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Merge pull request #4896 from BerriAI/docs_add_example_usage_with_mistral_python

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Docs Proxy - add example usage with mistral SDK with Proxy
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Ishaan Jaff 2024-07-25 20:02:26 -07:00 • committed by GitHub
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165
docs/my-website/docs/proxy/quick_start.md
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@ -255,6 +255,12 @@ litellm --config your_config.yaml
## Using LiteLLM Proxy - Curl Request, OpenAI Package, Langchain ## Using LiteLLM Proxy - Curl Request, OpenAI Package, Langchain
:::info
LiteLLM is compatible with several SDKs - including OpenAI SDK, Anthropic SDK, Mistral SDK, LLamaIndex, Langchain (Js, Python)
[More examples here](user_keys)
:::
<Tabs> <Tabs>
<TabItem value="Curl" label="Curl Request"> <TabItem value="Curl" label="Curl Request">
@ -396,165 +402,6 @@ print(response)
- POST `/key/generate` - generate a key to access the proxy - POST `/key/generate` - generate a key to access the proxy
## Using with OpenAI compatible projects
Set `base_url` to the LiteLLM Proxy server
<Tabs>
<TabItem value="openai" label="OpenAI v1.0.0+">
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
```
</TabItem>
<TabItem value="librechat" label="LibreChat">
#### Start the LiteLLM proxy
```shell
litellm --model gpt-3.5-turbo
#INFO: Proxy running on http://0.0.0.0:4000
```
#### 1. Clone the repo
```shell
git clone https://github.com/danny-avila/LibreChat.git
```
#### 2. Modify Librechat's `docker-compose.yml`
LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below
```yaml
OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions
```
#### 3. Save fake OpenAI key in Librechat's `.env`
Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key).
```env
OPENAI_API_KEY=sk-1234
```
#### 4. Run LibreChat:
```shell
docker compose up
```
</TabItem>
<TabItem value="continue-dev" label="ContinueDev">
Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart).
In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model.
```python
default=OpenAI(
api_key="IGNORED",
model="fake-model-name",
context_length=2048, # customize if needed for your model
api_base="http://localhost:4000" # your proxy server url
),
```
Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial.
</TabItem>
<TabItem value="aider" label="Aider">
```shell
$ pip install aider
$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key
```
</TabItem>
<TabItem value="autogen" label="AutoGen">
```python
pip install pyautogen
```
```python
from autogen import AssistantAgent, UserProxyAgent, oai
config_list=[
{
"model": "my-fake-model",
"api_base": "http://localhost:4000", #litellm compatible endpoint
"api_type": "open_ai",
"api_key": "NULL", # just a placeholder
}
]
response = oai.Completion.create(config_list=config_list, prompt="Hi")
print(response) # works fine
llm_config={
"config_list": config_list,
}
assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list)
```
Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial.
</TabItem>
<TabItem value="guidance" label="guidance">
A guidance language for controlling large language models.
https://github.com/guidance-ai/guidance
**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it.
**Fix**: Start your proxy using the `--drop_params` flag
```shell
litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params
```
```python
import guidance
# set api_base to your proxy
# set api_key to anything
gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything")
experts = guidance('''
{{#system~}}
You are a helpful and terse assistant.
{{~/system}}
{{#user~}}
I want a response to the following question:
{{query}}
Name 3 world-class experts (past or present) who would be great at answering this?
Don't answer the question yet.
{{~/user}}
{{#assistant~}}
{{gen 'expert_names' temperature=0 max_tokens=300}}
{{~/assistant}}
''', llm=gpt4)
result = experts(query='How can I be more productive?')
print(result)
```
</TabItem>
</Tabs>
## Debugging Proxy ## Debugging Proxy
Events that occur during normal operation Events that occur during normal operation

182
docs/my-website/docs/proxy/user_keys.md
View file

@ -1,7 +1,7 @@
import Tabs from '@theme/Tabs'; import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem'; import TabItem from '@theme/TabItem';
# Use with Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl # 💡 Use with Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl
:::info :::info
@ -234,6 +234,26 @@ main();
``` ```
</TabItem> </TabItem>
<TabItem value="mistral-py" label="Mistral Python SDK">
```python
import os
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
client = MistralClient(api_key="sk-1234", endpoint="http://0.0.0.0:4000")
chat_response = client.chat(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "this is a test request, write a short poem"}
],
)
print(chat_response.choices[0].message.content)
```
</TabItem>
<TabItem value="instructor" label="Instructor"> <TabItem value="instructor" label="Instructor">
```python ```python
@ -566,6 +586,166 @@ curl --location 'http://0.0.0.0:4000/moderations' \
``` ```
## Using with OpenAI compatible projects
Set `base_url` to the LiteLLM Proxy server
<Tabs>
<TabItem value="openai" label="OpenAI v1.0.0+">
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
```
</TabItem>
<TabItem value="librechat" label="LibreChat">
#### Start the LiteLLM proxy
```shell
litellm --model gpt-3.5-turbo
#INFO: Proxy running on http://0.0.0.0:4000
```
#### 1. Clone the repo
```shell
git clone https://github.com/danny-avila/LibreChat.git
```
#### 2. Modify Librechat's `docker-compose.yml`
LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below
```yaml
OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions
```
#### 3. Save fake OpenAI key in Librechat's `.env`
Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key).
```env
OPENAI_API_KEY=sk-1234
```
#### 4. Run LibreChat:
```shell
docker compose up
```
</TabItem>
<TabItem value="continue-dev" label="ContinueDev">
Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart).
In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model.
```python
default=OpenAI(
api_key="IGNORED",
model="fake-model-name",
context_length=2048, # customize if needed for your model
api_base="http://localhost:4000" # your proxy server url
),
```
Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial.
</TabItem>
<TabItem value="aider" label="Aider">
```shell
$ pip install aider
$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key
```
</TabItem>
<TabItem value="autogen" label="AutoGen">
```python
pip install pyautogen
```
```python
from autogen import AssistantAgent, UserProxyAgent, oai
config_list=[
{
"model": "my-fake-model",
"api_base": "http://localhost:4000", #litellm compatible endpoint
"api_type": "open_ai",
"api_key": "NULL", # just a placeholder
}
]
response = oai.Completion.create(config_list=config_list, prompt="Hi")
print(response) # works fine
llm_config={
"config_list": config_list,
}
assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list)
```
Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial.
</TabItem>
<TabItem value="guidance" label="guidance">
A guidance language for controlling large language models.
https://github.com/guidance-ai/guidance
**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it.
**Fix**: Start your proxy using the `--drop_params` flag
```shell
litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params
```
```python
import guidance
# set api_base to your proxy
# set api_key to anything
gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything")
experts = guidance('''
{{#system~}}
You are a helpful and terse assistant.
{{~/system}}
{{#user~}}
I want a response to the following question:
{{query}}
Name 3 world-class experts (past or present) who would be great at answering this?
Don't answer the question yet.
{{~/user}}
{{#assistant~}}
{{gen 'expert_names' temperature=0 max_tokens=300}}
{{~/assistant}}
''', llm=gpt4)
result = experts(query='How can I be more productive?')
print(result)
```
</TabItem>
</Tabs>
## Advanced ## Advanced
### (BETA) Batch Completions - pass multiple models ### (BETA) Batch Completions - pass multiple models

4
litellm/proxy/proxy_config.yaml
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@ -8,6 +8,10 @@ model_list:
litellm_params: litellm_params:
model: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct model: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct
api_key: "os.environ/FIREWORKS" api_key: "os.environ/FIREWORKS"
- model_name: mistral-small-latest
litellm_params:
model: mistral/mistral-small-latest
api_key: "os.environ/MISTRAL_API_KEY"
- model_name: tts - model_name: tts
litellm_params: litellm_params:
model: openai/tts-1 model: openai/tts-1

13
litellm/proxy/tests/test_mistral_sdk.py Normal file
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@ -0,0 +1,13 @@
import os
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
client = MistralClient(api_key="sk-1234", endpoint="http://0.0.0.0:4000")
chat_response = client.chat(
model="mistral-small-latest",
messages=[
{"role": "user", "content": "this is a test request, write a short poem"}
],
)
print(chat_response.choices[0].message.content)