Resposta de LLMs em cache com MongoDB Atlas e Vector Atlas Search

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pip install fastapi pymongo openai uvicorn

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from fastapi import FastAPI
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server = FastAPI()
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# root route
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@server.get("/")
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async def home():
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  return { "message": "This is home server" }
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# search route
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@server.get("/ask")
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async def search(query: str):
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  return { "message": "The query is: {}".format(query) }

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uvicorn app:server --reload

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curl -X GET "http://127.0.0.1:8000/ask?query=hello+this+is+search+query"

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{ "message": "The query is: hello this is search query" }

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from openai import OpenAI
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open_api_key = "..."  # OpenAI API Key
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openai_client = OpenAI(api_key=open_api_key)
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language_model = "gpt-3.5-turbo"
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# getTextResponse receive text and ask LLM for answer
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def getTextResponse(text):
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	chat_completion = openai_client.chat.completions.create(
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    	messages=[{"role": "user", "content": text}], model=language_model
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	)
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	return chat_completion.choices[0].message.content

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# ... other import dependencies
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from llm import getTextResponse
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# ... other routes
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# search route
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@server.get("/ask")
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async def search(query):
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	llm_response = getTextResponse(query)
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	return {"message": "Your AI response is: {}".format(llm_response)}

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curl -X GET "http://127.0.0.1:8000/ask?query=what+is+llm?"
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Response
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{
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  "message": "Your AI response is: LLM stands for Master of Laws, which is an advanced law degree typically pursued by individuals who have already received a law degree (such as a JD) and want to further specialize or advance their knowledge in a specific area of law. LLM programs are typically one year in length and often focus on areas such as international law, human rights, or commercial law."
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}

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text = "Large Language Model"
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# embedding_function(text: str) -> vector<number>([...N])
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vector = embedding_function(text)
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# vector = [12, 23, 0.11, 22, 85, 43, ..., 90]

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# ... other code
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text_embedding_model = "text-embedding-3-small"
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# getEmbedding receives text and response embedding (vector) of its original data
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def getEmbedding(text):
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	embedding = openai_client.embeddings.create(input=text, model=text_embedding_model)
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	return embedding.data[0].embedding

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# ... other import dependencies
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from llm import getTextResponse, getEmbedding
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# ... other routes
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# search route
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@server.get("/ask")
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async def search(query):
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	llm_response = getTextResponse(query)
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	query_vector = getEmbedding(query)
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	print("embedding : ", query_vector)
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	return {"message": "Your AI response is: {}".format(llm_response)}

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embedding :  [-0.02254991, 0.031336114, 0.019013261, 0.00017081834, -0.0202526, -0.0020466715, 0.0111036645, -0.0111036645, 0.036172554, 0.04038429, -0.027043771, 0.0046273666, -0.039820038, -0.011456322, 0.0048339227, 0.021824444, 0.0048666694, -0.017501874, 0.03915503, 0.03895351, 0.041311275, ..., 0.046349235]

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import pymongo
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MONGO_URI = ""  # MongoDB connection string
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mongo_client = pymongo.MongoClient(MONGO_URI)
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db = mongo_client.get_database("logging")  # database name in mongodb
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collection = db.get_collection("test")  # collection name
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# document {
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#   query: string,
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#   response: string,
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#   embeddings: vector<number<1408>>
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# }
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def save_cache(document):
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	collection.insert_one(document)

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# ... other import dependencies
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from db import save_cache
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# ... other code
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# search route
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@server.get("/ask")
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async def search(query):
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	llm_response = getTextResponse(query)
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	query_vector = getEmbedding(query)
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	document = {
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  	"response": llm_response,
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  	"embeddings": query_vector,
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  	"query": query
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  	}
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	save_cache(document)
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	return {"message": "Your AI response is: {}".format(llm_response)}

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{
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  "type": "vectorSearch", # vector search identity
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  "fields": [
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	{
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  	# type of data in the asking the system again with a new question (but similar meaning): Howfield (vector information)
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  	"type": "vector",
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  	# field of document that store vector information
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  	"path": "embeddings",
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  	# dimension of vector, receiving from language model specification.
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  	# for `text-embedding-3-small` of OpenAI, it's 1408 dimensions of vector.
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  	"numDimensions": 1408,
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  	# vector search find similarity of searching document with other. So, closest documents would get high score.
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  	"similarity": "cosine"
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	}
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  ]
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}

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# Step 1: Perform vector search
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{
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  # $vectorSearch operation
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    "$vectorSearch": {
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        # Vector Search index in Atlas
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        "index": "vector_index",
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        # vector information in document
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        "path": "embeddings",
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        # embedding information of client's query
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        "queryVector": numerical_embedding,
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       # number of nearest neighbors to find the closest group. This value is used for 'searchScore' ranking
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        "numCandidates": 20,
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           # number of document to return ranked by semantic meaning / searchScore
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        "limit": 5,
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    },
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},
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# add 'score' field to show vector search score.
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{
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  "$addFields": {
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  "score": {
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    # vectorSearchScore is attached in $meta for pipeline after vectorSearch operation
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    "$meta": "vectorSearchScore"
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    }
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  }
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},
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# return document that score greater than specific ratio
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# in this case, only top one that has ration > 70% is going to return.
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{
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  "$match": {
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    "score": { "$gte": 0.70 }
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  }
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},
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# remove `embeddings` field. Because it reduce load to retrieve data.
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{
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  "$unset": ["embeddings"]
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}
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]

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# ... other code
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def perform_search_cache(query):
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	pipeline = [
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    	{
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        	"$vectorSearch": {
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            	"index": "vector_index",
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            	"path": "embeddings",
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            	"queryVector": query,
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            	"numCandidates": 20,
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            	"limit": 1,
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        	},
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    	},
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    	{"$addFields": {"score": {"$meta": "vectorSearchScore"}}},
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    	{"$match": {"score": {"$gte": 0.70}}},
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    	{"$unset": ["embeddings"]},
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	]
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	result = collection.aggregate(pipeline)
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	return result

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# ... other import dependencies
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from db import save_cache, perform_search_cache
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# ... other code
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# search route
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@server.get("/ask")
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async def search(query):
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	query_vector = getEmbedding(query)
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	cache_response = list(perform_search_cache(query_vector))
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	response = ""
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	if len(cache_response) < 1:
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    	# in case no cache hit
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    	# perform search and save the cache
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    	llm_response = getTextResponse(query)
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    	document = {"response": llm_response, "embeddings": query_vector, "query": query}
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    	save_cache(document)
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    	response = llm_response
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	else:
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    	# if cache hit, return it
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    	response = cache_response[0]["response"]
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    	print("cache hit")
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    	print(cache_response)
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	return {"message": "Your AI response is: {}".format(response)}

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curl -X GET "http://127.0.0.1:8000/ask?query=how+are+things+with+you?"

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{
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  "message": "Your AI response is: I'm just a computer program, so I don't have feelings or emotions. But I'm here to help you with anything you need! How can I assist you today?"
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}

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curl -X GET "http://127.0.0.1:8000/ask?query=how+are+you+today?"

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{
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  "message": "Your AI response is: I'm just a computer program, so I don't have feelings or emotions. But I'm here to help you with anything you need! How can I assist you today?"
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}

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cache hit
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[
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  {
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	'_id': ObjectId('6671440cb2bf0b0eb12b75b3'),
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	'response': "I'm just a computer program, so I don't have feelings or emotions. But I'm here to help you with anything you need! How can I assist you today?",
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	'query': 'how are thing with you?',
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	'score': 0.8066450357437134
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  }
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]