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Documentation Index

Fetch the complete documentation index at: https://docs.aurelio.ai/llms.txt

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Route score thresholds are what defines whether a route should be chosen. If the score we identify for any given route is higher than the Route.score_threshold it passes, otherwise it does not and either another route is chosen, or we return no route. Given that this one score_threshold parameter can define the choice of a route, it’s important to get it right — but it’s incredibly inefficient to do so manually. Instead, we can use the fit and evaluate methods of our SemanticRouter. All we must do is pass a smaller number of (utterance, target route) examples to our methods, and with fit we will often see dramatically improved performance within seconds.

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Full Example

Open In Colab Open nbviewer 
!pip install -qU "semantic-router>=0.1.5"

​
Define SemanticRouter

As usual we will define our SemanticRouter. The SemanticRouter requires just routes and an encoder. If using dynamic routes you must also define an llm (or use the OpenAI default). We will start by defining four routes; politics, chitchat, mathematics, and biology. 
from semantic_router import Route

# we could use this as a guide for our chatbot to avoid political conversations
politics = Route(
    name="politics",
    utterances=[
        "isn't politics the best thing ever",
        "why don't you tell me about your political opinions",
        "don't you just love the president" "don't you just hate the president",
        "they're going to destroy this country!",
        "they will save the country!",
    ],
)

# this could be used as an indicator to our chatbot to switch to a more
# conversational prompt
chitchat = Route(
    name="chitchat",
    utterances=[
        "Did you watch the game last night?",
        "what's your favorite type of music?",
        "Have you read any good books lately?",
        "nice weather we're having",
        "Do you have any plans for the weekend?",
    ],
)

# we can use this to switch to an agent with more math tools, prompting, and LLMs
mathematics = Route(
    name="mathematics",
    utterances=[
        "can you explain the concept of a derivative?",
        "What is the formula for the area of a triangle?",
        "how do you solve a system of linear equations?",
        "What is the concept of a prime number?",
        "Can you explain the Pythagorean theorem?",
    ],
)

# we can use this to switch to an agent with more biology knowledge
biology = Route(
    name="biology",
    utterances=[
        "what is the process of osmosis?",
        "can you explain the structure of a cell?",
        "What is the role of RNA?",
        "What is genetic mutation?",
        "Can you explain the process of photosynthesis?",
    ],
)

# we place all of our decisions together into single list
routes = [politics, chitchat, mathematics, biology]
For our encoder we will use the local HuggingFaceEncoder. Other popular encoders include CohereEncoder, FastEmbedEncoder, OpenAIEncoder, and AzureOpenAIEncoder. 
from semantic_router.encoders import HuggingFaceEncoder

encoder = HuggingFaceEncoder()
Now we initialize our SemanticRouter. 
from semantic_router import SemanticRouter

sr = SemanticRouter(encoder=encoder, routes=routes)
By default, we should get reasonable performance: 
for utterance in [
    "don't you love politics?",
    "how's the weather today?",
    "What's DNA?",
    "I'm interested in learning about llama 2",
]:
    print(f"{utterance} -> {sr(utterance).name}")

don't you love politics? -> politics
how's the weather today? -> chitchat
What's DNA? -> biology
I'm interested in learning about llama 2 -> None
We can evaluate the performance of our route layer using the evaluate method. All we need is to pass a list of utterances and target route labels: 
test_data = [
    ("don't you love politics?", "politics"),
    ("how's the weather today?", "chitchat"),
    ("What's DNA?", "biology"),
    ("I'm interested in learning about llama 2", None),
]

# unpack the test data
X, y = zip(*test_data)

# evaluate using the default thresholds
accuracy = sr.evaluate(X=X, y=y)
print(f"Accuracy: {accuracy*100:.2f}%")

Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 76.91it/s]
Accuracy: 100.00%
On this small subset we get perfect accuracy — but what if we try with a larger, more robust dataset? Hint: try using GPT-4 or another LLM to generate some examples for your own use-cases. The more accurate examples you provide, the better you can expect the routes to perform on your actual use-case. 
test_data = [
    # politics
    ("What's your opinion on the current government?", "politics"),
    ("Who do you think will win the next election?", "politics"),
    ("What are your thoughts on the new policy?", "politics"),
    ("How do you feel about the political situation?", "politics"),
    ("Do you agree with the president's actions?", "politics"),
    ("What's your stance on the political debate?", "politics"),
    ("How do you see the future of our country?", "politics"),
    ("What do you think about the opposition party?", "politics"),
    ("Do you believe the government is doing enough?", "politics"),
    ("What's your opinion on the political scandal?", "politics"),
    ("Do you think the new law will make a difference?", "politics"),
    ("What are your thoughts on the political reform?", "politics"),
    ("Do you agree with the government's foreign policy?", "politics"),
    # chitchat
    ("What's the weather like?", "chitchat"),
    ("It's a beautiful day today.", "chitchat"),
    ("How's your day going?", "chitchat"),
    ("It's raining cats and dogs.", "chitchat"),
    ("Let's grab a coffee.", "chitchat"),
    ("What's up?", "chitchat"),
    ("It's a bit chilly today.", "chitchat"),
    ("How's it going?", "chitchat"),
    ("Nice weather we're having.", "chitchat"),
    ("It's a bit windy today.", "chitchat"),
    ("Let's go for a walk.", "chitchat"),
    ("How's your week been?", "chitchat"),
    ("It's quite sunny today.", "chitchat"),
    ("How are you feeling?", "chitchat"),
    ("It's a bit cloudy today.", "chitchat"),
    # mathematics
    ("What is the Pythagorean theorem?", "mathematics"),
    ("Can you solve this quadratic equation?", "mathematics"),
    ("What is the derivative of x squared?", "mathematics"),
    ("Explain the concept of integration.", "mathematics"),
    ("What is the area of a circle?", "mathematics"),
    ("How do you calculate the volume of a sphere?", "mathematics"),
    ("What is the difference between a vector and a scalar?", "mathematics"),
    ("Explain the concept of a matrix.", "mathematics"),
    ("What is the Fibonacci sequence?", "mathematics"),
    ("How do you calculate permutations?", "mathematics"),
    ("What is the concept of probability?", "mathematics"),
    ("Explain the binomial theorem.", "mathematics"),
    ("What is the difference between discrete and continuous data?", "mathematics"),
    ("What is a complex number?", "mathematics"),
    ("Explain the concept of limits.", "mathematics"),
    # biology
    ("What is photosynthesis?", "biology"),
    ("Explain the process of cell division.", "biology"),
    ("What is the function of mitochondria?", "biology"),
    ("What is DNA?", "biology"),
    ("What is the difference between prokaryotic and eukaryotic cells?", "biology"),
    ("What is an ecosystem?", "biology"),
    ("Explain the theory of evolution.", "biology"),
    ("What is a species?", "biology"),
    ("What is the role of enzymes?", "biology"),
    ("What is the circulatory system?", "biology"),
    ("Explain the process of respiration.", "biology"),
    ("What is a gene?", "biology"),
    ("What is the function of the nervous system?", "biology"),
    ("What is homeostasis?", "biology"),
    ("What is the difference between a virus and a bacteria?", "biology"),
    ("What is the role of the immune system?", "biology"),
    # add some None routes to prevent excessively small thresholds
    ("What is the capital of France?", None),
    ("how many people live in the US?", None),
    ("when is the best time to visit Bali?", None),
    ("how do I learn a language", None),
    ("tell me an interesting fact", None),
    ("what is the best programming language?", None),
    ("I'm interested in learning about llama 2", None),
]

# unpack the test data
X, y = zip(*test_data)

# evaluate using the default thresholds
accuracy = sr.evaluate(X=X, y=y)
print(f"Accuracy: {accuracy*100:.2f}%")

Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 9.23it/s]
Accuracy: 34.85%
Ouch, that’s not so good! Fortunately, we can easily improve our performance here.

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Router Optimization

Our optimization works by finding the best route thresholds for each Route in our SemanticRouter. We can see the current, default thresholds by calling the get_thresholds method: 
route_thresholds = sr.get_thresholds()
print("Default route thresholds:", route_thresholds)

Default route thresholds: {'politics': 0.5, 'chitchat': 0.5, 'mathematics': 0.5, 'biology': 0.5}
These are all preset route threshold values. Fortunately, it’s very easy to optimize these — we simply call the fit method and provide our training utterances X, and target route labels y: 
# Call the fit method
sr.fit(X=X, y=y)

Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 9.21it/s]
Training: 100%|██████████| 500/500 [00:01<00:00, 419.45it/s, acc=0.89]
Let’s see what our new thresholds look like: 
route_thresholds = sr.get_thresholds()
print("Updated route thresholds:", route_thresholds)

Updated route thresholds: {'politics': 0.05050505050505051, 'chitchat': 0.32323232323232326, 'mathematics': 0.18181818181818182, 'biology': 0.21212121212121213}
These are vastly different thresholds to what we were seeing before — it’s worth noting that optimal values for different encoders can vary greatly. For example, OpenAI’s Ada 002 model, when used with our encoders will tend to output much larger numbers in the 0.5 to 0.8 range. After training we have a final performance of: 
accuracy = sr.evaluate(X=X, y=y)
print(f"Accuracy: {accuracy*100:.2f}%")

Generating embeddings: 100%|██████████| 1/1 [00:00<00:00, 8.89it/s]
Accuracy: 89.39%
That is much better. If we wanted to optimize this further we can focus on adding more utterances to our existing routes, analyzing where exactly our failures are, and modifying our routes around those. This extended optimization process is much more manual, but with it we can continue optimizing routes to get even better performance.