support s3 as native source

Cargo.toml

@@ -109,3 +109,5 @@ rand = "0.9.0"
 indoc = "2.0.6"
 owo-colors = "4.2.0"
 json5 = "0.4.1"
+aws-config = "1.6.2"
+aws-sdk-s3 = "1.85.0"

examples/amazon_s3_text_embedding/.env.example

@@ -0,0 +1,6 @@
+# Postgres database address for cocoindex
+COCOINDEX_DATABASE_URL=postgres://cocoindex:cocoindex@localhost/cocoindex
+
+# Amazon S3 Configuration
+AMAZON_S3_BUCKET_NAME=your-bucket-name
+AMAZON_S3_PREFIX=optional/prefix/path

examples/amazon_s3_text_embedding/.gitignore

@@ -0,0 +1 @@
+.env

examples/amazon_s3_text_embedding/README.md

@@ -0,0 +1,104 @@
+This example builds an embedding index based on files stored in an Amazon S3 bucket.
+It continuously updates the index as files are added / updated / deleted in the source bucket:
+it keeps the index in sync with the Amazon S3 bucket effortlessly.
+
+## Prerequisite
+
+Before running the example, you need to:
+
+1.  [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
+
+2.  Prepare for Amazon S3:
+
+    -   **Create an Amazon S3 bucket:**
+        - Go to the [AWS S3 Console](https://s3.console.aws.amazon.com/s3/home) and click **Create bucket**. Give it a unique name and choose a region.
+        - Or, use the AWS CLI:
+          ```sh
+          aws s3 mb s3://your-s3-bucket-name
+          ```
+
+    -   **Upload your files to the bucket:**
+        - In the AWS Console, click your bucket, then click **Upload** and add your `.md`, `.txt`, `.docx`, or other files.
+        - Or, use the AWS CLI:
+          ```sh
+          aws s3 cp localfile.txt s3://your-s3-bucket-name/
+          aws s3 cp your-folder/ s3://your-s3-bucket-name/ --recursive
+          ```
+
+    -   **Set up AWS credentials:**
+        - The easiest way is to run:
+          ```sh
+          aws configure
+          ```
+          Enter your AWS Access Key ID, Secret Access Key, region (e.g., `us-east-1`), and output format (`json`).
+        - This creates a credentials file at `~/.aws/credentials` and config at `~/.aws/config`.
+        - Alternatively, you can set environment variables:
+          ```sh
+          export AWS_ACCESS_KEY_ID=your-access-key-id
+          export AWS_SECRET_ACCESS_KEY=your-secret-access-key
+          export AWS_DEFAULT_REGION=us-east-1
+          ```
+        - If running on AWS EC2 or Lambda, you can use an [IAM role](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) with S3 read permissions.
+
+    -   **(Optional) Specify a prefix** to restrict to a subfolder in the bucket by setting `AMAZON_S3_PREFIX` in your `.env`.
+
+    See [AWS S3 documentation](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) for more details.
+
+3.  Create a `.env` file with your Amazon S3 bucket name and (optionally) prefix.
+    Start from copying the `.env.example`, and then edit it to fill in your bucket name and prefix.
+
+    ```bash
+    cp .env.example .env
+    $EDITOR .env
+    ```
+
+    Example `.env` file:
+    ```
+    # Database Configuration
+    DATABASE_URL=postgresql://localhost:5432/cocoindex
+
+    # Amazon S3 Configuration
+    AMAZON_S3_BUCKET_NAME=your-bucket-name
+    AMAZON_S3_PREFIX=optional/prefix/path
+    ```
+
+## Run
+
+Install dependencies:
+
+```sh
+uv pip install -r requirements.txt
+```
+
+Setup:
+
+```sh
+uv run main.py cocoindex setup
+```
+
+Run:
+
+```sh
+uv run main.py
+```
+
+During running, it will keep observing changes in the Amazon S3 bucket and update the index automatically.
+At the same time, it accepts queries from the terminal, and performs search on top of the up-to-date index.
+
+
+## CocoInsight 
+CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: [Watch on YouTube](https://youtu.be/ZnmyoHslBSc?si=pPLXWALztkA710r9).
+
+Run CocoInsight to understand your RAG data pipeline:
+
+```sh
+uv run main.py cocoindex server -ci
+```
+
+You can also add a `-L` flag to make the server keep updating the index to reflect source changes at the same time:
+
+```sh
+uv run main.py cocoindex server -ci -L
+```
+
+Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).

examples/amazon_s3_text_embedding/main.py

@@ -0,0 +1,78 @@
+from dotenv import load_dotenv
+
+import asyncio
+import cocoindex
+import datetime
+import os
+
+@cocoindex.flow_def(name="AmazonS3TextEmbedding")
+def amazon_s3_text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
+    """
+    Define an example flow that embeds text from Amazon S3 into a vector database.
+    """
+    bucket_name = os.environ["AMAZON_S3_BUCKET_NAME"]
+    prefix = os.environ.get("AMAZON_S3_PREFIX", None)
+
+    data_scope["documents"] = flow_builder.add_source(
+        cocoindex.sources.AmazonS3(
+            bucket_name=bucket_name,
+            prefix=prefix,
+            included_patterns=["*.md", "*.txt", "*.docx"],
+            binary=False),
+        refresh_interval=datetime.timedelta(minutes=1))
+
+    doc_embeddings = data_scope.add_collector()
+
+    with data_scope["documents"].row() as doc:
+        doc["chunks"] = doc["content"].transform(
+            cocoindex.functions.SplitRecursively(),
+            language="markdown", chunk_size=2000, chunk_overlap=500)
+
+        with doc["chunks"].row() as chunk:
+            chunk["embedding"] = chunk["text"].transform(
+                cocoindex.functions.SentenceTransformerEmbed(
+                 model="sentence-transformers/all-MiniLM-L6-v2")) 
+            doc_embeddings.collect(filename=doc["filename"], location=chunk["location"],
+                                   text=chunk["text"], embedding=chunk["embedding"])
+
+    doc_embeddings.export(
+        "doc_embeddings",
+        cocoindex.storages.Postgres(),
+        primary_key_fields=["filename", "location"],
+        vector_indexes=[
+            cocoindex.VectorIndexDef(
+                field_name="embedding",
+                metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
+
+query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
+    name="SemanticsSearch",
+    flow=amazon_s3_text_embedding_flow,
+    target_name="doc_embeddings",
+    query_transform_flow=lambda text: text.transform(
+        cocoindex.functions.SentenceTransformerEmbed(
+            model="sentence-transformers/all-MiniLM-L6-v2")),
+    default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)
+
+@cocoindex.main_fn()
+def _run():
+    # Use a `FlowLiveUpdater` to keep the flow data updated.
+    with cocoindex.FlowLiveUpdater(amazon_s3_text_embedding_flow):
+        # Run queries in a loop to demonstrate the query capabilities.
+        while True:
+            try:
+                query = input("Enter search query (or Enter to quit): ")
+                if query == '':
+                    break
+                results, _ = query_handler.search(query, 10)
+                print("\nSearch results:")
+                for result in results:
+                    print(f"[{result.score:.3f}] {result.data['filename']}")
+                    print(f"    {result.data['text']}")
+                    print("---")
+                print()
+            except KeyboardInterrupt:
+                break
+
+if __name__ == "__main__":
+    load_dotenv(override=True)
+    _run()

examples/amazon_s3_text_embedding/requirements.txt

@@ -0,0 +1,3 @@
+cocoindex
+python-dotenv
+boto3 

python/cocoindex/sources.py

@@ -28,3 +28,15 @@ class GoogleDrive(op.SourceSpec):
     root_folder_ids: list[str]
     binary: bool = False
     recent_changes_poll_interval: datetime.timedelta | None = None
+
+
+class AmazonS3(op.SourceSpec):
+    """Import data from an Amazon S3 bucket. Supports optional prefix and file filtering by glob patterns."""
+
+    _op_category = op.OpCategory.SOURCE
+
+    bucket_name: str
+    prefix: str | None = None
+    binary: bool = False
+    included_patterns: list[str] | None = None
+    excluded_patterns: list[str] | None = None

src/ops/registration.rs

@@ -8,6 +8,7 @@ use std::sync::{Arc, LazyLock, RwLock, RwLockReadGuard};
 fn register_executor_factories(registry: &mut ExecutorFactoryRegistry) -> Result<()> {
     sources::local_file::Factory.register(registry)?;
     sources::google_drive::Factory.register(registry)?;
+    sources::amazon_s3::Factory.register(registry)?;
 
     functions::parse_json::Factory.register(registry)?;
     functions::split_recursively::Factory.register(registry)?;