diff --git a/helpers/types.ts b/helpers/types.ts
index 5e4a9f6efc942c2fafafd150bba4cd8283619a3f..19253cc152f7aaa286e3b9554b1adf0518bbb473 100644
--- a/helpers/types.ts
+++ b/helpers/types.ts
@@ -4,7 +4,7 @@ export type TemplateType = "simple" | "streaming" | "community" | "llamapack";
export type TemplateFramework = "nextjs" | "express" | "fastapi";
export type TemplateEngine = "simple" | "context";
export type TemplateUI = "html" | "shadcn";
-export type TemplateVectorDB = "none" | "mongo" | "pg";
+export type TemplateVectorDB = "none" | "mongo" | "pg" | "pinecone";
export type TemplatePostInstallAction = "none" | "dependencies" | "runApp";
export type TemplateDataSource = {
type: TemplateDataSourceType;
diff --git a/questions.ts b/questions.ts
index 62fdb7eca0225a97b342d6f07d42a6ab1015b1d8..b0cca28cf09f31036a18e46a68f18f13f9bb830f 100644
--- a/questions.ts
+++ b/questions.ts
@@ -89,6 +89,7 @@ const getVectorDbChoices = (framework: TemplateFramework) => {
},
{ title: "MongoDB", value: "mongo" },
{ title: "PostgreSQL", value: "pg" },
+ { title: "Pinecone", value: "pinecone" },
];
const vectordbLang = framework === "fastapi" ? "python" : "typescript";
diff --git a/templates/components/vectordbs/python/pinecone/__init__.py b/templates/components/vectordbs/python/pinecone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/templates/components/vectordbs/python/pinecone/constants.py b/templates/components/vectordbs/python/pinecone/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dd46619b2a2532e46e30a1abde8efcf94cfda06
--- /dev/null
+++ b/templates/components/vectordbs/python/pinecone/constants.py
@@ -0,0 +1,3 @@
+DATA_DIR = "data" # directory containing the documents to index
+CHUNK_SIZE = 512
+CHUNK_OVERLAP = 20
diff --git a/templates/components/vectordbs/python/pinecone/context.py b/templates/components/vectordbs/python/pinecone/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceb8a50ae0cd02425aeb5fa4436df374590a4fad
--- /dev/null
+++ b/templates/components/vectordbs/python/pinecone/context.py
@@ -0,0 +1,14 @@
+from llama_index import ServiceContext
+
+from app.context import create_base_context
+from app.engine.constants import CHUNK_SIZE, CHUNK_OVERLAP
+
+
+def create_service_context():
+ base = create_base_context()
+ return ServiceContext.from_defaults(
+ llm=base.llm,
+ embed_model=base.embed_model,
+ chunk_size=CHUNK_SIZE,
+ chunk_overlap=CHUNK_OVERLAP,
+ )
diff --git a/templates/components/vectordbs/python/pinecone/generate.py b/templates/components/vectordbs/python/pinecone/generate.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c0e1c0b42a88f23723bf8f54cdde1eb2f8b0ad4
--- /dev/null
+++ b/templates/components/vectordbs/python/pinecone/generate.py
@@ -0,0 +1,45 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+import os
+import logging
+from llama_index.vector_stores import PineconeVectorStore
+
+from app.engine.constants import DATA_DIR
+from app.engine.context import create_service_context
+from app.engine.loader import get_documents
+
+
+from llama_index import (
+ SimpleDirectoryReader,
+ VectorStoreIndex,
+ StorageContext,
+)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+
+def generate_datasource(service_context):
+ logger.info("Creating new index")
+ # load the documents and create the index
+ documents = get_documents()
+ store = PineconeVectorStore(
+ api_key=os.environ["PINECONE_API_KEY"],
+ index_name=os.environ["PINECONE_INDEX_NAME"],
+ environment=os.environ["PINECONE_ENVIRONMENT"],
+ )
+ storage_context = StorageContext.from_defaults(vector_store=store)
+ VectorStoreIndex.from_documents(
+ documents,
+ service_context=service_context,
+ storage_context=storage_context,
+ show_progress=True, # this will show you a progress bar as the embeddings are created
+ )
+ logger.info(
+ f"Successfully created embeddings and save to your Pinecone index {os.environ['PINECONE_INDEX_NAME']}"
+ )
+
+
+if __name__ == "__main__":
+ generate_datasource(create_service_context())
diff --git a/templates/components/vectordbs/python/pinecone/index.py b/templates/components/vectordbs/python/pinecone/index.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e9b88102f97fe510e2772a8a92ce5e8111772e3
--- /dev/null
+++ b/templates/components/vectordbs/python/pinecone/index.py
@@ -0,0 +1,23 @@
+import logging
+import os
+
+from llama_index import (
+ VectorStoreIndex,
+)
+from llama_index.vector_stores import PineconeVectorStore
+
+from app.engine.context import create_service_context
+
+
+def get_index():
+ service_context = create_service_context()
+ logger = logging.getLogger("uvicorn")
+ logger.info("Connecting to index from Pinecone...")
+ store = PineconeVectorStore(
+ api_key=os.environ["PINECONE_API_KEY"],
+ index_name=os.environ["PINECONE_INDEX_NAME"],
+ environment=os.environ["PINECONE_ENVIRONMENT"],
+ )
+ index = VectorStoreIndex.from_vector_store(store, service_context)
+ logger.info("Finished connecting to index from Pinecone.")
+ return index
diff --git a/templates/components/vectordbs/typescript/pinecone/generate.mjs b/templates/components/vectordbs/typescript/pinecone/generate.mjs
new file mode 100644
index 0000000000000000000000000000000000000000..b371a639a8e56c5186ee45b274539cff81c04973
--- /dev/null
+++ b/templates/components/vectordbs/typescript/pinecone/generate.mjs
@@ -0,0 +1,35 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
+import {
+ PineconeVectorStore,
+ SimpleDirectoryReader,
+ VectorStoreIndex,
+ storageContextFromDefaults,
+} from "llamaindex";
+import { STORAGE_DIR, checkRequiredEnvVars } from "./shared.mjs";
+
+dotenv.config();
+
+async function loadAndIndex() {
+ // load objects from storage and convert them into LlamaIndex Document objects
+ const documents = await new SimpleDirectoryReader().loadData({
+ directoryPath: STORAGE_DIR,
+ });
+
+ // create vector store
+ const vectorStore = new PineconeVectorStore();
+
+ // create index from all the Documentss and store them in Pinecone
+ console.log("Start creating embeddings...");
+ const storageContext = await storageContextFromDefaults({ vectorStore });
+ await VectorStoreIndex.fromDocuments(documents, { storageContext });
+ console.log(
+ "Successfully created embeddings and save to your Pinecone index.",
+ );
+}
+
+(async () => {
+ checkRequiredEnvVars();
+ await loadAndIndex();
+ console.log("Finished generating storage.");
+})();
diff --git a/templates/components/vectordbs/typescript/pinecone/index.ts b/templates/components/vectordbs/typescript/pinecone/index.ts
new file mode 100644
index 0000000000000000000000000000000000000000..be18486c4e73c598474c502856d39808bbe98fa8
--- /dev/null
+++ b/templates/components/vectordbs/typescript/pinecone/index.ts
@@ -0,0 +1,29 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import {
+ ContextChatEngine,
+ LLM,
+ PineconeVectorStore,
+ VectorStoreIndex,
+ serviceContextFromDefaults,
+} from "llamaindex";
+import { CHUNK_OVERLAP, CHUNK_SIZE, checkRequiredEnvVars } from "./shared.mjs";
+
+async function getDataSource(llm: LLM) {
+ checkRequiredEnvVars();
+ const serviceContext = serviceContextFromDefaults({
+ llm,
+ chunkSize: CHUNK_SIZE,
+ chunkOverlap: CHUNK_OVERLAP,
+ });
+ const store = new PineconeVectorStore();
+ return await VectorStoreIndex.fromVectorStore(store, serviceContext);
+}
+
+export async function createChatEngine(llm: LLM) {
+ const index = await getDataSource(llm);
+ const retriever = index.asRetriever({ similarityTopK: 5 });
+ return new ContextChatEngine({
+ chatModel: llm,
+ retriever,
+ });
+}
diff --git a/templates/components/vectordbs/typescript/pinecone/shared.mjs b/templates/components/vectordbs/typescript/pinecone/shared.mjs
new file mode 100644
index 0000000000000000000000000000000000000000..f9140261c36f2c98e569fea5847c245a6d151ca9
--- /dev/null
+++ b/templates/components/vectordbs/typescript/pinecone/shared.mjs
@@ -0,0 +1,22 @@
+export const STORAGE_DIR = "./data";
+export const CHUNK_SIZE = 512;
+export const CHUNK_OVERLAP = 20;
+
+const REQUIRED_ENV_VARS = ["PINECONE_ENVIRONMENT", "PINECONE_API_KEY"];
+
+export function checkRequiredEnvVars() {
+ const missingEnvVars = REQUIRED_ENV_VARS.filter((envVar) => {
+ return !process.env[envVar];
+ });
+
+ if (missingEnvVars.length > 0) {
+ console.log(
+ `The following environment variables are required but missing: ${missingEnvVars.join(
+ ", ",
+ )}`,
+ );
+ throw new Error(
+ `Missing environment variables: ${missingEnvVars.join(", ")}`,
+ );
+ }
+}