add embeddings

qdrant-landing/content/documentation/embeddings/gpt4all.md

@@ -0,0 +1,52 @@
+
+---
+title: GPT4All
+weight: 1700
+aliases:
+  - /documentation/examples/gpt4all-search/
+  - /documentation/tutorials/gpt4all-search/
+  - /documentation/integrations/gpt4all/ 
+---
+
+# Using GPT4All with Qdrant 
+
+GPT4All offers a range of large language models that can be fine-tuned for various applications. GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
+
+No API calls or GPUs required - you can just download the application and get started. Use GPT4All in Python to program with LLMs implemented with the llama.cpp backend and Nomic's C backend.
+
+## Installation
+
+You can install the required package using the following pip command:
+
+```bash
+pip install gpt4all
+```
+
+Here is how you might connect to GPT4ALL using Qdrant:
+
+```python
+import qdrant_client
+from qdrant_client.models import Batch
+from gpt4all import GPT4All
+
+# Initialize GPT4All model
+model = GPT4All("gpt4all-lora-quantized")
+
+# Generate embeddings for a text
+text = "GPT4All enables open-source AI applications."
+embeddings = model.embed(text)
+
+# Initialize Qdrant client
+qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
+
+# Upsert the embedding into Qdrant
+qdrant_client.upsert(
+    collection_name="OpenSourceAI",
+    points=Batch(
+        ids=[1],
+        vectors=[embeddings],
+    )
+)
+
+```
+