import requests

texts = [
    "Artificial intelligence is transforming technology",
    "Machine learning enables computers to learn patterns",
    "Deep learning uses neural networks for complex tasks",
    "Natural language processing helps computers understand text"
]

response = requests.post(
    "https://api.anyapi.ai/v1/embeddings",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "cohere/embed-v4",
        "input": texts,
        "encoding_format": "float"
    }
)

embeddings = response.json()["data"]
for i, embedding in enumerate(embeddings):
    print(f"Text {i+1}: {len(embedding['embedding'])} dimensions")

import requests
from sklearn.metrics.pairwise import cosine_similarity

class SemanticSearchEngine:
    def __init__(self, model="cohere/embed-v4"):
        self.model = model
        self.documents = []
        self.embeddings = []

    def add_documents(self, texts):
        """Add documents to search index"""
        response = requests.post(
            "https://api.anyapi.ai/v1/embeddings",
            headers={"Authorization": "Bearer your_api_key"},
            json={
                "model": self.model,
                "input": texts
            }
        )

        embeddings = response.json()["data"]

        for i, text in enumerate(texts):
            self.documents.append(text)
            self.embeddings.append(embeddings[i]["embedding"])

    def search(self, query, top_k=5):
        """Search for similar documents"""
        response = requests.post(
            "https://api.anyapi.ai/v1/embeddings",
            headers={"Authorization": "Bearer your_api_key"},
            json={
                "model": self.model,
                "input": query
            }
        )

        query_embedding = response.json()["data"][0]["embedding"]

        similarities = []
        for i, doc_embedding in enumerate(self.embeddings):
            similarity = cosine_similarity([query_embedding], [doc_embedding])[0][0]
            similarities.append({
                "document": self.documents[i],
                "similarity": similarity,
                "index": i
            })

        similarities.sort(key=lambda x: x["similarity"], reverse=True)
        return similarities[:top_k]

# Usage example
search_engine = SemanticSearchEngine()

documents = [
    "Python is a programming language",
    "Machine learning algorithms learn from data",
    "Cats are popular pets",
    "Neural networks are used in AI",
    "Dogs need regular exercise"
]

search_engine.add_documents(documents)
results = search_engine.search("artificial intelligence", top_k=3)

for result in results:
    print(f"Similarity: {result['similarity']:.3f} - {result['document']}")

import requests

def rag_system(query, knowledge_base):
    """Simple RAG implementation"""
    # 1. Get relevant documents using embeddings
    search_engine = SemanticSearchEngine()
    search_engine.add_documents(knowledge_base)
    relevant_docs = search_engine.search(query, top_k=3)

    # 2. Create context from relevant documents
    context = "\n".join([doc["document"] for doc in relevant_docs])

    # 3. Generate answer using context
    response = requests.post(
        "https://api.anyapi.ai/v1/chat/completions",
        headers={"Authorization": "Bearer your_api_key"},
        json={
            "model": "google/gemini-2.5-flash",
            "messages": [
                {
                    "role": "user",
                    "content": f"Context: {context}\n\nQuestion: {query}\n\nAnswer based on the context:"
                }
            ]
        }
    )

    return response.json()["choices"][0]["message"]["content"]