forked from phoenix/litellm-mirror
37 lines
1.8 KiB
Markdown
37 lines
1.8 KiB
Markdown
import Image from '@theme/IdealImage';
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Life of a Request
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## High Level architecture
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<Image img={require('../../img/litellm_gateway.png')} />
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### Request Flow
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1. **User Sends Request**: The process begins when a user sends a request to the LiteLLM Proxy Server (Gateway).
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2. [**Virtual Keys**](../virtual_keys): At this stage the `Bearer` token in the request is checked to ensure it is valid and under it's budget
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3. **Rate Limiting**: The [MaxParallelRequestsHandler](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py) checks the **rate limit (rpm/tpm)** for the the following components:
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- Global Server Rate Limit
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- Virtual Key Rate Limit
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- User Rate Limit
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- Team Limit
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4. **LiteLLM `proxy_server.py`**: Contains the `/chat/completions` and `/embeddings` endpoints. Requests to these endpoints are sent through the LiteLLM Router
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5. [**LiteLLM Router**](../routing): The LiteLLM Router handles Load balancing, Fallbacks, Retries for LLM API deployments.
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6. [**litellm.completion() / litellm.embedding()**:](../index#litellm-python-sdk) The litellm Python SDK is used to call the LLM in the OpenAI API format (Translation and parameter mapping)
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7. **Post-Request Processing**: After the response is sent back to the client, the following **asynchronous** tasks are performed:
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- [Logging to LangFuse (logging destination is configurable)](./logging)
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- The [MaxParallelRequestsHandler](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py) updates the rpm/tpm usage for the
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- Global Server Rate Limit
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- Virtual Key Rate Limit
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- User Rate Limit
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- Team Limit
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- The `_PROXY_track_cost_callback` updates spend / usage in the LiteLLM database.
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