FastChat Support more OpenAI-compatible APIs (embedding, completion)

This PR adds support for a subset of OpenAI API features, including completion, create embeddings, and chat completion. With these changes, users will be able to leverage the local LLM to generate completions, embeddings, and chat completions.

Please start your worker and controller with FastChat API

Try chat completion with

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vicuna-7b-v1.1",
    "messages": [{"role": "user", "content": "Hello, can you tell me a joke for me?"}],
    "temperature": 0.5
  }'

Try create embedding with

curl http://localhost:8000/v1/create_embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vicuna-7b-v1.1",
    "input": "Hello, can you tell me a joke for me?"
  }'

Try text completion with

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vicuna-7b-v1.1",
    "prompt": "Once upon a time",
    "max_tokens": 20,
    "temperature": 0.5
  }'

Try create_embeddings to analyze the prompts!

import json
import numpy as np
import requests
from scipy.spatial.distance import cosine


def get_embedding_from_api(word, model='vicuna-7b-v1.1'):
    url = 'http://localhost:8000/v1/create_embeddings'
    headers = {'Content-Type': 'application/json'}
    data = json.dumps({
        'model': model,
        'input': word
    })

    response = requests.post(url, headers=headers, data=data)
    if response.status_code == 200:
        embedding = np.array(response.json()['data'][0]['embedding'])
        return embedding
    else:
        print(f"Error: {response.status_code} - {response.text}")
        return None


def cosine_similarity(vec1, vec2):
    return 1 - cosine(vec1, vec2)


def print_cosine_similarity(embeddings, texts):
    for i in range(len(texts)):
        for j in range(i + 1, len(texts)):
            sim = cosine_similarity(embeddings[texts[i]], embeddings[texts[j]])
            print(f"Cosine similarity between '{texts[i]}' and '{texts[j]}': {sim:.2f}")


texts = [
    'The quick brown fox',
    'The quick brown dog',
    'The fast brown fox',
    'A completely different sentence'
]

embeddings = {}
for text in texts:
    embeddings[text] = get_embedding_from_api(text)

print_cosine_similarity(embeddings, texts)

create_embeddings can even do semantic search just like openai apis! see openai semantic search


def search_reviews(df, product_description, n=3, pprint=True):
    product_embedding = get_embedding_from_api(
        product_description,
    )
    df["similarity"] = df.embedding.apply(lambda x: cosine_similarity(x, product_embedding))

    results = (
        df.sort_values("similarity", ascending=False)
        .head(n)
        .combined.str.replace("Title: ", "")
        .str.replace("; Content:", ": ")
    )
    if pprint:
        for r in results:
            print(r[:200])
            print()
    return results

input_datapath = "data/fine_food_reviews_1k.csv"  
df = pd.read_csv(input_datapath, index_col=0)
df = df[["Time", "ProductId", "UserId", "Score", "Summary", "Text"]]
df = df.dropna()
df["combined"] = (
    "Title: " + df.Summary.str.strip() + "; Content: " + df.Text.str.strip()
)
top_n = 1000
df = df.sort_values("Time").tail(top_n * 2)  
df.drop("Time", axis=1, inplace=True)

df["n_tokens"] = df.combined.apply(lambda x: len(x))
df = df[df.n_tokens <= 500].tail(top_n)

df["embedding"] = df.combined.apply(lambda x: np.array2string(get_embedding_from_api(x), separator=',', max_line_width=np.inf))

results = search_reviews(df, "delicious beans", n=20)
print(results)

Feel free to use create_embedding to build your own classifier! You can find all examples on the playground.