[Question]: RateLimit Error llamaindex

[mirallm]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

Im using this code to generate embeddings. I have rate limit errors and warnings. I have no idea what is being embedded and what's not. Moreover, I dont know how to deal with the ones that are not embedded.         


` 
                    if self.vector_index is None:
                            
                            
                                # Create a new index if not already created
                                self.vector_index = VectorStoreIndex(
                                    nodes=nodes,
                                    use_async=False,
                                
                                    embed_model=embed_model,
                                    storage_context=self.storage_context, 
                                    show_progress=True
                                )
                                print(self.vector_index.index_struct)
                                print("Created new index.")
                    else:
                                # If index exists, load it and insert the nodes
                                print("Loading existing index and inserting nodes.")
                                await self.load_index(embed_model)
                           
                                self.vector_index.build_index_from_nodes(nodes, use_async=True)`

Shouldnt llamaindex deal with ratelimit error. How can i audit that and control that and make sure everything is being embedded. Please provide me with a code and an explanation. @dosubot

[dosubot]

I found a similar open issue regarding rate limit errors when using LlamaIndex for generating embeddings. The suggested solution is to implement a retry mechanism using the tenacity library, which allows handling rate limit errors by retrying the request with an exponential backoff strategy. Here's a code snippet that demonstrates how to set up this retry mechanism:

from tenacity import retry, wait_random_exponential, stop_after_attempt

# Define your retry strategy
retry_decorator = retry(
    wait=wait_random_exponential(min=4, max=10),  # Random exponential backoff between 4 and 10 seconds
    stop=stop_after_attempt(5)  # Maximum number of retries
)

# Apply the retry decorator to your function that makes the API call
@retry_decorator
async def call_openai_embedding():
    # Your code to call the OpenAI API
    pass

# Use the function
await call_openai_embedding()

This approach helps manage rate limit errors by automatically retrying the request if a RateLimitError occurs, with a delay that increases exponentially between retries ^[1].

Additionally, to ensure that all data is embedded correctly, you can implement a mechanism to handle rate limit errors by waiting for the reset time specified in the response headers when a rate limit error (HTTP status code 429) occurs. Here's an example:

import requests
import time

def generate_embedding(text, model_api_string, api_key, api_base):
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "Authorization": f"Bearer {api_key}",
    }
    session = requests.session()
    while True:
        response = session.post(
            api_base.strip("/") + "/embeddings",
            headers=headers,
            json={"input": text, "model": model_api_string},
        )
        if response.status_code != 200:
            if response.status_code == 429:
                """Rate limit exceeded, wait for reset"""
                reset_time = int(response.headers.get("X-RateLimit-Reset", 0))
                if reset_time > 0:
                    time.sleep(reset_time)
                    continue
                else:
                    """Rate limit reset time has passed, retry immediately"""
                    continue

            """ Handle other non-200 status codes """
            raise ValueError(
                f"Request failed with status code {response.status_code}: {response.text}"
            )

        return response.json()["data"][0]["embedding"]

# Example usage
# generate_embedding("your text", "your_model_api_string", "your_api_key", "https://api.together.xyz/v1")

This code ensures that all data is embedded correctly by retrying the request until it succeeds, handling rate limit errors appropriately ^[2].

To continue talking to Dosu, mention @dosu.

---

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[mirallm]

@dosubot i need you to provide me a solution based on my code. I use this code " self.vector_index.build_index_from_nodes(nodes, use_async=True)". 1) how can i audit which nodes have the embeddings which not 2) how can i add the retry decorator? Please based on my code, show me how to deal with it . 3)doesnt llamaindex deal with it in the background?

[dosubot]

1. To audit which nodes have embeddings and which do not, you can check the id_to_embed_map dictionary returned by the embed_nodes function. This dictionary maps node IDs to their corresponding embeddings. If a node's ID is present in this dictionary, it means that the node has been successfully embedded ^[1].

2. To add the retry decorator to your code, you can use the llm_retry_decorator function from the LlamaIndex library, which is designed to handle retries for both synchronous and asynchronous methods. Here's how you can apply it to your code:

from llama_index.llms.utils import llm_retry_decorator

# Assuming build_index_from_nodes is a method of a class
class YourClass:
    def __init__(self, max_retries):
        self.max_retries = max_retries

    @llm_retry_decorator
    async def build_index_from_nodes(self, nodes, use_async=True):
        # Your existing code to build the index
        self.vector_index.build_index_from_nodes(nodes, use_async=use_async)

# Example usage
your_instance = YourClass(max_retries=5)
await your_instance.build_index_from_nodes(nodes, use_async=True)

This decorator will automatically retry the embedding process if an exception occurs, using an exponential backoff strategy ^[2][3][4].

3. LlamaIndex does not handle rate limit errors in the background by default. You need to implement a retry mechanism, as shown above, to manage rate limit errors effectively.

To continue talking to Dosu, mention @dosu.

---

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