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docs add examples with litellm proxy
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1 changed files with 136 additions and 3 deletions
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@ -270,7 +270,45 @@ response = await litellm.acompletion(
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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<TabItem value="proxy" label="LiteLLM Proxy">
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```python
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import openai
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client = openai.AsyncOpenAI(
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api_key="anything", # litellm proxy api key
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base_url="http://0.0.0.0:4000" # litellm proxy base url
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)
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response = await client.chat.completions.create(
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model="anthropic/claude-3-5-sonnet-20240620",
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messages=[
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{
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": "You are an AI assistant tasked with analyzing legal documents.",
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},
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{
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"type": "text",
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"text": "Here is the full text of a complex legal agreement",
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"cache_control": {"type": "ephemeral"},
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},
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],
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},
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{
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"role": "user",
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"content": "what are the key terms and conditions in this agreement?",
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},
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],
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extra_headers={
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "prompt-caching-2024-07-31",
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},
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)
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```
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</TabItem>
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</Tabs>
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@ -318,7 +356,45 @@ response = await litellm.acompletion(
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)
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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<TabItem value="proxy" label="LiteLLM Proxy">
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```python
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import openai
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client = openai.AsyncOpenAI(
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api_key="anything", # litellm proxy api key
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base_url="http://0.0.0.0:4000" # litellm proxy base url
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)
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response = await client.chat.completions.create(
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model="anthropic/claude-3-5-sonnet-20240620",
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messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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"cache_control": {"type": "ephemeral"}
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},
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}
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],
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extra_headers={
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "prompt-caching-2024-07-31",
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},
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)
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```
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</TabItem>
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</Tabs>
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@ -387,7 +463,64 @@ response = await litellm.acompletion(
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)
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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<TabItem value="proxy" label="LiteLLM Proxy">
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```python
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import openai
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client = openai.AsyncOpenAI(
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api_key="anything", # litellm proxy api key
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base_url="http://0.0.0.0:4000" # litellm proxy base url
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)
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response = await client.chat.completions.create(
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model="anthropic/claude-3-5-sonnet-20240620",
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messages=[
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# System Message
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{
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": "Here is the full text of a complex legal agreement"
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* 400,
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"cache_control": {"type": "ephemeral"},
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}
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],
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},
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# marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What are the key terms and conditions in this agreement?",
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"cache_control": {"type": "ephemeral"},
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}
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],
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},
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{
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"role": "assistant",
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"content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo",
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},
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# The final turn is marked with cache-control, for continuing in followups.
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What are the key terms and conditions in this agreement?",
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"cache_control": {"type": "ephemeral"},
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}
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],
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},
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],
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extra_headers={
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"anthropic-version": "2023-06-01",
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"anthropic-beta": "prompt-caching-2024-07-31",
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},
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)
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```
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</TabItem>
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</Tabs>
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