I have this below code that works well when I connect to OpenAI. I am trying to do the same using llama2 but I am having trouble using the right embedding technique.. In the below code I use OpenAIEmbeddings, want some help to find how can this be tweaked to run well using llama2 model

 from langchain_community.vectorstores import Chroma
 from langchain_core.example_selectors import SemanticSimilarityExampleSelector
 from langchain_openai import OpenAIEmbeddings

 vectorstore = Chroma()
 vectorstore.delete_collection()
 example_selector = SemanticSimilarityExampleSelector.from_examples(
     examples,
     OpenAIEmbeddings(),
     vectorstore,
     k=2,
     input_keys=["input"],
 )
 example_selector.select_examples({"input": "what is the customer name tagged to order 3455"})
 few_shot_prompt = FewShotChatMessagePromptTemplate(
     example_prompt=example_prompt,
     example_selector=example_selector,
     input_variables=["input","top_k"],
 )
 print(few_shot_prompt.format(input="what is the customer name tagged to order 3455"))