How can I use embeddings for semantic search inside a chatbot?
Asked on Oct 01, 2025
Answer
To use embeddings for semantic search within a chatbot, you can leverage vector representations of text to find semantically similar responses or documents. This typically involves using an embedding model, such as OpenAI's embeddings API, to convert text into vectors and then performing similarity comparisons.
<!-- BEGIN COPY / PASTE -->
import openai
import numpy as np
# Example function to get embeddings
def get_embedding(text):
response = openai.Embedding.create(input=text, model="text-embedding-ada-002")
return np.array(response['data'][0]['embedding'])
# Calculate similarity (e.g., cosine similarity)
def cosine_similarity(vec1, vec2):
return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
# Example usage
user_query = "What is the weather like today?"
query_embedding = get_embedding(user_query)
# Compare with stored embeddings of potential responses
<!-- END COPY / PASTE -->Additional Comment:
- Embeddings allow the chatbot to understand the meaning behind user queries rather than relying on keyword matching.
- Ensure that the embedding model you use is suitable for your language and domain requirements.
- Consider storing precomputed embeddings for frequent responses to improve performance.
- Use a similarity threshold to determine if a response is relevant enough to be returned to the user.
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