Add docs file for customizing embedding mode (letta-ai#554)

* minor fix

* forgot to add embedding file

docs/embedding_endpoints.md

@@ -0,0 +1,68 @@
+MemGPT uses embedding models for retrieval search over archival memory. You can use embeddings provided by OpenAI, Azure, or any model on Hugging Face. 
+
+## OpenAI
+To use OpenAI, make sure your `OPENAI_API_KEY` enviornment variable is set.
+```sh
+export OPENAI_API_KEY=YOUR_API_KEY # on Linux/Mac
+```
+Then, configure MemGPT and select `openai` as the embedding provider: 
+```
+> memgpt configure                                                                                    
+...
+? Select embedding provider: openai 
+...
+```
+
+## Azure
+To use Azure, set enviornment variables for Azure and an additional variable specifying your embedding deployment:
+```sh
+# see https://github.com/openai/openai-python#microsoft-azure-endpoints
+export AZURE_OPENAI_KEY = ...
+export AZURE_OPENAI_ENDPOINT = ...
+export AZURE_OPENAI_VERSION = ...
+
+# set the below if you are using deployment ids
+export AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT = ...
+```
+Then, configure MemGPT and select `azure` as the embedding provider: 
+```
+> memgpt configure                                                                                    
+...
+? Select embedding provider: azure 
+...
+```
+
+## Custom Endpoint
+MemGPT supports running embeddings with any Hugging Face model using the [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)(TEI) library. To get started, first make sure you follow TEI's [instructions](https://github.com/huggingface/text-embeddings-inference#get-started) for getting started. Once you have a running endpoint, you can configure MemGPT to use your endpoint: 
+```
+> memgpt configure                                                                                    
+...
+? Select embedding provider: hugging-face
+? Enter default endpoint: http://localhost:8080
+? Enter HuggingFace model tag (e.g. BAAI/bge-large-en-v1.5): BAAI/bge-large-en-v1.5
+? Enter embedding model dimentions (e.g. 1024): 1536
+...
+```
+
+## Local Embeddings 
+
+MemGPT can compute embeddings locally using a lightweight embedding model [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5). 
+!!! warning "Local LLM Performance"
+
+    The `BAAI/bge-small-en-v1.5` was chose to be lightweight, so you may notice degraded performance with embedding-based retrieval when using this option. 
+
+
+
+To compute embeddings locally, install dependencies with: 
+```
+pip install `pymemgpt[local]`
+```
+Then, select the `local` option during configuration: 
+```
+> memgpt configure                                                                                    
+...
+? Select embedding provider: local
+...
+```
+
+