets.pinecone_embeddings, - ], -) -``` - -![Pinecone job](/images/tutorials/pinecone/pinecone_job.png) - -In the Dagster UI go to **Job** and select **goodreads_pinecone** and click the **Materialize all** button. If this is the first time running the job, it may take a minute or so to process the data. - -### Building AI Assets -Populating our vector database is only one part of our workflow. We can also build assets that query that index. We will look at one final asset that is separate from the `goodreads_pinecone` job. This asset is called `graphic_novel_search` which allows us to run queries and get the most similar results based on the reviews. We might want to ask many different questions using this asset so we will include a run configuration so we can execute many different questions: -```python -#assets.py -class GoodreadsQuestion(dg.Config): - question: str - top_k: int - -@dg.asset( - kinds={"Pinecone"}, - group_name="retrieval", -) -def graphic_novel_search( - context: dg.AssetExecutionContext, - config: GoodreadsQuestion, - pinecone_resource: PineconeResource, - pinecone_embeddings, -): - query_embedding = pinecone_resource.embed( - inputs=[config.question], - input_type="query", - ) - index = pinecone_resource.index(INDEX_NAME) - results = index.query( - namespace=NAMESPACE, - vector=query_embedding[0].values, - top_k=config.top_k, - include_values=False, - include_metadata=True, - ) - for result in results["matches"]: - context.log.info(result) -``` - -Again we are using the Pinecone resource, though this time we are querying rather than uploading results. Within the Dagster UI we can execute this asset by itself. Since this asset has a run configuration, we will have to set that before we can materialize a run. Feel free to experiment but we will ask it to find a scary graphic novel: -``` -ops: - graphic_novel_search: - config: - question: A scary graphic novel - top_k: 1 -``` - -After materializing the asset you can view the logs to see the output of what was the best match: -``` -{'id': '37b6d106-2223-f7b9-c9b7-c4bfc4867dc1', - 'metadata': {'rating': 4.0, - 'source': 'goodreads', - 'text': 'Dark and disturbing, yet many of the panels are ' - 'gorgeously illustrated. A promising start!', - 'title': 'Wytches, Volume 1'}, - 'score': 0.852409363, - 'values': []} -``` - -# Going Forward -This was a relatively simple example but you see the amount of coordination that is needed to power realworld AI applications. 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zojbGD>Vo{{%ks^$Zv5N+Ei-!$_uq@HGFq}<`Pp=2UQA8}u5kV(<@)&Yo@4yHm(Lu` zdf+4*b7Env>}$RSnTrkJH~PX79w>QTc*j&azwz2TgDdi|{0wjZfHJOV0z+>dtN=mQ z*xLB5cO zP8~?)Z=M(oV^^g9&Ioq-&_wpP6)y<~&OpouVz4R)f_qgGUpB8G58F@YkA7|-X-rz0hsoM80=W!mt#Ulw$mF=<59=)CSH{^yro9?7-&82^% zxU(gJr3k2|EC^w&uJ>ESlU={**zN%K@S+EIw0Gt<6|-&Kv*G;Ho7-Q^JGsvn*eFmC zIKL3+-_(U?l(8kp3+tFl_aC~sPT`6)wshR+1x&13rZb?O4^$-!ynqdVErB}71@UNc zyC4WC>BajDTMlk6ls(C>hjT2}_J92CriI$DZ$$&sL)n-opR{p00i_ I>zopr03PBRUH||9 diff --git a/examples/tutorial_pinecone/.env.example b/examples/tutorial_pinecone/.env.example deleted file mode 100644 index 630b73dc683ba..0000000000000 --- a/examples/tutorial_pinecone/.env.example +++ /dev/null @@ -1 +0,0 @@ -PINECONE_API_KEY= diff --git a/examples/tutorial_pinecone/.gitignore b/examples/tutorial_pinecone/.gitignore deleted file mode 100644 index f7fa1b1260d7b..0000000000000 --- a/examples/tutorial_pinecone/.gitignore +++ /dev/null @@ -1,9 +0,0 @@ -*.egg-info/* -*.mp3 -*.pyc -.DS_Store -.direnv/ -.env -.envrc -__pycache__/ -tmp*/ diff --git a/examples/tutorial_pinecone/Makefile b/examples/tutorial_pinecone/Makefile deleted file mode 100644 index 3a3026a2678c2..0000000000000 --- a/examples/tutorial_pinecone/Makefile +++ /dev/null @@ -1,9 +0,0 @@ -ruff: - ruff check --select I --fix . - -clean: - find . -name "__pycache__" -exec rm -rf {} + - find . -name "*.pyc" -exec rm -f {} + - -install: - uv pip install -e .[dev] diff --git a/examples/tutorial_pinecone/README.md b/examples/tutorial_pinecone/README.md deleted file mode 100644 index a957029b531d8..0000000000000 --- a/examples/tutorial_pinecone/README.md +++ /dev/null @@ -1 +0,0 @@ -## Dagster × Pinecone Demo diff --git a/examples/tutorial_pinecone/pyproject.toml b/examples/tutorial_pinecone/pyproject.toml deleted file mode 100644 index 07872284f5355..0000000000000 --- a/examples/tutorial_pinecone/pyproject.toml +++ /dev/null @@ -1,7 +0,0 @@ -[build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" - -[tool.dagster] -module_name = "tutorial_pinecone.definitions" -code_location_name = "tutorial_pinecone" diff --git a/examples/tutorial_pinecone/setup.cfg b/examples/tutorial_pinecone/setup.cfg deleted file mode 100644 index e3954805dba3a..0000000000000 --- a/examples/tutorial_pinecone/setup.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[metadata] -name = tutorial_pinecone diff --git a/examples/tutorial_pinecone/setup.py b/examples/tutorial_pinecone/setup.py deleted file mode 100644 index 8e62254864835..0000000000000 --- a/examples/tutorial_pinecone/setup.py +++ /dev/null @@ -1,15 +0,0 @@ -from setuptools import find_packages, setup - -setup( - name="tutorial_pinecone", - packages=find_packages(exclude=["tutorial_pinecone_tests"]), - install_requires=[ - "dagster", - "dagster-duckdb", - "dagster-duckdb-pandas", - "pinecone-client", - "pydantic", - "pandas", - ], - extras_require={"dev": ["dagster-webserver", "pytest", "ruff"]}, -) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/__init__.py b/examples/tutorial_pinecone/tutorial_pinecone/__init__.py deleted file mode 100644 index 8b137891791fe..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/examples/tutorial_pinecone/tutorial_pinecone/assets.py b/examples/tutorial_pinecone/tutorial_pinecone/assets.py deleted file mode 100644 index 9700c6f6d8852..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/assets.py +++ /dev/null @@ -1,171 +0,0 @@ -import dagster as dg -import pandas as pd -import dagster_duckdb as dg_duckdb -from .resources import PineconeResource -from pinecone import ServerlessSpec - -INDEX_NAME = "goodreads" -NAMESPACE = "ns1" - - -def _chunks(lst: list, n: int): - for i in range(0, len(lst), n): - yield lst[i:i + n] - - -goodreads = dg.AssetSpec( - "Goodreads", - description="Goodreads Data. https://mengtingwan.github.io/data/goodreads#datasets", - group_name="raw_data", -) - - -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", - deps=[goodreads], -) -def graphic_novels(duckdb_resource: dg_duckdb.DuckDBResource): - url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_books_comics_graphic.json.gz" - query = f""" - create table if not exists graphic_novels as ( - select * - from read_json( - '{url}', - ignore_errors = true - ) - ); - """ - with duckdb_resource.get_connection() as conn: - conn.execute(query) - - -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", - deps=[goodreads], -) -def reviews(duckdb_resource: dg_duckdb.DuckDBResource): - url = "https://datarepo.eng.ucsd.edu/mcauley_group/gdrive/goodreads/byGenre/goodreads_reviews_comics_graphic.json.gz" - query = f""" - create table if not exists reviews as ( - select * - from read_json( - '{url}', - ignore_errors = true - ) - ); - """ - with duckdb_resource.get_connection() as conn: - conn.execute(query) - - -@dg.asset( - kinds={"duckdb"}, - group_name="ingestion", -) -def enriched_reviews( - duckdb_resource: dg_duckdb.DuckDBResource, - graphic_novels, - reviews, -) -> pd.DataFrame: - query = f""" - select - reviews.review_id as review_id, - graphic_novels.title as title, - reviews.rating as rating, - reviews.review_text as text - from graphic_novels - left join reviews - on graphic_novels.book_id = reviews.book_id - where graphic_novels.language_code = 'eng' - limit 500 - """ - with duckdb_resource.get_connection() as conn: - return conn.execute(query).fetch_df() - - -@dg.asset( - kinds={"Pinecone"}, - group_name="processing", -) -def pinecone_index( - pinecone_resource: PineconeResource, -): - spec = ServerlessSpec(cloud="aws", region="us-east-1") - pinecone_resource.create_index( - INDEX_NAME, - dimension=1024, - metric="cosine", - serverless_spec=spec, - ) - - -@dg.asset( - kinds={"Pinecone"}, - group_name="processing", -) -def pinecone_embeddings( - pinecone_resource: PineconeResource, - enriched_reviews: pd.DataFrame, - pinecone_index, -): - data = enriched_reviews.to_dict("records") - - vectors = [] - for chunk in _chunks(data, 50): - - embeddings = pinecone_resource.embed( - inputs=[r["text"] for r in chunk], - input_type="passage", - ) - - for d, e in zip(chunk, embeddings): - vectors.append( - { - "id": str(d["review_id"]), - "values": e["values"], - "metadata": { - "source": "goodreads", - "rating": d["rating"], - "title": d["title"], - "text": d["text"], - }, - } - ) - - pinecone_resource.index(INDEX_NAME).upsert( - vectors=vectors, - namespace=NAMESPACE, - ) - - -class GoodreadsQuestion(dg.Config): - question: str - top_k: int - - -@dg.asset( - kinds={"Pinecone"}, - group_name="retrieval", -) -def graphic_novel_search( - context: dg.AssetExecutionContext, - config: GoodreadsQuestion, - pinecone_resource: PineconeResource, - pinecone_embeddings, -): - query_embedding = pinecone_resource.embed( - inputs=[config.question], - input_type="query", - ) - index = pinecone_resource.index(INDEX_NAME) - results = index.query( - namespace=NAMESPACE, - vector=query_embedding[0].values, - top_k=config.top_k, - include_values=False, - include_metadata=True, - ) - for result in results["matches"]: - context.log.info(result) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/definitions.py b/examples/tutorial_pinecone/tutorial_pinecone/definitions.py deleted file mode 100644 index 6d0e138d89b21..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/definitions.py +++ /dev/null @@ -1,15 +0,0 @@ -import dagster as dg -from . import assets, resources, schedules, jobs - -all_assets = dg.load_assets_from_modules([assets]) -external_assets = [assets.goodreads] - -defs = dg.Definitions( - assets=all_assets + external_assets, - resources={ - "duckdb_resource": resources.duckdb_resouce, - "pinecone_resource": resources.pinecone_resource, - }, - jobs=[jobs.goodreads_pinecone], - schedules=[schedules.goodreads_pinecone_schedule], -) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/jobs.py b/examples/tutorial_pinecone/tutorial_pinecone/jobs.py deleted file mode 100644 index bccaf4258a0b2..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/jobs.py +++ /dev/null @@ -1,13 +0,0 @@ -import dagster as dg -from . import assets - -goodreads_pinecone = dg.define_asset_job( - name="goodreads_pinecone", - selection=[ - assets.graphic_novels, - assets.reviews, - assets.enriched_reviews, - assets.pinecone_index, - assets.pinecone_embeddings, - ], -) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/resources.py b/examples/tutorial_pinecone/tutorial_pinecone/resources.py deleted file mode 100644 index 14c535628e840..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/resources.py +++ /dev/null @@ -1,60 +0,0 @@ -import dagster as dg -from pinecone import Pinecone, ServerlessSpec -from pinecone.core.openapi.control.model.embeddings_list import EmbeddingsList -from pydantic import Field, PrivateAttr -import dagster_duckdb as dg_duckdb -import time - - -class PineconeResource(dg.ConfigurableResource): - api_key: str = Field( - description=( - "Pinecone API key. See https://docs.pinecone.io/reference/api/introduction" - ) - ) - - _client: Pinecone = PrivateAttr() - - def setup_for_execution(self, context: dg.InitResourceContext) -> None: - self._client = Pinecone( - api_key=self.api_key, - ) - - def create_index( - self, - index_name: str, - dimension: int, - metric: str, - serverless_spec: ServerlessSpec, - ): - existing_indexes = [ - index_info["name"] for index_info in self._client.list_indexes() - ] - - if index_name not in existing_indexes: - self._client.create_index( - index_name, - dimension=dimension, - metric=metric, - spec=serverless_spec, - ) - while not self._client.describe_index(index_name).status["ready"]: - time.sleep(1) - - def index(self, index_name: str) -> Pinecone.Index: - return self._client.Index(index_name) - - def embed( - self, - inputs: list[str], - input_type: str, - ) -> EmbeddingsList: - return self._client.inference.embed( - model="multilingual-e5-large", - inputs=inputs, - parameters={"input_type": input_type}, - ) - - -duckdb_resouce = dg_duckdb.DuckDBResource(database="data/data.duckdb") -pinecone_resource = PineconeResource(api_key=dg.EnvVar("PINECONE_API_KEY")) diff --git a/examples/tutorial_pinecone/tutorial_pinecone/schedules.py b/examples/tutorial_pinecone/tutorial_pinecone/schedules.py deleted file mode 100644 index 0a3df7fce9d49..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone/schedules.py +++ /dev/null @@ -1,8 +0,0 @@ -import dagster as dg -from . import jobs - -goodreads_pinecone_schedule = dg.ScheduleDefinition( - name="pinecone_schedule", - target=jobs.goodreads_pinecone, - cron_schedule="0 12 * * *", -) diff --git a/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py b/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py deleted file mode 100644 index 8b137891791fe..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone_tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py b/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py deleted file mode 100644 index 8b137891791fe..0000000000000 --- a/examples/tutorial_pinecone/tutorial_pinecone_tests/test_assets.py +++ /dev/null @@ -1 +0,0 @@ -

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+.envrc +__pycache__/ +tmp*/ diff --git a/examples/tutorial_pinecone/Makefile b/examples/tutorial_pinecone/Makefile new file mode 100644 index 0000000000000..3a3026a2678c2 --- /dev/null +++ b/examples/tutorial_pinecone/Makefile @@ -0,0 +1,9 @@ +ruff: + ruff check --select I --fix . + +clean: + find . -name "__pycache__" -exec rm -rf {} + + find . -name "*.pyc" -exec rm -f {} + + +install: + uv pip install -e .[dev] diff --git a/examples/tutorial_pinecone/README.md b/examples/tutorial_pinecone/README.md new file mode 100644 index 0000000000000..a957029b531d8 --- /dev/null +++ b/examples/tutorial_pinecone/README.md @@ -0,0 +1 @@ +## Dagster × Pinecone Demo diff --git a/examples/tutorial_pinecone/_static/architecture-diagram.png b/examples/tutorial_pinecone/_static/architecture-diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..b6a0c9dad3c33e50f66fa2625102c01d8b6f0835 GIT binary patch literal 90858 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