Premai integration (#924)

* Added premai content in the index md file * Added prem qdrant integration quide markdown with js and py code snippets * removed api key * remove full code section and changed jsx to typescript * small typo fix * lowered the verbosity * removed aliases * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * Update qdrant-landing/content/documentation/embeddings/premai.md Co-authored-by: Anush <anushshetty90@gmail.com> * chore: minor fix after doc review and changes * docs: Alphabetical _index.md * docs: Proposed refactor --------- Co-authored-by: Anush <anushshetty90@gmail.com>
2026-09-29 07:58:31 +02:00 · 2024-05-31 15:56:30 +05:30
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@@ -14,5 +14,6 @@ weight: 34
 | [Nomic](./nomic/)             |
 | [Nvidia](./nvidia/)           |
 | [OpenAI](./openai/)           |
+| [Prem AI](./premai/)          |
 | [Snowflake](./snowflake/)     |
 | [Voyage AI](./voyage/)        |
@@ -0,0 +1,216 @@
+---
+title: Prem AI
+weight: 1600
+---
+
+# Prem AI
+
+[PremAI](https://premai.io/) is a unified generative AI development platform for fine-tuning deploying, and monitoring AI models.
+
+Qdrant is compatible with PremAI APIs.
+
+### Installing the SDKs
+
+```bash
+pip install premai qdrant-client
+```
+
+To install the npm package:
+
+```bash
+npm install @premai/prem-sdk @qdrant/js-client-rest
+```
+
+### Import all required packages
+
+```python
+from premai import Prem
+
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams
+```
+
+```typescript
+import Prem from '@premai/prem-sdk';
+import { QdrantClient } from '@qdrant/js-client-rest';
+```
+
+### Define all the constants
+
+We need to define the project ID and the embedding model to use. You can learn more about obtaining these in the PremAI [docs](https://docs.premai.io/quick-start).
+
+
+```python
+PROJECT_ID = 123
+EMBEDDING_MODEL = "text-embedding-3-large"
+COLLECTION_NAME = "prem-collection-py"
+QDRANT_SERVER_URL = "http://localhost:6333"
+DOCUMENTS = [
+    "This is a sample python document",
+    "We will be using qdrant and premai python sdk"
+]
+```
+
+```typescript
+const PROJECT_ID = 123;
+const EMBEDDING_MODEL = "text-embedding-3-large";
+const COLLECTION_NAME = "prem-collection-js";
+const SERVER_URL = "http://localhost:6333"
+const DOCUMENTS = [
+    "This is a sample javascript document",
+    "We will be using qdrant and premai javascript sdk"
+];
+```
+
+### Set up PremAI and Qdrant clients
+
+
+```python
+prem_client = Prem(api_key="xxxx-xxx-xxx")
+qdrant_client = QdrantClient(url=QDRANT_SERVER_URL)
+```
+
+```typescript
+const premaiClient = new Prem({
+    apiKey: "xxxx-xxx-xxx"
+})
+const qdrantClient = new QdrantClient({ url: SERVER_URL });
+```
+
+### Generating Embeddings
+
+```python
+from typing import Union, List
+
+def get_embeddings(
+    project_id: int, 
+    embedding_model: str, 
+    documents: Union[str, List[str]]
+) -> List[List[float]]:
+    """
+    Helper function to get the embeddings from premai sdk 
+    Args
+        project_id (int): The project id from prem saas platform.
+        embedding_model (str): The embedding model alias to choose
+        documents (Union[str, List[str]]): Single texts or list of texts to embed
+    Returns:
+        List[List[int]]: A list of list of integers that represents different
+            embeddings
+    """
+    embeddings = []
+    documents = [documents] if isinstance(documents, str) else documents 
+    for embedding in prem_client.embeddings.create(
+        project_id=project_id,
+        model=embedding_model, 
+        input=documents
+    ).data:
+        embeddings.append(embedding.embedding)
+    
+    return embeddings
+```
+
+```typescript
+async function getEmbeddings(projectID, embeddingModel, documents) {
+    const response = await premaiClient.embeddings.create({
+        project_id: projectID,
+        model: embeddingModel,
+        input: documents
+    });
+    return response;
+}
+```
+
+### Converting Embeddings to Qdrant Points
+
+
+```python
+from qdrant_client.models import PointStruct
+
+embeddings = get_embeddings(
+    project_id=PROJECT_ID,
+    embedding_model=EMBEDDING_MODEL, 
+    documents=DOCUMENTS 
+)
+
+points = [
+    PointStruct(
+        id=idx, 
+        vector=embedding,
+        payload={"text": text},
+    ) for idx, (embedding, text) in enumerate(zip(embeddings, DOCUMENTS))
+]
+```
+
+```typescript
+function convertToQdrantPoints(embeddings, texts) {
+    return embeddings.data.map((data, i) => {
+        return {
+            id: i,
+            vector: data.embedding,
+            payload: {
+                text: texts[i]
+            }
+        };
+    });
+}
+
+const embeddings = await getEmbeddings(PROJECT_ID, EMBEDDING_MODEL, DOCUMENTS);
+const points = convertToQdrantPoints(embeddings, DOCUMENTS);
+```
+
+### Set up a Qdrant Collection
+
+```python
+qdrant_client.create_collection(
+    collection_name=COLLECTION_NAME, 
+    vectors_config=VectorParams(size=3072, distance=Distance.DOT)
+)
+```
+```typescript
+await qdrantClient.createCollection(COLLECTION_NAME, {
+    vectors: {
+        size: 3072,
+        distance: 'Cosine'
+    }
+})
+```
+
+### Insert Documents into the Collection
+
+```python
+doc_ids = list(range(len(embeddings)))
+
+qdrant_client.upsert(
+    collection_name=COLLECTION_NAME, 
+    points=points
+ )
+```
+
+```typescript
+await qdrantClient.upsert(COLLECTION_NAME, {
+        wait: true,
+        points
+    });
+```
+
+### Perform a Search
+
+```python
+query = "what is the extension of python document"
+
+query_embedding = get_embeddings(
+    project_id=PROJECT_ID, 
+    embedding_model=EMBEDDING_MODEL, 
+    documents=query
+)
+
+qdrant_client.search(collection_name=COLLECTION_NAME, query_vector=query_embedding[0])
+```
+```typescript
+const query = "what is the extension of javascript document"
+const query_embedding_response = await getEmbeddings(PROJECT_ID, EMBEDDING_MODEL, query)
+
+await qdrantClient.search(COLLECTION_NAME, {
+    vector: query_embedding_response.data[0].embedding
+});
+```