OpenAI¶

OpenAI offers a spectrum of models with different levels of power suitable for different tasks.

Setup OpenAI api key:

import os
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

from semantic_ai.llm import Openai
openai_llm = await Openai().llm_model()

If you manually want to specify your OpenAI API key and/or organization ID, you can use the following:

openai_llm = await Openai(openai_api_key="api_key").llm_model()

Vector Database:

Now we are going to use Elastic Search vector database.

from semantic_ai.indexer import ElasticsearchIndexer
from semantic_ai.embeddings.huggingface import HFEmbeddings

embeddings = await HFEmbeddings().embed()
elastic_search = await ElasticsearchIndexer(
        url="http://localhost:9200",
        index_name="test_index",
        embedding=embeddings
).create()

Search:

from semantic_ai.search.semantic_search import Search
search_obj = Search(
            model=openai_llm,
            load_vector_db=elastic_search
)
query = "What is an AI"
search = await search_obj.generate(query)

We can change the top_k value and prompt using top_k and ‘prompt’ params respectively

search_obj = Search(
            model=openai_llm,
            load_vector_db=elastic_search,
            top_k=5,
            prompt=prompt
)
query = "What is an AI"
search = await search_obj.generate(query)