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Semantic Search

Semantic Search is, to retrieve the expected results and generate human-readable conversational responses with the help of LLM (Large Language Model).

First we need to create LLM and vector db object.

LLM:

import os
os.environ['OPENAI_API_KEY'] = "<openai_api_key>"
from semantic_ai.llm import Openai

llm_model = await Openai().llm_model()

Vector DB:

from semantic_ai.indexer import ElasticsearchIndexer
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=llm_model,
            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=llm_model,
            load_vector_db=elastic_search,
            top_k=5,
            prompt=prompt
)
query = "What is an AI"
search = await search_obj.generate(query)