Add Pinecone VectorStore

 - Based on the official pinecone java library.
   Later expects that indices are created externally via Ops.
 - Map Document metadata to and from Pinecone's internal Struct.
   Later converts the metadata into pinecone json format.
 - Add integration tests and README.

pom.xml

@@ -28,6 +28,8 @@
 		<module>document-readers/pdf-reader</module>
 		<module>document-readers/tika-reader</module>
 		<module>embedding-clients/transformers-embedding</module>
+		<module>vector-stores/spring-ai-pinecone</module>
+
 	</modules>
 
 	<organization>
@@ -82,6 +84,8 @@
 		<pgvector.version>0.1.3</pgvector.version>
 		<postgresql.version>42.6.0</postgresql.version>
 		<milvus.version>2.3.0</milvus.version>
+		<pinecone.version>0.6.0</pinecone.version>
+		<protobuf-java-util.version>3.24.4</protobuf-java-util.version>
 
 		<!-- testing dependecies -->
 		<testcontainers.version>1.19.0</testcontainers.version>

vector-stores/spring-ai-pinecone/README.md

@@ -0,0 +1,113 @@
+# Pinecone VectorStore
+
+This readme will walk you through setting up the Pinecone VectorStore to store document embeddings and perform similarity searches.
+
+## What is Pinecone?
+
+[Pinecone](https://www.pinecone.io/) is a popular cloud-based vector database, which allows you to store and search vectors efficiently.
+
+## Prerequisites
+
+1. Pinecone Account: Before you start, ensure you sign up for a [Pinecone account](https://app.pinecone.io/).
+2. Pinecone Project: Once registered, create a new project, an index, and generate an API key. You'll need these details for configuration.
+3. OpenAI Account: Create an account at [OpenAI Signup](https://platform.openai.com/signup) and generate the token at [API Keys](https://platform.openai.com/account/api-keys)
+
+## Configuraiton
+
+To set up PineconeVectorStore, gather the following details from your Pinecone account:
+
+* Pinecond API Key
+* Pinecone Environment
+* Pinecone Project ID
+* Pinecone Index Name
+* Pinecone Namespace
+
+> **Note**
+>  This information is available to you in the Pinecone UI portal.
+
+
+When setting up embeddings, select a vector dimension of 1526. This matches the dimensionality of OpenAI's model "text-embedding-ada-002", which we'll be using for this guide.
+
+Additionally, you'll need to provide your OpenAI API Key. Set it as an environment variable like so:
+
+```bash
+export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key'
+```
+
+## Dependencies
+
+Add these dependencies to your project:
+
+1. OpenAI: Required for calculating embeddings.
+
+```xml
+    <dependency>
+        <groupId>org.springframework.experimental.ai</groupId>
+        <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+        <version>0.7.0-SNAPSHOT</version>
+    </dependency>
+```
+
+2. Pinecone 
+
+```xml
+    <dependency>
+        <groupId>org.springframework.experimental.ai</groupId>
+        <artifactId>spring-ai-pinecone</artifactId>
+        <version>0.7.0-SNAPSHOT</version>
+    </dependency>
+```
+
+## Sample Code
+
+To configure Pinecone in your application, you can use the following setup:
+
+```java
+@Bean
+public PineconeVectorStoreConfig pineconeVectorStoreConfig() {
+
+        return PineconeVectorStoreConfig.builder()
+        .withApiKey(System.getenv( <PINECONE_API_KEY> ))
+        .withEnvironment(gcp-starter)
+        .withProjectId(89309e6)
+        .withIndexName(spring-ai-test-index)
+        .withNamespace("") // Leave it empty as for free tier as later doesn't support namespaces.
+        .build();
+}
+```
+
+Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI starter to your project.
+This provides you with an implementation of the Embeddings client:
+
+```java
+@Bean
+public VectorStore vectorStore(PineconeVectorStoreConfig config, EmbeddingClient embeddingClient) {
+    return new PineconeVectorStore(config, embeddingClient);
+}
+```
+
+In your main code, create some documents
+
+```java
+	List<Document> documents = List.of(
+			new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!",
+					Collections.singletonMap("meta1", "meta1")),
+			new Document("Hello World Hello World Hello World Hello World Hello World Hello World Hello World"),
+			new Document(
+					"Great Depression Great Depression Great Depression Great Depression Great Depression Great Depression",
+					Collections.singletonMap("meta2", "meta2")));
+```
+
+Add the documents to your vector store:
+
+```java
+vectorStore.add(List.of(document));
+```
+
+And finally, retrieve documents similar to a query:
+
+```java
+List<Document> results = vectorStore.similaritySearch("Spring", 5);
+```
+
+If all goes well, you should retrieve the document containing the text "Spring AI rocks!!".

vector-stores/spring-ai-pinecone/pom.xml

@@ -0,0 +1,70 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <parent>
+        <groupId>org.springframework.experimental.ai</groupId>
+        <artifactId>spring-ai</artifactId>
+        <version>0.7.0-SNAPSHOT</version>
+        <relativePath>../../pom.xml</relativePath>
+    </parent>
+    <artifactId>spring-ai-pinecone</artifactId>
+    <packaging>jar</packaging>
+    <name>spring-ai-pinecone</name>
+    <description>spring-ai-pinecone</description>
+    <url>https://github.com/spring-projects-experimental/spring-ai</url>
+
+    <scm>
+        <url>https://github.com/spring-projects-experimental/spring-ai</url>
+        <connection>git://github.com/spring-projects-experimental/spring-ai.git</connection>
+        <developerConnection>[email protected]:spring-projects-experimental/spring-ai.git</developerConnection>
+    </scm>
+
+    <properties>
+        <maven.compiler.target>17</maven.compiler.target>
+        <maven.compiler.source>17</maven.compiler.source>
+    </properties>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.springframework.experimental.ai</groupId>
+            <artifactId>spring-ai-core</artifactId>
+            <version>${project.parent.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>io.pinecone</groupId>
+            <artifactId>pinecone-client</artifactId>
+            <version>${pinecone.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.google.protobuf</groupId>
+            <artifactId>protobuf-java-util</artifactId>
+            <version>${protobuf-java-util.version}</version>
+        </dependency>
+
+        <!-- TESTING -->
+        <dependency>
+            <groupId>org.springframework.experimental.ai</groupId>
+            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+            <version>${parent.version}</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-test</artifactId>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.awaitility</groupId>
+            <artifactId>awaitility</artifactId>
+            <version>3.0.0</version>
+            <scope>test</scope>
+        </dependency>
+
+    </dependencies>
+
+</project>