Merge pull request #202 from amikos-tech/feature/improve-example-readability-imports

[DOCS]: Improved example readability

Author: jeffchuber

docs/embeddings/cohere.md

@@ -21,6 +21,7 @@ Chroma also provides a convenient wrapper around Cohere's embedding API. This em
 This embedding function relies on the `cohere` python package, which you can install with `pip install cohere`.
 
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 cohere_ef  = embedding_functions.CohereEmbeddingFunction(api_key="YOUR_API_KEY",  model_name="large")
 cohere_ef(texts=["document1","document2"])
 ```

docs/embeddings/google-gemini.md

@@ -24,8 +24,7 @@ This embedding function relies on the `google-generativeai` python package, whic
 
 ```python
 # import
-import chromadb
-from chromadb.utils import embedding_functions
+import chromadb.utils.embedding_functions as embedding_functions
 
 # use directly
 google_ef  = embedding_functions.GoogleGenerativeAiEmbeddingFunction(api_key="YOUR_API_KEY")

docs/embeddings/google-palm.md

@@ -8,6 +8,7 @@
 To use the PaLM embedding API, you must have `google.generativeai` Python package installed and have the API key. To use:
 
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 palm_embedding = embedding_functions.GooglePalmEmbeddingFunction(
     api_key=api_key, model=model_name)
 

docs/embeddings/hugging-face.md

@@ -21,6 +21,7 @@ Chroma also provides a convenient wrapper around HuggingFace's embedding API. Th
 This embedding function relies on the `requests` python package, which you can install with `pip install requests`.
 
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
     api_key="YOUR_API_KEY",
     model_name="sentence-transformers/all-MiniLM-L6-v2"

docs/embeddings/instructor.md

@@ -9,10 +9,12 @@ There are three models available. The default is `hkunlp/instructor-base`, and f
 
 ```python
 #uses base model and cpu
+import chromadb.utils.embedding_functions as embedding_functions
 ef = embedding_functions.InstructorEmbeddingFunction() 
 ```
 or
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 ef = embedding_functions.InstructorEmbeddingFunction(
 model_name="hkunlp/instructor-xl", device="cuda")
 ```

docs/embeddings/jinaai.md

@@ -21,6 +21,7 @@ Chroma provides a convenient wrapper around JinaAI's embedding API. This embeddi
 This embedding function relies on the `requests` python package, which you can install with `pip install requests`.
 
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 jinaai_ef = embedding_functions.JinaEmbeddingFunction(
                 api_key="YOUR_API_KEY",
                 model_name="jina-embeddings-v2-base-en"

docs/embeddings/openai.md

@@ -21,7 +21,7 @@ Chroma provides a convenient wrapper around OpenAI's embedding API. This embeddi
 This embedding function relies on the `openai` python package, which you can install with `pip install openai`.
 
 ```python
-import chromadb.utils.embedding_functions
+import chromadb.utils.embedding_functions as embedding_functions
 openai_ef = embedding_functions.OpenAIEmbeddingFunction(
                 api_key="YOUR_API_KEY",
                 model_name="text-embedding-ada-002"
@@ -30,6 +30,7 @@ openai_ef = embedding_functions.OpenAIEmbeddingFunction(
 
 To use the OpenAI embedding models on other platforms such as Azure, you can use the `api_base` and `api_type` parameters: 
 ```python
+import chromadb.utils.embedding_functions as embedding_functions
 openai_ef = embedding_functions.OpenAIEmbeddingFunction(
                 api_key="YOUR_API_KEY",
                 api_base="YOUR_API_BASE_PATH",