search

run-llama / llama_index

LlamaIndex is a data framework for your LLM applications
https://docs.llamaindex.ai
MIT License
33.39k stars 4.67k forks source link

[Question]: how to slowdown the summary_index query #14551

Open gmdl007 opened 4 days ago

gmdl007 commented 4 days ago

Question Validation

Question

hi,

i am building a code for router agent to route between retrieval and summary for some docs, here is my code.

from llama_index.core import SummaryIndex from llama_index.core import VectorStoreIndex

summary_index = SummaryIndex(nodes, storage_context=storage_context) vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

list_query_engine = summary_index.as_query_engine( response_mode="tree_summarize", use_async=True, ) vector_query_engine = vector_index.as_query_engine()

from llama_index.core.tools import QueryEngineTool

list_tool = QueryEngineTool.from_defaults( query_engine=list_query_engine, description=( "Useful for summarization questions related to 8k on" " its documentations" ), )

vector_tool = QueryEngineTool.from_defaults( query_engine=vector_query_engine, description=( "Useful for retrieving specific context from 8k on" "its documentations." ), )

from llama_index.core.query_engine import RouterQueryEngine from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector from llama_index.core.selectors import ( PydanticMultiSelector, PydanticSingleSelector, )

query_engine = RouterQueryEngine( selector=PydanticSingleSelector.from_defaults(), query_engine_tools=[ list_tool, vector_tool, ], )

response = query_engine.query("give me the summary of the xyz") print(str(response))

INFO:llama_index.core.query_engine.router_query_engine:Selecting query engine 1: The summary of the upgrade process is a specific context that can be retrieved from the 8000 documentation.. Selecting query engine 1: The summary of the upgrade process is a specific context that can be retrieved from the 8000 documentation..

since the azure openAI is rate limited to certain rate, once the query started, it is too fast for the azure openai to handle: INFO:openai._base_client:Retrying request to /deployments/gpt-35-turbo/chat/completions in 0.904817 seconds Retrying request to /deployments/gpt-35-turbo/chat/completions in 0.904817 seconds INFO:httpx:HTTP Request: POST https://chat-ai.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 429 Too Many Requests" HTTP Request: POST https://chat-ai.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 429 Too Many Requests" INFO:httpx:HTTP Request: POST https://chat-ai.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 429 Too Many Requests"

can you help to reduce the rate of doing summary_index query?

Best

dosubot[bot] commented 4 days ago

To reduce the rate of doing the summary_index query and avoid hitting the rate limit, you can implement a rate limiting decorator. Here is an example of how you can do this in Python:

import time
import threading
from functools import wraps

def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

@rate_limit(allowed_per_second=1)  # Adjust the rate as needed
def query_summary_index(query):
    # Your existing query logic here
    return summary_index.query(query)

# Example usage
response = query_summary_index("give me the summary of the xyz")
print(str(response))

This decorator ensures that the query_summary_index function does not exceed the specified rate limit. Adjust the allowed_per_second parameter to control the rate of queries.

Additionally, you can consider implementing a caching mechanism to store the results of frequently queried summaries and reuse them instead of querying the summary_index every time. This can further reduce the number of queries and help avoid hitting the rate limit.

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_query_summary_index(query):
    return query_summary_index(query)

# Example usage
response = cached_query_summary_index("give me the summary of the xyz")
print(str(response))

By combining rate limiting and caching, you can effectively manage the rate of queries to the summary_index and avoid hitting the rate limit [1][2].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 3 days ago

Thanks! Will this work as well for :

query_engine = RouterQueryEngine( selector=PydanticSingleSelector.from_defaults(), query_engine_tools=[ list_tool, vector_tool, ], )

From: dosubot[bot] @.> Date: Wednesday, July 3, 2024 at 10:24 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To reduce the rate of doing the summary_index query and avoid hitting the rate limit, you can implement a rate limiting decorator. Here is an example of how you can do this in Python:

import time

import threading

from functools import wraps

def rate_limit(*, allowed_per_second: int):

max_period = 1.0 / allowed_per_second

last_call = [time.perf_counter()]

lock = threading.Lock()

def decorate(func):

    @wraps(func)

    def limit(*args, **kwargs):

        with lock:

            elapsed = time.perf_counter() - last_call[0]

            hold = max_period - elapsed

            if hold > 0:

                time.sleep(hold)

            result = func(*args, **kwargs)

            last_call[0] = time.perf_counter()

        return result

    return limit

return decorate

@rate_limit(allowed_per_second=1) # Adjust the rate as needed

def query_summary_index(query):

# Your existing query logic here

return summary_index.query(query)

Example usage

response = query_summary_index("give me the summary of the xyz")

print(str(response))

This decorator ensures that the query_summary_index function does not exceed the specified rate limit. Adjust the allowed_per_second parameter to control the rate of queries.

Additionally, you can consider implementing a caching mechanism to store the results of frequently queried summaries and reuse them instead of querying the summary_index every time. This can further reduce the number of queries and help avoid hitting the rate limit.

from functools import lru_cache

@lru_cache(maxsize=100)

def cached_query_summary_index(query):

return query_summary_index(query)

Example usage

response = cached_query_summary_index("give me the summary of the xyz")

print(str(response))

By combining rate limiting and caching, you can effectively manage the rate of queries to the summary_index and avoid hitting the rate limit [1]https://github.com/run-llama/llama_index/blob/main/docs/docs/understanding/indexing/indexing.md[2]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_05_25_23.json.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2207185199, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXENZQ3XTVF762WA4SLZKRMVZAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMBXGE4DKMJZHE. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 3 days ago

Yes, you can apply the rate limiting and caching decorators to the RouterQueryEngine as well. You can wrap the query method of the RouterQueryEngine with these decorators to control the rate of queries and cache the results. Here is an example of how you can do this:

import time
import threading
from functools import wraps, lru_cache
from llama_index.core.query_engine import RouterQueryEngine

# Rate limiting decorator
def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

# Caching decorator
@lru_cache(maxsize=100)
def cached_query(query_engine, query):
    return query_engine.query(query)

# Apply decorators to the RouterQueryEngine's query method
@rate_limit(allowed_per_second=1)  # Adjust the rate as needed
def rate_limited_query(query_engine, query):
    return cached_query(query_engine, query)

# Example usage
query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)

response = rate_limited_query(query_engine, "give me the summary of the xyz")
print(str(response))

In this example:

By combining these decorators, you can effectively manage the rate of queries to the RouterQueryEngine and avoid hitting the rate limit.

To continue talking to Dosu, mention @dosu.

gmdl007 commented 2 days ago

The problem is also, once it starts, it will not stop, I can not stop the code from sending many request towards the azure openAI server. Can I also implement number of request if using summary index?

From: dosubot[bot] @.> Date: Friday, July 5, 2024 at 11:15 AM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

Yes, you can apply the rate limiting and caching decorators to the RouterQueryEngine as well. You can wrap the query method of the RouterQueryEngine with these decorators to control the rate of queries and cache the results. Here is an example of how you can do this:

import time

import threading

from functools import wraps, lru_cache

from llama_index.core.query_engine import RouterQueryEngine

Rate limiting decorator

def rate_limit(*, allowed_per_second: int):

max_period = 1.0 / allowed_per_second

last_call = [time.perf_counter()]

lock = threading.Lock()

def decorate(func):

    @wraps(func)

    def limit(*args, **kwargs):

        with lock:

            elapsed = time.perf_counter() - last_call[0]

            hold = max_period - elapsed

            if hold > 0:

                time.sleep(hold)

            result = func(*args, **kwargs)

            last_call[0] = time.perf_counter()

        return result

    return limit

return decorate

Caching decorator

@lru_cache(maxsize=100)

def cached_query(query_engine, query):

return query_engine.query(query)

Apply decorators to the RouterQueryEngine's query method

@rate_limit(allowed_per_second=1) # Adjust the rate as needed

def rate_limited_query(query_engine, query):

return cached_query(query_engine, query)

Example usage

query_engine = RouterQueryEngine(

selector=PydanticSingleSelector.from_defaults(),

query_engine_tools=[

    list_tool,

    vector_tool,

],

)

response = rate_limited_query(query_engine, "give me the summary of the xyz")

print(str(response))

In this example:

By combining these decorators, you can effectively manage the rate of queries to the RouterQueryEngine and avoid hitting the rate limit.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2210510904, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXD5JBTKE4HOYFUMIQLZKZP3ZAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJQGUYTAOJQGQ. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 2 days ago

To stop your code from sending too many requests to the Azure OpenAI server when using summary_index and implement a limit on the number of requests, you can introduce a delay between requests to ensure you stay within the rate limits. Additionally, you can use the GPTSimpleVectorIndex.from_documents() method to manage the rate of embedding creation. Here is an example of how you can do this in Python:

import time
import openai

# Function to create embeddings with rate limiting
def create_embeddings_with_rate_limit(documents, rate_limit_per_minute):
    interval = 60 / rate_limit_per_minute  # Calculate the interval between requests
    embeddings = []

    for doc in documents:
        try:
            # Create embedding for the document
            embedding = openai.Embedding.create(input=doc, model="text-embedding-ada-002")
            embeddings.append(embedding)

            # Wait for the interval to respect the rate limit
            time.sleep(interval)
        except openai.error.RateLimitError as e:
            print(f"Rate limit error: {e}")
            # Optionally, you can implement a backoff strategy here
            time.sleep(interval)

    return embeddings

# Example usage
documents = ["Document 1", "Document 2", "Document 3"]
rate_limit_per_minute = 60  # Set your rate limit here
embeddings = create_embeddings_with_rate_limit(documents, rate_limit_per_minute)

This code ensures that you do not exceed the rate limit by introducing a delay between each request. Adjust the rate_limit_per_minute variable to match your specific rate limit.

Additionally, if you are using GPTSimpleVectorIndex.from_documents() and encountering rate limit issues, you can manage the rate of requests by implementing a similar delay mechanism or by batching your requests to stay within the allowed limits. If you continue to face issues, consider reaching out to OpenAI support or upgrading your pricing tier to increase your rate limits [1][2].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 1 day ago

I am still getting the same error despite I am setting it to 0.1

Apply decorators to the RouterQueryEngine's query method

@rate_limit(allowed_per_second=0.1) # Adjust the rate as needed def rate_limited_query(query_engine, query): return cached_query(query_engine, query)

HTTP Request: POST https://chat-ai.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2023-07-01-preview "HTTP/1.1 200 OK" INFO:llama_index.core.query_engine.router_query_engine:Selecting query engine 0: The question is asking for a summary of the upgrade process on 8000 router, which is related to the documentation of 8000.. Selecting query engine 0: The question is asking for a summary of the upgrade process on 8000, which is related to the documentation of 8000.. huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either:

From: dosubot[bot] @.> Date: Friday, July 5, 2024 at 3:22 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To stop your code from sending too many requests to the Azure OpenAI server when using summary_index and implement a limit on the number of requests, you can introduce a delay between requests to ensure you stay within the rate limits. Additionally, you can use the GPTSimpleVectorIndex.from_documents() method to manage the rate of embedding creation. Here is an example of how you can do this in Python:

import time

import openai

Function to create embeddings with rate limiting

def create_embeddings_with_rate_limit(documents, rate_limit_per_minute):

interval = 60 / rate_limit_per_minute  # Calculate the interval between requests

embeddings = []

for doc in documents:

    try:

        # Create embedding for the document

        embedding = openai.Embedding.create(input=doc, model="text-embedding-ada-002")

        embeddings.append(embedding)

        # Wait for the interval to respect the rate limit

        time.sleep(interval)

    except openai.error.RateLimitError as e:

        print(f"Rate limit error: {e}")

        # Optionally, you can implement a backoff strategy here

        time.sleep(interval)

return embeddings

Example usage

documents = ["Document 1", "Document 2", "Document 3"]

rate_limit_per_minute = 60 # Set your rate limit here

embeddings = create_embeddings_with_rate_limit(documents, rate_limit_per_minute)

This code ensures that you do not exceed the rate limit by introducing a delay between each request. Adjust the rate_limit_per_minute variable to match your specific rate limit.

Additionally, if you are using GPTSimpleVectorIndex.from_documents() and encountering rate limit issues, you can manage the rate of requests by implementing a similar delay mechanism or by batching your requests to stay within the allowed limits. If you continue to face issues, consider reaching out to OpenAI support or upgrading your pricing tier to increase your rate limits [1]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_05_25_23.json[2]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_06_02_23.json.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2210871617, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXGCCSLTTZFBBBZCJTTZK2M2NAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJQHA3TCNRRG4. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 1 day ago

To implement a mechanism to limit the number of requests when using summary_index and ensure that the code stops sending requests once the limit is reached, you can introduce a counter to track the number of requests and a limit variable to define the maximum number of requests allowed. Here is an example of how you can modify the existing code to include this mechanism:

import time
import threading
from functools import wraps

# Rate limiting decorator
def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

class LimitedRequestRouterQueryEngine:
    def __init__(self, query_engine, request_limit):
        self.query_engine = query_engine
        self.request_limit = request_limit
        self.request_count = 0

    @rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed
    def query(self, query):
        if self.request_count >= self.request_limit:
            print("Request limit reached. No more requests will be sent.")
            return None
        self.request_count += 1
        return self.query_engine.query(query)

# Example usage
query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)

limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=5)

response = limited_query_engine.query("give me the summary of the xyz")
print(str(response))

In this example:

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and avoid hitting the rate limit [1].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 1 day ago

Also getting this response from OpenAI

Error code: 400 - {'error': {'message': "This model's maximum context length is 4096 tokens. However, you requested 4172 tokens (3660 in the messages, 512 in the completion). Please reduce the length of the messages or completion.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}, '

From: dosubot[bot] @.> Date: Saturday, July 6, 2024 at 10:19 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To implement a mechanism to limit the number of requests when using summary_index and ensure that the code stops sending requests once the limit is reached, you can introduce a counter to track the number of requests and a limit variable to define the maximum number of requests allowed. Here is an example of how you can modify the existing code to include this mechanism:

import time

import threading

from functools import wraps

Rate limiting decorator

def rate_limit(*, allowed_per_second: int):

max_period = 1.0 / allowed_per_second

last_call = [time.perf_counter()]

lock = threading.Lock()

def decorate(func):

    @wraps(func)

    def limit(*args, **kwargs):

        with lock:

            elapsed = time.perf_counter() - last_call[0]

            hold = max_period - elapsed

            if hold > 0:

                time.sleep(hold)

            result = func(*args, **kwargs)

            last_call[0] = time.perf_counter()

        return result

    return limit

return decorate

class LimitedRequestRouterQueryEngine:

def __init__(self, query_engine, request_limit):

    self.query_engine = query_engine

    self.request_limit = request_limit

    self.request_count = 0

@rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed

def query(self, query):

    if self.request_count >= self.request_limit:

        print("Request limit reached. No more requests will be sent.")

        return None

    self.request_count += 1

    return self.query_engine.query(query)

Example usage

query_engine = RouterQueryEngine(

selector=PydanticSingleSelector.from_defaults(),

query_engine_tools=[

    list_tool,

    vector_tool,

],

)

limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=5)

response = limited_query_engine.query("give me the summary of the xyz")

print(str(response))

In this example:

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and avoid hitting the rate limit [1]https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/indices/llama-index-indices-managed-vectara/llama_index/indices/managed/vectara/retriever.py.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2211952429, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXEJOKLRQQMB3T6KESTZLBGOLAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJRHE2TENBSHE. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 1 day ago

To handle the "context length exceeded" error from OpenAI, you need to reduce the length of the messages or the completion. Here are some steps you can take:

  1. Set max_tokens: Limit the number of tokens generated in each response.
  2. Reduce Message Length: Truncate or summarize the input messages to reduce their length.
  3. Implement Retry Logic: Use the max_retries parameter to handle transient errors by retrying the request.
  4. Adjust Timeout: Set an appropriate timeout to avoid long waits for responses.
  5. Rate Limiting: Implement a rate-limiting mechanism to ensure you do not exceed the allowed number of requests per minute.

Here is an example of how you can configure these settings in the OpenAI class:

class OpenAI(LLM):
    model: str = Field(
        default=DEFAULT_OPENAI_MODEL, description="The OpenAI model to use."
    )
    temperature: float = Field(
        default=DEFAULT_TEMPERATURE,
        description="The temperature to use during generation.",
        gte=0.0,
        lte=1.0,
    )
    max_tokens: Optional[int] = Field(
        description="The maximum number of tokens to generate.",
        gt=0,
    )
    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict, description="Additional kwargs for the OpenAI API."
    )
    max_retries: int = Field(
        default=3,
        description="The maximum number of API retries.",
        gte=0,
    )
    timeout: float = Field(
        default=60.0,
        description="The timeout, in seconds, for API requests.",
        gte=0,
    )
    default_headers: Dict[str, str] = Field(
        default=None, description="The default headers for API requests."
    )
    reuse_client: bool = Field(
        default=True,
        description=(
            "Reuse the OpenAI client between requests. When doing anything with large "
            "volumes of async API calls, setting this to false can improve stability."
        ),
    )

    api_key: str = Field(default=None, description="The OpenAI API key.", exclude=True)
    api_base: str = Field(description="The base URL for OpenAI API.")
    api_version: str = Field(description="The API version for OpenAI API.")

    _client: Optional[SyncOpenAI] = PrivateAttr()
    _aclient: Optional[AsyncOpenAI] = PrivateAttr()
    _http_client: Optional[httpx.Client] = PrivateAttr()

    def __init__(
        self,
        model: str = DEFAULT_OPENAI_MODEL,
        temperature: float = DEFAULT_TEMPERATURE,
        max_tokens: Optional[int] = None,
        additional_kwargs: Optional[Dict[str, Any]] = None,
        max_retries: int = 3,
        timeout: float = 60.0,
        reuse_client: bool = True,
        api_key: Optional[str] = None,
        api_base: Optional[str] = None,
        api_version: Optional[str] = None,
        callback_manager: Optional[CallbackManager] = None,
        default_headers: Optional[Dict[str, str]] = None,
        http_client: Optional[httpx.Client] = None,
        # base class
        system_prompt: Optional[str] = None,
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
        **kwargs: Any,
    ) -> None:
        additional_kwargs = additional_kwargs or {}

        api_key, api_base, api_version = resolve_openai_credentials(
            api_key=api_key,
            api_base=api_base,
            api_version=api_version,
        )

        super().__init__(
            model=model,
            temperature=temperature,
            max_tokens=max_tokens,
            additional_kwargs=additional_kwargs,
            max_retries=max_retries,
            callback_manager=callback_manager,
            api_key=api_key,
            api_version=api_version,
            api_base=api_base,
            timeout=timeout,
            reuse_client=reuse_client,
            default_headers=default_headers,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
            **kwargs,
        )

        self._client = None
        self._aclient = None
        self._http_client = http_client

    def _get_client(self) -> SyncOpenAI:
        if not self.reuse_client:
            return SyncOpenAI(**self._get_credential_kwargs())

        if self._client is None:
            self._client = SyncOpenAI(**self._get_credential_kwargs())
        return self._client

    def _get_aclient(self) -> AsyncOpenAI:
        if not self.reuse_client:
            return AsyncOpenAI(**self._get_credential_kwargs())

        if self._aclient is None:
            self._aclient = AsyncOpenAI(**self._get_credential_kwargs())
        return self._aclient

    def _get_model_name(self) -> str:
        model_name = self.model
        if "ft-" in model_name:  # legacy fine-tuning
            model_name = model_name.split(":")[0]
        elif model_name.startswith("ft:"):
            model_name = model_name.split(":")[1]
        return model_name

    def _is_azure_client(self) -> bool:
        return isinstance(self._get_client(), AzureOpenAI)

    @classmethod
    def class_name(cls) -> str:
        return "openai_llm"

    @property
    def _tokenizer(self) -> Optional[Tokenizer]:
        """
        Get a tokenizer for this model, or None if a tokenizing method is unknown.

        OpenAI can do this using the tiktoken package, subclasses may not have
        this convenience.
        """
        return tiktoken.encoding_for_model(self._get_model_name())

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=openai_modelname_to_contextsize(self._get_model_name()),
            num_output=self.max_tokens or -1,
            is_chat_model=is_chat_model(model=self._get_model_name()),
            is_function_calling_model=is_function_calling_model(
                model=self._get_model_name()
            ),
            model_name=self.model,
        )

By setting max_tokens, you can control the length of the generated responses. Additionally, you can handle retries and timeouts to manage the rate of queries effectively. Implementing a rate-limiting mechanism will help you stay within the allowed number of requests per minute [1].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 17 hours ago

What is this log say ?

Batches: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.70it/s]

I still think it is sending request too fast

From: dosubot[bot] @.> Date: Saturday, July 6, 2024 at 10:32 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To handle the "context length exceeded" error from OpenAI, you need to reduce the length of the messages or the completion. Here are some steps you can take:

  1. Set max_tokens: Limit the number of tokens generated in each response.
  2. Reduce Message Length: Truncate or summarize the input messages to reduce their length.
  3. Implement Retry Logic: Use the max_retries parameter to handle transient errors by retrying the request.
  4. Adjust Timeout: Set an appropriate timeout to avoid long waits for responses.
  5. Rate Limiting: Implement a rate-limiting mechanism to ensure you do not exceed the allowed number of requests per minute.

Here is an example of how you can configure these settings in the OpenAI class:

class OpenAI(LLM):

model: str = Field(

    default=DEFAULT_OPENAI_MODEL, description="The OpenAI model to use."

)

temperature: float = Field(

    default=DEFAULT_TEMPERATURE,

    description="The temperature to use during generation.",

    gte=0.0,

    lte=1.0,

)

max_tokens: Optional[int] = Field(

    description="The maximum number of tokens to generate.",

    gt=0,

)

additional_kwargs: Dict[str, Any] = Field(

    default_factory=dict, description="Additional kwargs for the OpenAI API."

)

max_retries: int = Field(

    default=3,

    description="The maximum number of API retries.",

    gte=0,

)

timeout: float = Field(

    default=60.0,

    description="The timeout, in seconds, for API requests.",

    gte=0,

)

default_headers: Dict[str, str] = Field(

    default=None, description="The default headers for API requests."

)

reuse_client: bool = Field(

    default=True,

    description=(

        "Reuse the OpenAI client between requests. When doing anything with large "

        "volumes of async API calls, setting this to false can improve stability."

    ),

)

api_key: str = Field(default=None, description="The OpenAI API key.", exclude=True)

api_base: str = Field(description="The base URL for OpenAI API.")

api_version: str = Field(description="The API version for OpenAI API.")

_client: Optional[SyncOpenAI] = PrivateAttr()

_aclient: Optional[AsyncOpenAI] = PrivateAttr()

_http_client: Optional[httpx.Client] = PrivateAttr()

def __init__(

    self,

    model: str = DEFAULT_OPENAI_MODEL,

    temperature: float = DEFAULT_TEMPERATURE,

    max_tokens: Optional[int] = None,

    additional_kwargs: Optional[Dict[str, Any]] = None,

    max_retries: int = 3,

    timeout: float = 60.0,

    reuse_client: bool = True,

    api_key: Optional[str] = None,

    api_base: Optional[str] = None,

    api_version: Optional[str] = None,

    callback_manager: Optional[CallbackManager] = None,

    default_headers: Optional[Dict[str, str]] = None,

    http_client: Optional[httpx.Client] = None,

    # base class

    system_prompt: Optional[str] = None,

    messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,

    completion_to_prompt: Optional[Callable[[str], str]] = None,

    pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,

    output_parser: Optional[BaseOutputParser] = None,

    **kwargs: Any,

) -> None:

    additional_kwargs = additional_kwargs or {}

    api_key, api_base, api_version = resolve_openai_credentials(

        api_key=api_key,

        api_base=api_base,

        api_version=api_version,

    )

    super().__init__(

        model=model,

        temperature=temperature,

        max_tokens=max_tokens,

        additional_kwargs=additional_kwargs,

        max_retries=max_retries,

        callback_manager=callback_manager,

        api_key=api_key,

        api_version=api_version,

        api_base=api_base,

        timeout=timeout,

        reuse_client=reuse_client,

        default_headers=default_headers,

        system_prompt=system_prompt,

        messages_to_prompt=messages_to_prompt,

        completion_to_prompt=completion_to_prompt,

        pydantic_program_mode=pydantic_program_mode,

        output_parser=output_parser,

        **kwargs,

    )

    self._client = None

    self._aclient = None

    self._http_client = http_client

def _get_client(self) -> SyncOpenAI:

    if not self.reuse_client:

        return SyncOpenAI(**self._get_credential_kwargs())

    if self._client is None:

        self._client = SyncOpenAI(**self._get_credential_kwargs())

    return self._client

def _get_aclient(self) -> AsyncOpenAI:

    if not self.reuse_client:

        return AsyncOpenAI(**self._get_credential_kwargs())

    if self._aclient is None:

        self._aclient = AsyncOpenAI(**self._get_credential_kwargs())

    return self._aclient

def _get_model_name(self) -> str:

    model_name = self.model

    if "ft-" in model_name:  # legacy fine-tuning

        model_name = model_name.split(":")[0]

    elif model_name.startswith("ft:"):

        model_name = model_name.split(":")[1]

    return model_name

def _is_azure_client(self) -> bool:

    return isinstance(self._get_client(), AzureOpenAI)

@classmethod

def class_name(cls) -> str:

    return "openai_llm"

@property

def _tokenizer(self) -> Optional[Tokenizer]:

    """

    Get a tokenizer for this model, or None if a tokenizing method is unknown.

    OpenAI can do this using the tiktoken package, subclasses may not have

    this convenience.

    """

    return tiktoken.encoding_for_model(self._get_model_name())

@property

def metadata(self) -> LLMMetadata:

    return LLMMetadata(

        context_window=openai_modelname_to_contextsize(self._get_model_name()),

        num_output=self.max_tokens or -1,

        is_chat_model=is_chat_model(model=self._get_model_name()),

        is_function_calling_model=is_function_calling_model(

            model=self._get_model_name()

        ),

        model_name=self.model,

    )

By setting max_tokens, you can control the length of the generated responses. Additionally, you can handle retries and timeouts to manage the rate of queries effectively. Implementing a rate-limiting mechanism will help you stay within the allowed number of requests per minute [1]https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/llms/openai.py.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2211957552, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXEK2CVYEFHYMYWLGLDZLBH33AVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJRHE2TONJVGI. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 17 hours ago

The log message Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.70it/s] indicates that a batch process has completed successfully, processing one batch at a rate of 3.70 iterations per second. This suggests that the process is running quite fast.

To ensure that your code sends requests at a slower rate and handles the "context length exceeded" error effectively, you can implement rate limiting and context length checks. Here is an example in Python:

Rate Limiting and Context Length Handling

  1. Rate Limiting: Use the time.sleep function to introduce delays between requests.
  2. Context Length Handling: Check the length of the context before sending the request and truncate if necessary.

Here is a sample code snippet:

import time

# Function to send a request
def send_request(data):
    # Simulate sending a request
    print(f"Sending request with data: {data}")

# Function to handle context length
def handle_context_length(context, max_length):
    if len(context) > max_length:
        print(f"Context length exceeded: {len(context)} > {max_length}")
        context = context[:max_length]
    return context

# Example usage
max_context_length = 4096
requests = ["short request", "a" * 5000, "another short request"]

for request in requests:
    # Handle context length
    request = handle_context_length(request, max_context_length)

    # Send the request
    send_request(request)

    # Rate limiting: wait for 1 second before sending the next request
    time.sleep(1)

In this example:

Additionally, if you are using LlamaIndex, note that it does not have built-in protection for rate limits, but it does have some retry mechanisms for unstable connections. This might be something to consider adding, especially for services like OpenAI [1][2].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 17 hours ago

Can you check the whole code, where this should be inserted:

import time import threading from functools import wraps

Rate limiting decorator

def rate_limit(*, allowed_per_second: int): max_period = 1.0 / allowed_per_second last_call = [time.perf_counter()] lock = threading.Lock()

def decorate(func):
    @wraps(func)
    def limit(*args, **kwargs):
        with lock:
            elapsed = time.perf_counter() - last_call[0]
            hold = max_period - elapsed
            if hold > 0:
                time.sleep(hold)
            result = func(*args, **kwargs)
            last_call[0] = time.perf_counter()
        return result

    return limit

return decorate

class LimitedRequestRouterQueryEngine: def init(self, query_engine, request_limit): self.query_engine = query_engine self.request_limit = request_limit self.request_count = 0

@rate_limit(allowed_per_second=0.0000001)  # Adjust the rate as needed
def query(self, query):
    if self.request_count >= self.request_limit:
        print("Request limit reached. No more requests will be sent.")
        return None
    self.request_count += 1
    return self.query_engine.query(query)

limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=3)

response = rate_limited_query(query_engine, "summarize the xyz configuration steps")

From: dosubot[bot] @.> Date: Sunday, July 7, 2024 at 6:48 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

The log message Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.70it/s] indicates that a batch process has completed successfully, processing one batch at a rate of 3.70 iterations per second. This suggests that the process is running quite fast.

To ensure that your code sends requests at a slower rate and handles the "context length exceeded" error effectively, you can implement rate limiting and context length checks. Here is an example in Python:

Rate Limiting and Context Length Handling

  1. Rate Limiting: Use the time.sleep function to introduce delays between requests.
  2. Context Length Handling: Check the length of the context before sending the request and truncate if necessary.

Here is a sample code snippet:

import time

Function to send a request

def send_request(data):

# Simulate sending a request

print(f"Sending request with data: {data}")

Function to handle context length

def handle_context_length(context, max_length):

if len(context) > max_length:

    print(f"Context length exceeded: {len(context)} > {max_length}")

    context = context[:max_length]

return context

Example usage

max_context_length = 4096

requests = ["short request", "a" * 5000, "another short request"]

for request in requests:

# Handle context length

request = handle_context_length(request, max_context_length)

# Send the request

send_request(request)

# Rate limiting: wait for 1 second before sending the next request

time.sleep(1)

In this example:

Additionally, if you are using LlamaIndex, note that it does not have built-in protection for rate limits, but it does have some retry mechanisms for unstable connections. This might be something to consider adding, especially for services like OpenAI [1]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_05_25_23.json[2]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_06_02_23.json.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2212506020, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXGTL5A6OINY7MCUXFLZLFWN7AVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJSGUYDMMBSGA. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 17 hours ago

To integrate the rate limiting and context length handling into your existing code, you need to ensure that the LimitedRequestRouterQueryEngine class is correctly instantiated and used. Here is the complete code with the necessary modifications:

import time
import threading
from functools import wraps

# Rate limiting decorator
def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

# Function to handle context length
def handle_context_length(context, max_length):
    if len(context) > max_length:
        print(f"Context length exceeded: {len(context)} > {max_length}")
        context = context[:max_length]
    return context

class LimitedRequestRouterQueryEngine:
    def __init__(self, query_engine, request_limit, max_context_length):
        self.query_engine = query_engine
        self.request_limit = request_limit
        self.request_count = 0
        self.max_context_length = max_context_length

    @rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed
    def query(self, query):
        if self.request_count >= self.request_limit:
            print("Request limit reached. No more requests will be sent.")
            return None
        self.request_count += 1
        query = handle_context_length(query, self.max_context_length)
        return self.query_engine.query(query)

# Example usage
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.tools import QueryEngineTool

# Define your query engines
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

list_query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize",
    use_async=True,
)
vector_query_engine = vector_index.as_query_engine()

list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    description="Useful for summarization questions related to 8k on its documentations",
)

vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description="Useful for retrieving specific context from 8k on its documentations.",
)

query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[list_tool, vector_tool],
)

# Instantiate the LimitedRequestRouterQueryEngine with a request limit and max context length
limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=3, max_context_length=4096)

# Use the limited query engine
response = limited_query_engine.query("summarize the xyz configuration steps")
print(str(response))

Key Points:

  1. Rate Limiting: The rate_limit decorator ensures that the query method does not exceed the specified rate limit.
  2. Context Length Handling: The handle_context_length function checks if the context length exceeds the maximum allowed length and truncates it if necessary.
  3. Request Limiting: The LimitedRequestRouterQueryEngine class wraps the RouterQueryEngine and adds a request counter to limit the number of requests.

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and avoid hitting the rate limit [1][2].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 16 hours ago

I think we should implement something like this:

from openai.error import RateLimitError import backoff

@backoff.on_exception(backoff.expo, RateLimitError) def completions_with_backoff(kwargs): response = openai.Completion.create(kwargs) return response

All of the code before, did not help

From: dosubot[bot] @.> Date: Sunday, July 7, 2024 at 7:05 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To integrate the rate limiting and context length handling into your existing code, you need to ensure that the LimitedRequestRouterQueryEngine class is correctly instantiated and used. Here is the complete code with the necessary modifications:

import time

import threading

from functools import wraps

Rate limiting decorator

def rate_limit(*, allowed_per_second: int):

max_period = 1.0 / allowed_per_second

last_call = [time.perf_counter()]

lock = threading.Lock()

def decorate(func):

    @wraps(func)

    def limit(*args, **kwargs):

        with lock:

            elapsed = time.perf_counter() - last_call[0]

            hold = max_period - elapsed

            if hold > 0:

                time.sleep(hold)

            result = func(*args, **kwargs)

            last_call[0] = time.perf_counter()

        return result

    return limit

return decorate

Function to handle context length

def handle_context_length(context, max_length):

if len(context) > max_length:

    print(f"Context length exceeded: {len(context)} > {max_length}")

    context = context[:max_length]

return context

class LimitedRequestRouterQueryEngine:

def __init__(self, query_engine, request_limit, max_context_length):

    self.query_engine = query_engine

    self.request_limit = request_limit

    self.request_count = 0

    self.max_context_length = max_context_length

@rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed

def query(self, query):

    if self.request_count >= self.request_limit:

        print("Request limit reached. No more requests will be sent.")

        return None

    self.request_count += 1

    query = handle_context_length(query, self.max_context_length)

    return self.query_engine.query(query)

Example usage

from llama_index.core.query_engine import RouterQueryEngine

from llama_index.core.selectors import PydanticSingleSelector

from llama_index.core.tools import QueryEngineTool

Define your query engines

summary_index = SummaryIndex(nodes, storage_context=storage_context)

vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

list_query_engine = summary_index.as_query_engine(

response_mode="tree_summarize",

use_async=True,

)

vector_query_engine = vector_index.as_query_engine()

list_tool = QueryEngineTool.from_defaults(

query_engine=list_query_engine,

description="Useful for summarization questions related to 8k on its documentations",

)

vector_tool = QueryEngineTool.from_defaults(

query_engine=vector_query_engine,

description="Useful for retrieving specific context from 8k on its documentations.",

)

query_engine = RouterQueryEngine(

selector=PydanticSingleSelector.from_defaults(),

query_engine_tools=[list_tool, vector_tool],

)

Instantiate the LimitedRequestRouterQueryEngine with a request limit and max context length

limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=3, max_context_length=4096)

Use the limited query engine

response = limited_query_engine.query("summarize the xyz configuration steps")

print(str(response))

Key Points:

  1. Rate Limiting: The rate_limit decorator ensures that the query method does not exceed the specified rate limit.
  2. Context Length Handling: The handle_context_length function checks if the context length exceeds the maximum allowed length and truncates it if necessary.
  3. Request Limiting: The LimitedRequestRouterQueryEngine class wraps the RouterQueryEngine and adds a request counter to limit the number of requests.

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and avoid hitting the rate limit [1]https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/readers/llama-index-readers-stackoverflow/llama_index/readers/stackoverflow/base.py[2]https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-friendli/llama_index/llms/friendli/utils.py.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2212510541, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXHQGPIVFCAP4JXVNVLZLFYMRAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJSGUYTANJUGE. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 15 hours ago

To implement a backoff strategy using the backoff library for handling rate limit errors in your existing code, you can modify the LimitedRequestRouterQueryEngine class to include the backoff mechanism. Here is the updated code:

import time
import threading
from functools import wraps
import backoff
from openai.error import RateLimitError

# Rate limiting decorator
def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

# Function to handle context length
def handle_context_length(context, max_length):
    if len(context) > max_length:
        print(f"Context length exceeded: {len(context)} > {max_length}")
        context = context[:max_length]
    return context

class LimitedRequestRouterQueryEngine:
    def __init__(self, query_engine, request_limit, max_context_length):
        self.query_engine = query_engine
        self.request_limit = request_limit
        self.request_count = 0
        self.max_context_length = max_context_length

    @rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed
    @backoff.on_exception(backoff.expo, RateLimitError, max_tries=5)
    def query(self, query):
        if self.request_count >= self.request_limit:
            print("Request limit reached. No more requests will be sent.")
            return None
        self.request_count += 1
        query = handle_context_length(query, self.max_context_length)
        return self.query_engine.query(query)

# Example usage
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.tools import QueryEngineTool

# Define your query engines
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

list_query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize",
    use_async=True,
)
vector_query_engine = vector_index.as_query_engine()

list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    description="Useful for summarization questions related to 8k on its documentations",
)

vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description="Useful for retrieving specific context from 8k on its documentations.",
)

query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[list_tool, vector_tool],
)

# Instantiate the LimitedRequestRouterQueryEngine with a request limit and max context length
limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=3, max_context_length=4096)

# Use the limited query engine
response = limited_query_engine.query("summarize the xyz configuration steps")
print(str(response))

Key Points:

  1. Rate Limiting: The rate_limit decorator ensures that the query method does not exceed the specified rate limit.
  2. Context Length Handling: The handle_context_length function checks if the context length exceeds the maximum allowed length and truncates it if necessary.
  3. Request Limiting: The LimitedRequestRouterQueryEngine class wraps the RouterQueryEngine and adds a request counter to limit the number of requests.
  4. Backoff Strategy: The backoff.on_exception decorator retries the request with exponential backoff when a RateLimitError is encountered.

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and handle rate limit errors gracefully [1][2].

To continue talking to Dosu, mention @dosu.

gmdl007 commented 14 hours ago

What if I am using the different embedding model?

use Huggingface embeddings

from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.core import Settings

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

embed_model = HuggingFaceEmbedding(model_name="jinaai/jina-embeddings-v2-base-en")

Settings.embed_model=embed_model

From: dosubot[bot] @.> Date: Friday, July 5, 2024 at 3:22 PM To: run-llama/llama_index @.> Cc: gmdl007 @.>, Author @.> Subject: Re: [run-llama/llama_index] [Question]: how to slowdown the summary_index query (Issue #14551)

To stop your code from sending too many requests to the Azure OpenAI server when using summary_index and implement a limit on the number of requests, you can introduce a delay between requests to ensure you stay within the rate limits. Additionally, you can use the GPTSimpleVectorIndex.from_documents() method to manage the rate of embedding creation. Here is an example of how you can do this in Python:

import time

import openai

Function to create embeddings with rate limiting

def create_embeddings_with_rate_limit(documents, rate_limit_per_minute):

interval = 60 / rate_limit_per_minute  # Calculate the interval between requests

embeddings = []

for doc in documents:

    try:

        # Create embedding for the document

        embedding = openai.Embedding.create(input=doc, model="text-embedding-ada-002")

        embeddings.append(embedding)

        # Wait for the interval to respect the rate limit

        time.sleep(interval)

    except openai.error.RateLimitError as e:

        print(f"Rate limit error: {e}")

        # Optionally, you can implement a backoff strategy here

        time.sleep(interval)

return embeddings

Example usage

documents = ["Document 1", "Document 2", "Document 3"]

rate_limit_per_minute = 60 # Set your rate limit here

embeddings = create_embeddings_with_rate_limit(documents, rate_limit_per_minute)

This code ensures that you do not exceed the rate limit by introducing a delay between each request. Adjust the rate_limit_per_minute variable to match your specific rate limit.

Additionally, if you are using GPTSimpleVectorIndex.from_documents() and encountering rate limit issues, you can manage the rate of requests by implementing a similar delay mechanism or by batching your requests to stay within the allowed limits. If you continue to face issues, consider reaching out to OpenAI support or upgrading your pricing tier to increase your rate limits [1]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_05_25_23.json[2]https://github.com/run-llama/llama_index/blob/main/docs/docs/examples/discover_llamaindex/document_management/discord_dumps/help_channel_dump_06_02_23.json.

To continue talking to Dosuhttps://dosu.dev, mention @dosuhttps://github.com/dosu.

— Reply to this email directly, view it on GitHubhttps://github.com/run-llama/llama_index/issues/14551#issuecomment-2210871617, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AG2FHXGCCSLTTZFBBBZCJTTZK2M2NAVCNFSM6AAAAABKKHCLW2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEMJQHA3TCNRRG4. You are receiving this because you authored the thread.Message ID: @.***>

dosubot[bot] commented 14 hours ago

When using different embedding models like HuggingFace embeddings, you can still implement rate limiting and backoff strategies to manage the rate of requests and handle rate limit errors. Here is how you can integrate these mechanisms into your existing code:

Updated Code with Rate Limiting and Backoff for HuggingFace Embeddings

import time
import threading
from functools import wraps
import backoff
from requests.exceptions import HTTPError

# Rate limiting decorator
def rate_limit(*, allowed_per_second: int):
    max_period = 1.0 / allowed_per_second
    last_call = [time.perf_counter()]
    lock = threading.Lock()

    def decorate(func):
        @wraps(func)
        def limit(*args, **kwargs):
            with lock:
                elapsed = time.perf_counter() - last_call[0]
                hold = max_period - elapsed
                if hold > 0:
                    time.sleep(hold)
                result = func(*args, **kwargs)
                last_call[0] = time.perf_counter()
            return result

        return limit

    return decorate

# Function to handle context length
def handle_context_length(context, max_length):
    if len(context) > max_length:
        print(f"Context length exceeded: {len(context)} > {max_length}")
        context = context[:max_length]
    return context

class LimitedRequestRouterQueryEngine:
    def __init__(self, query_engine, request_limit, max_context_length):
        self.query_engine = query_engine
        self.request_limit = request_limit
        self.request_count = 0
        self.max_context_length = max_context_length

    @rate_limit(allowed_per_second=0.1)  # Adjust the rate as needed
    @backoff.on_exception(backoff.expo, HTTPError, max_tries=5)
    def query(self, query):
        if self.request_count >= self.request_limit:
            print("Request limit reached. No more requests will be sent.")
            return None
        self.request_count += 1
        query = handle_context_length(query, self.max_context_length)
        return self.query_engine.query(query)

# Example usage with HuggingFace embeddings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.tools import QueryEngineTool

# Use HuggingFace embeddings
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.embed_model = embed_model

# Define your query engines
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

list_query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize",
    use_async=True,
)
vector_query_engine = vector_index.as_query_engine()

list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    description="Useful for summarization questions related to 8k on its documentations",
)

vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description="Useful for retrieving specific context from 8k on its documentations.",
)

query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[list_tool, vector_tool],
)

# Instantiate the LimitedRequestRouterQueryEngine with a request limit and max context length
limited_query_engine = LimitedRequestRouterQueryEngine(query_engine, request_limit=3, max_context_length=4096)

# Use the limited query engine
response = limited_query_engine.query("summarize the xyz configuration steps")
print(str(response))

Key Points:

  1. Rate Limiting: The rate_limit decorator ensures that the query method does not exceed the specified rate limit.
  2. Context Length Handling: The handle_context_length function checks if the context length exceeds the maximum allowed length and truncates it if necessary.
  3. Request Limiting: The LimitedRequestRouterQueryEngine class wraps the RouterQueryEngine and adds a request counter to limit the number of requests.
  4. Backoff Strategy: The backoff.on_exception decorator retries the request with exponential backoff when a RateLimitError or HTTPError is encountered.

By combining these mechanisms, you can effectively manage the rate and number of queries to the RouterQueryEngine and handle rate limit errors gracefully, even when using different embedding models like HuggingFace embeddings [1][2].

To continue talking to Dosu, mention @dosu.