docs(spacy-embeddings): added docs for spacy embeddings.

docs/docs.trychroma.com/pages/integrations/_sidenav.js

@@ -11,6 +11,7 @@ export const items = [
         { href: '/integrations/jinaai', children: 'JinaAI' },
         { href: '/integrations/roboflow', children: 'Roboflow' },
         { href: '/integrations/ollama', children: 'Ollama Embeddings' },
+        { href: '/integrations/spacy', children: 'Spacy Embeddings' }
       ]
     },
     {

docs/docs.trychroma.com/pages/integrations/index.md

@@ -24,6 +24,7 @@ Chroma provides lightweight wrappers around popular embedding providers, making
 | [Jina AI](/integrations/jinaai) | ✅  | ✅ |
 | [Roboflow](/integrations/roboflow) | ✅  | ➖ |
 | [Ollama Embeddings](/integrations/ollama) | ✅  | ✅ |
+| [Spacy Embeddings](/integrations/spacy) | ✅  | ➖ | 
 
 
 ***

docs/docs.trychroma.com/pages/integrations/spacy.md

@@ -0,0 +1,22 @@
+---
+---
+
+# spaCy
+
+Pre-trained embeddings that are available from [spaCy](https://spacy.io/models/) can be used for encoding text into vectors. They are fast, robust and good alternative for a lot of language models. To use spacy models in embedding function we have to install spacy module and also download a model of our choice. Please use the below snippet to install and download a model of our choice.
+
+```bash
+pip install spacy
+```
+
+```bash
+spacy download model_name
+```
+
+For the list models please visit: [spacy-models](https://spacy.io/models/)
+
+```python
+import chromadb.utils.embedding_functions as embedding_functions
+ef = embedding_functions.SpacyEmbeddingFunction(model_name="en_core_web_md")
+embeddings = ef(["text-1", "text-2"])
+```