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<main>
<article id="content">
<header>
<h1 class="title">Module <code>selection.api</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="selection.api.APIQuery"><code class="flex name class">
<span>class <span class="ident">APIQuery</span></span>
<span>(</span><span>model, timeout=30, temperature=0, max_tokens=256, return_logprobs=False, api='openai', chat=True, max_retries=20, requests_per_second=30, check_every_n_seconds=0.1, read_cost=None, write_cost=None, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Initializes an instance of the API class.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>model</code></strong> : <code>str</code></dt>
<dd>The model to query.</dd>
<dt><strong><code>timeout</code></strong> : <code>int</code>, optional</dt>
<dd>The timeout value in seconds. Defaults to 30.</dd>
<dt><strong><code>temperature</code></strong> : <code>int</code>, optional</dt>
<dd>The temperature value. Defaults to 0.</dd>
<dt><strong><code>max_tokens</code></strong> : <code>int</code>, optional</dt>
<dd>The maximum number of tokens. Defaults to 256.</dd>
<dt><strong><code>return_logprobs</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to return log probabilities. Defaults to False.</dd>
<dt><strong><code>api</code></strong> : <code>str</code>, optional</dt>
<dd>The API to be used, one of "openai", "together", "huggingface", "gemini", "claude". Defaults to 'openai'.</dd>
<dt><strong><code>chat</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to enable chat mode. Defaults to True.</dd>
<dt><strong><code>max_retries</code></strong> : <code>int</code>, optional</dt>
<dd>The maximum number of retries. Defaults to 20.</dd>
<dt><strong><code>requests_per_second</code></strong> : <code>int</code>, optional</dt>
<dd>The number of requests per second. Defaults to 30.</dd>
<dt><strong><code>check_every_n_seconds</code></strong> : <code>float</code>, optional</dt>
<dd>The interval for checking rate limits. Defaults to 0.1.</dd>
<dt><strong><code>read_cost</code></strong> : <code>float</code>, optional</dt>
<dd>The cost of read operations. Defaults to None.</dd>
<dt><strong><code>write_cost</code></strong> : <code>float</code>, optional</dt>
<dd>The cost of write operations. Defaults to None.</dd>
<dt><strong><code>**kwargs</code></strong></dt>
<dd>Additional keyword arguments for the API model.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>None</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class APIQuery:
def __init__(self, model,
timeout=30,
temperature=0,
max_tokens=256,
return_logprobs=False,
api='openai',
chat=True,
max_retries=20,
requests_per_second=30,
check_every_n_seconds=0.1,
read_cost=None,
write_cost=None,
**kwargs):
"""
Initializes an instance of the API class.
Args:
model (str): The model to query.
timeout (int, optional): The timeout value in seconds. Defaults to 30.
temperature (int, optional): The temperature value. Defaults to 0.
max_tokens (int, optional): The maximum number of tokens. Defaults to 256.
return_logprobs (bool, optional): Whether to return log probabilities. Defaults to False.
api (str, optional): The API to be used, one of "openai", "together", "huggingface", "gemini", "claude". Defaults to 'openai'.
chat (bool, optional): Whether to enable chat mode. Defaults to True.
max_retries (int, optional): The maximum number of retries. Defaults to 20.
requests_per_second (int, optional): The number of requests per second. Defaults to 30.
check_every_n_seconds (float, optional): The interval for checking rate limits. Defaults to 0.1.
read_cost (float, optional): The cost of read operations. Defaults to None.
write_cost (float, optional): The cost of write operations. Defaults to None.
**kwargs: Additional keyword arguments for the API model.
Returns:
None
"""
self.model = model
self.return_logprobs = return_logprobs
self.temperature = temperature
self.max_tokens = max_tokens
self.kwargs = kwargs
if read_cost is None:
self.read_cost = 1
self.write_cost = 1
else:
self.read_cost = read_cost
self.write_cost = write_cost
self.max_retries = max_retries
self.api = api
self.chat = chat
self.timeout = timeout
self.rate_limiter = InMemoryRateLimiter(requests_per_second=requests_per_second,
check_every_n_seconds=check_every_n_seconds,
max_bucket_size=requests_per_second)
self.initialize_api_model()
def initialize_api_model(self):
"""
Initializes the API model based on the specified API and chat settings.
Raises:
ValueError: If the specified API or chat settings are not supported.
Returns:
None
"""
if self.api == 'openai' and self.chat:
self.api_model = ChatOpenAI(model=self.model, max_retries=self.max_retries,
temperature=self.temperature,
timeout=self.timeout, max_tokens=self.max_tokens,
rate_limiter=self.rate_limiter,
api_key=os.getenv('OPENAI_API_KEY'), seed=0,
stream_usage=True, **self.kwargs)
elif self.api == 'openai' and not self.chat:
self.api_model = OpenAICompletion(model=self.model, temperature=self.temperature,
max_tokens=self.max_tokens,
api_key=os.getenv('OPENAI_API_KEY'), seed=0,
**self.kwargs)
elif self.api == 'anthropic' and self.chat:
self.api_model = ChatAnthropic(model_name=self.model, temperature=self.temperature,
timeout=self.timeout, max_tokens=self.max_tokens,
api_key=os.getenv('ANTHROPIC_API_KEY'),
stream_usage=True, **self.kwargs)
elif self.api == 'anthropic' and not self.chat:
self.api_model = AnthropicLLMCompletion(model_name=self.model, temperature=self.temperature,
max_tokens=self.max_tokens, **self.kwargs)
elif self.api == 'together' and self.chat:
self.api_model = ChatTogether(model=self.model, temperature=self.temperature,
timeout=self.timeout, max_tokens=self.max_tokens,
api_key=os.getenv('TOGETHER_API_KEY'),
**self.kwargs)
elif self.api == 'together' and not self.chat:
self.api_model = TogetherLLMCompletion(model=self.model, temperature=self.temperature,
max_tokens=self.max_tokens, **self.kwargs)
elif self.api == 'google' and self.chat:
self.api_model = ChatGoogleGenerativeAI(model=self.model, temperature=self.temperature,
timeout=self.timeout, max_tokens=self.max_tokens,
api_key=os.getenv('GOOGLE_API_KEY'),
stream_usage=True, **self.kwargs)
else:
raise ValueError(f'API {self.api} not supported or chat {self.chat} not supported')
async def run_queries(self, queries):
"""
Run queries against the API model.
Args:
queries (list): A list of queries to be executed.
If chat is enabled, each query is a list of tuples, where each tuple contains ('system', 'ai', 'human') and the query message.
If chat is disabled, each query is a string.
Returns:
tuple: A tuple containing the outputs of the queries, the detailed cost, and the total cost.
Total cost is a dictionary containing the input tokens, output tokens, and the total cost.
Detailed cost is a list of dictionaries containing the same information for each query.
Raises:
ValueError: If the query type is not supported.
"""
retry_api_model = self.api_model.with_retry(stop_after_attempt=self.max_retries)
if self.chat:
queries_converted = []
for query in queries:
current_query = []
for query_type, query_message in query:
if query_type == 'system':
current_query.append(SystemMessage(content=query_message))
elif query_type == 'ai':
current_query.append(AIMessage(content=query_message))
elif query_type == 'human':
current_query.append(HumanMessage(content=query_message))
else:
raise ValueError(f'Query type {query_type} not supported')
queries_converted.append(current_query)
else:
queries_converted = queries
results = await self._run_with_rate_limiting(retry_api_model.abatch,
queries_converted)
results = self.unify_output_format(results)
cost, detailed_cost = self.get_cost(results)
outputs = [result['content'] for result in results]
if self.return_logprobs:
logprob_info = self.get_logprobs(results)
outputs = [(result['content'], logprob)
for result, logprob in zip(results, logprob_info)]
return outputs, detailed_cost, cost
def unify_output_format(self, results):
"""
Unifies the output format of the given results across all APIs.
Args:
results (list): A list of results.
Returns:
list: A list of unified results.
"""
unified_results = []
for result in results:
if not isinstance(result, dict):
result = dict(result)
if 'generation_info' in result:
result = result['generation_info']
unified_results.append(result)
return unified_results
def get_logprobs(self, results):
"""
Retrieves the log probabilities from the given results.
Parameters:
results (list): A list of results.
Returns:
list: A nested list containing the log probabilities for each result.
Raises:
None
"""
if self.api == 'huggingface':
logprob_info = [
[[(key, val) for key, val in result['logprobs'].items()]] for result in results
]
if self.api == 'together':
logprob_info = []
for result in results:
logprob_info.append((
result['response_metadata']['logprobs']['tokens'],
result['response_metadata']['logprobs']['token_logprobs']
))
logprob_info = [
[[(token, logprob)] for token, logprob in zip(result[0], result[1])]
for result in logprob_info
]
elif self.api == 'openai':
logprob_info = []
for result in results:
result_specific_logprob = []
for token_logprobs in result['response_metadata']['logprobs']['content']:
top_logprobs_token = []
for token in token_logprobs['top_logprobs']:
top_logprobs_token.append((token['token'], token['logprob']))
result_specific_logprob.append(top_logprobs_token)
logprob_info.append(result_specific_logprob)
return logprob_info
async def _run_with_rate_limiting(self, func, queries):
"""
Runs the given function with rate limiting.
Args:
func (callable): The function to be executed.
queries (list): The list of queries to be processed.
Returns:
list: The results of the function execution.
"""
results = []
for i in tqdm(range(0, len(queries), self.rate_limiter.max_bucket_size),
desc='Running queries'):
batch = queries[i:i + self.rate_limiter.max_bucket_size]
while not self.rate_limiter.acquire():
continue
results.extend(await func(batch))
return results
def get_cost(self, results):
"""
Calculates the cost of the given results.
Args:
results (list): A list of results.
Returns:
tuple: A tuple containing the total input tokens, total output tokens, total cost, and detailed cost for each result.
"""
input_tokens = 0
output_tokens = 0
detailed_cost = []
for result in results:
input_tokens += result['usage_metadata']['input_tokens']
output_tokens += result['usage_metadata']['output_tokens']
detailed = result['usage_metadata']
detailed['cost'] = detailed['input_tokens'] * self.read_cost / 10 ** 6
detailed['cost'] += detailed['output_tokens'] * self.write_cost / 10 ** 6
detailed_cost.append(detailed)
return {
'input_tokens': input_tokens,
'output_tokens': output_tokens,
'cost': input_tokens * self.read_cost / 10 ** 6 + output_tokens * self.write_cost / 10 ** 6
}, detailed_cost</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="selection.api.APIQuery.get_cost"><code class="name flex">
<span>def <span class="ident">get_cost</span></span>(<span>self, results)</span>
</code></dt>
<dd>
<div class="desc"><p>Calculates the cost of the given results.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>results</code></strong> : <code>list</code></dt>
<dd>A list of results.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>tuple</code></dt>
<dd>A tuple containing the total input tokens, total output tokens, total cost, and detailed cost for each result.</dd>
</dl></div>
</dd>
<dt id="selection.api.APIQuery.get_logprobs"><code class="name flex">
<span>def <span class="ident">get_logprobs</span></span>(<span>self, results)</span>
</code></dt>
<dd>
<div class="desc"><p>Retrieves the log probabilities from the given results.</p>
<h2 id="parameters">Parameters</h2>
<p>results (list): A list of results.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>list</code></dt>
<dd>A nested list containing the log probabilities for each result.</dd>
</dl>
<h2 id="raises">Raises</h2>
<p>None</p></div>
</dd>
<dt id="selection.api.APIQuery.initialize_api_model"><code class="name flex">
<span>def <span class="ident">initialize_api_model</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Initializes the API model based on the specified API and chat settings.</p>
<h2 id="raises">Raises</h2>
<dl>
<dt><code>ValueError</code></dt>
<dd>If the specified API or chat settings are not supported.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>None</p></div>
</dd>
<dt id="selection.api.APIQuery.run_queries"><code class="name flex">
<span>async def <span class="ident">run_queries</span></span>(<span>self, queries)</span>
</code></dt>
<dd>
<div class="desc"><p>Run queries against the API model.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>queries</code></strong> : <code>list</code></dt>
<dd>A list of queries to be executed.
If chat is enabled, each query is a list of tuples, where each tuple contains ('system', 'ai', 'human') and the query message.
If chat is disabled, each query is a string.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>tuple</code></dt>
<dd>A tuple containing the outputs of the queries, the detailed cost, and the total cost.</dd>
</dl>
<p>Total cost is a dictionary containing the input tokens, output tokens, and the total cost.
Detailed cost is a list of dictionaries containing the same information for each query.</p>
<h2 id="raises">Raises</h2>
<dl>
<dt><code>ValueError</code></dt>
<dd>If the query type is not supported.</dd>
</dl></div>
</dd>
<dt id="selection.api.APIQuery.unify_output_format"><code class="name flex">
<span>def <span class="ident">unify_output_format</span></span>(<span>self, results)</span>
</code></dt>
<dd>
<div class="desc"><p>Unifies the output format of the given results across all APIs.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>results</code></strong> : <code>list</code></dt>
<dd>A list of results.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>list</code></dt>
<dd>A list of unified results.</dd>
</dl></div>
</dd>
</dl>
</dd>
</dl>
</section>
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<li><h3>Super-module</h3>
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<li><code><a title="selection" href="index.html">selection</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<h4><code><a title="selection.api.APIQuery" href="#selection.api.APIQuery">APIQuery</a></code></h4>
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<li><code><a title="selection.api.APIQuery.get_cost" href="#selection.api.APIQuery.get_cost">get_cost</a></code></li>
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<li><code><a title="selection.api.APIQuery.unify_output_format" href="#selection.api.APIQuery.unify_output_format">unify_output_format</a></code></li>
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