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ishaan-jaff 2024-02-09 09:42:35 -08:00
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77
docs/my-website/docs/index.md
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@ -5,10 +5,14 @@ import TabItem from '@theme/TabItem';
https://github.com/BerriAI/litellm https://github.com/BerriAI/litellm
import QuickStart from '../src/components/QuickStart.js'
## **Call 100+ LLMs using the same Input/Output Format** ## **Call 100+ LLMs using the same Input/Output Format**
- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
- Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
## Basic usage ## Basic usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
@ -157,9 +161,6 @@ response = completion(
messages=[{ "content": "Hello, how are you?","role": "user"}], messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -177,9 +178,6 @@ response = completion(
messages=[{ "content": "Hello, how are you?","role": "user"}], messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -199,9 +197,6 @@ response = completion(
messages=[{ "content": "Hello, how are you?","role": "user"}], messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -222,9 +217,7 @@ response = completion(
stream=True, stream=True,
) )
print(response)
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -246,9 +239,6 @@ response = completion(
messages = [{ "content": "Hello, how are you?","role": "user"}], messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -265,9 +255,6 @@ response = completion(
api_base="http://localhost:11434", api_base="http://localhost:11434",
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
<TabItem value="or" label="Openrouter"> <TabItem value="or" label="Openrouter">
@ -284,9 +271,6 @@ response = completion(
messages = [{ "content": "Hello, how are you?","role": "user"}], messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True, stream=True,
) )
for chunk in response:
print(chunk)
``` ```
</TabItem> </TabItem>
@ -327,34 +311,8 @@ litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langf
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
``` ```
## Calculate Costs, Usage, Latency ## Track Costs, Usage, Latency for streaming
Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost
```python
from litellm import completion, completion_cost
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
```
**Output**
```shell
Cost for completion call with gpt-3.5-turbo: $0.0000775000
```
### Track Costs, Usage, Latency for streaming
We use a custom callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
- We define a callback function to calculate cost `def track_cost_callback()`
- In `def track_cost_callback()` we check if the stream is complete - `if "complete_streaming_response" in kwargs`
- Use `litellm.completion_cost()` to calculate cost, once the stream is complete
```python ```python
import litellm import litellm
@ -366,18 +324,8 @@ def track_cost_callback(
start_time, end_time # start/end time start_time, end_time # start/end time
): ):
try: try:
# check if it has collected an entire stream response response_cost = kwargs.get("response_cost", 0)
if "complete_streaming_response" in kwargs: print("streaming response_cost", response_cost)
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
except: except:
pass pass
# set callback # set callback
@ -400,6 +348,8 @@ response = completion(
Track spend across multiple projects/people Track spend across multiple projects/people
![ui_3](https://github.com/BerriAI/litellm/assets/29436595/47c97d5e-b9be-4839-b28c-43d7f4f10033)
The proxy provides: The proxy provides:
1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth) 1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class) 2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
@ -436,8 +386,7 @@ response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
print(response) print(response)
``` ```
## More details ## More details
* [exception mapping](./exception_mapping.md) * [exception mapping](./exception_mapping.md)
* [retries + model fallbacks for completion()](./completion/reliable_completions.md) * [retries + model fallbacks for completion()](./completion/reliable_completions.md)
* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) * [proxy virtual keys & spend management](./tutorials/fallbacks.md)

51
docs/my-website/src/pages/index.md
View file

@ -8,6 +8,11 @@ https://github.com/BerriAI/litellm
## **Call 100+ LLMs using the same Input/Output Format** ## **Call 100+ LLMs using the same Input/Output Format**
- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
- Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
## Basic usage ## Basic usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb"> <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
@ -306,30 +311,7 @@ litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langf
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
``` ```
## Calculate Costs, Usage, Latency ## Track Costs, Usage, Latency for streaming
Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost
```python
from litellm import completion, completion_cost
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
```
**Output**
```shell
Cost for completion call with gpt-3.5-turbo: $0.0000775000
```
### Track Costs, Usage, Latency for streaming
Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
```python ```python
@ -342,18 +324,8 @@ def track_cost_callback(
start_time, end_time # start/end time start_time, end_time # start/end time
): ):
try: try:
# check if it has collected an entire stream response response_cost = kwargs.get("response_cost", 0)
if "complete_streaming_response" in kwargs: print("streaming response_cost", response_cost)
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
except: except:
pass pass
# set callback # set callback
@ -372,13 +344,12 @@ response = completion(
) )
``` ```
Need a dedicated key? Email us @ krrish@berri.ai
## OpenAI Proxy ## OpenAI Proxy
Track spend across multiple projects/people Track spend across multiple projects/people
![ui_3](https://github.com/BerriAI/litellm/assets/29436595/47c97d5e-b9be-4839-b28c-43d7f4f10033)
The proxy provides: The proxy provides:
1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth) 1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class) 2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
@ -418,4 +389,4 @@ print(response)
## More details ## More details
* [exception mapping](./exception_mapping.md) * [exception mapping](./exception_mapping.md)
* [retries + model fallbacks for completion()](./completion/reliable_completions.md) * [retries + model fallbacks for completion()](./completion/reliable_completions.md)
* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) * [proxy virtual keys & spend management](./tutorials/fallbacks.md)