ComponentsText embedding modelsOllamaOllamaLet's load the Ollama Embeddings class.from langchain.embeddings import OllamaEmbeddingsembeddings = OllamaEmbeddings()text = "This is a test document."To generate embeddings, you can either query an invidivual text, or you can query a list of texts.query_result = embeddings.embed_query(text)query_result[:5]    [-0.09996652603149414,     0.015568195842206478,     0.17670190334320068,     0.16521021723747253,     0.21193109452724457]doc_result = embeddings.embed_documents([text])doc_result[0][:5]    [-0.04242777079343796,     0.016536075621843338,     0.10052520781755447,     0.18272875249385834,     0.2079043835401535]Let's load the Ollama Embeddings class with smaller model (e.g. llama:7b). Note: See other supported models https://ollama.ai/libraryembeddings = OllamaEmbeddings(model="llama2:7b")text = "This is a test document."query_result = embeddings.embed_query(text)query_result[:5]    [-0.09996627271175385,     0.015567859634757042,     0.17670205235481262,     0.16521376371383667,     0.21193283796310425]doc_result = embeddings.embed_documents([text])doc_result[0][:5]    [-0.042427532374858856,     0.01653730869293213,     0.10052604228258133,     0.18272635340690613,     0.20790338516235352]PreviousNLP CloudNextOpenAI