feat: Updated LlamaIndex EF adapter example

Author: tazarov

docs/integrations/llamaindex/embeddings.md

@@ -4,36 +4,45 @@
 
 Chroma and LlamaIndex both offer embedding functions which are wrappers on top of popular embedding models.
 
-Unfortunately Chroma and LI's embedding functions are not compatible with each other. Below we offer an adapters to convert LI embedding function to Chroma one.
+Unfortunately Chroma and LI's embedding functions are not compatible with each other. Below we offer an adapters to
+convert LI embedding function to Chroma one.
 
 ```python
-from llama_index.embeddings.base import BaseEmbedding
-from chromadb.api.types import EmbeddingFunction
+from llama_index.core.schema import TextNode
+from llama_index.core.base.embeddings.base import BaseEmbedding
+from chromadb import EmbeddingFunction, Documents, Embeddings
+
 
 class LlamaIndexEmbeddingAdapter(EmbeddingFunction):
-  def __init__(self,ef:BaseEmbedding):
-    self.ef = ef
+    def __init__(self, ef: BaseEmbedding):
+        self.ef = ef
 
-  def __call__(self, input: Documents) -> Embeddings:
-    return [node.embedding for node in self.ef(input)]
+    def __call__(self, input: Documents) -> Embeddings:
+        return [node.embedding for node in self.ef([TextNode(text=doc) for doc in input])]
 
 ```
 
+!!! warn "Text modality"
+
+    The above adapter assumes that the input documents are text. If you are using a different modality, 
+    you will need to modify the adapter accordingly.
+
 An example of how to use the above with LlamaIndex:
 
-> Note: Make sure you have `OPENAI_API_KEY` as env var.
+!!! note "Prerequisites for example"
+
+    Run `pip install llama-index chromadb llama-index-embeddings-fastembed fastembed`
 
 ```python
-from llama_index.embeddings import OpenAIEmbedding
-from llama_index import ServiceContext, set_global_service_context
 import chromadb
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
 
-embed_model = OpenAIEmbedding(embed_batch_size=10)
+# make sure to include the above adapter and imports
+embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
 
 client = chromadb.Client()
 
-col = client.get_or_create_collection("test_collection",embedding_function=LlamaIndexEmbeddingAdapter(embed_model))
+col = client.get_or_create_collection("test_collection", embedding_function=LlamaIndexEmbeddingAdapter(embed_model))
 
-col.add(ids=["1"],documents=["this is a test document"])
-# your embeddings should be of 1536 dimensions (OpenAI's ADA model)
-```
+col.add(ids=["1"], documents=["this is a test document"])
+```