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<main>
<article id="content">
<header>
<h1 class="title">Module <code>selection.completion_llms</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.completion_llms.AnthropicLLMCompletion"><code class="flex name class">
<span>class <span class="ident">AnthropicLLMCompletion</span></span>
<span>(</span><span>*args: Any, **kwargs: Any)</span>
</code></dt>
<dd>
<div class="desc"><p>Anthropic large language model.</p>
<p>To use, you should have the environment variable <code>ANTHROPIC_API_KEY</code>
set with your API key, or pass it as a named parameter to the constructor.</p>
<h2 id="example">Example</h2>
<p>.. code-block:: python</p>
<pre><code>from langchain_anthropic import AnthropicLLM
model = AnthropicLLM()
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class AnthropicLLMCompletion(AnthropicLLM):
async def _acall(self, prompt, stop=None, run_manager=None, **kwargs) -> str:
"""Call out to Anthropic's completion endpoint asynchronously."""
if self.streaming:
completion = ""
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
completion += chunk.text
return completion
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
response = await self.async_client.completions.create(
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**params,
)
return {
'content': response.completion,
'usage_metadata': {
'input_tokens': self.count_tokens(prompt),
'output_tokens': self.count_tokens(response.completion),
}
}</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>langchain_anthropic.llms.AnthropicLLM</li>
<li>langchain_core.language_models.llms.LLM</li>
<li>langchain_core.language_models.llms.BaseLLM</li>
<li>langchain_anthropic.llms._AnthropicCommon</li>
<li>langchain_core.language_models.base.BaseLanguageModel</li>
<li>langchain_core.runnables.base.RunnableSerializable</li>
<li>langchain_core.load.serializable.Serializable</li>
<li>pydantic.v1.main.BaseModel</li>
<li>pydantic.v1.utils.Representation</li>
<li>langchain_core.runnables.base.Runnable</li>
<li>typing.Generic</li>
<li>abc.ABC</li>
</ul>
</dd>
<dt id="selection.completion_llms.OpenAICompletion"><code class="flex name class">
<span>class <span class="ident">OpenAICompletion</span></span>
<span>(</span><span>*args: Any, **kwargs: Any)</span>
</code></dt>
<dd>
<div class="desc"><p>OpenAI completion model integration.</p>
<h2 id="setup">Setup</h2>
<p>Install <code>langchain-openai</code> and set environment variable <code>OPENAI_API_KEY</code>.</p>
<p>.. code-block:: bash</p>
<pre><code>pip install -U langchain-openai
export OPENAI_API_KEY="your-api-key"
</code></pre>
<p>Key init args — completion params:
model: str
Name of OpenAI model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
stream_options: Dict
Configure streaming outputs, like whether to return token usage when
streaming (<code>{"include_usage": True}</code>).</p>
<p>Key init args — client params:
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY.
base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service
emulator.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.</p>
<p>See full list of supported init args and their descriptions in the params section.</p>
<h2 id="instantiate">Instantiate</h2>
<p>.. code-block:: python</p>
<pre><code>from langchain_openai import OpenAI
llm = OpenAI(
model="gpt-3.5-turbo-instruct",
temperature=0,
max_retries=2,
# api_key="...",
# base_url="...",
# organization="...",
# other params...
)
</code></pre>
<h2 id="invoke">Invoke</h2>
<p>.. code-block:: python</p>
<pre><code>input_text = "The meaning of life is "
llm.invoke(input_text)
</code></pre>
<p>.. code-block:: none</p>
<pre><code>"a philosophical question that has been debated by thinkers and scholars for centuries."
</code></pre>
<h2 id="stream">Stream</h2>
<p>.. code-block:: python</p>
<pre><code>for chunk in llm.stream(input_text):
print(chunk, end="|")
</code></pre>
<p>.. code-block:: none</p>
<pre><code>a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.
</code></pre>
<p>.. code-block:: python</p>
<pre><code>"".join(llm.stream(input_text))
</code></pre>
<p>.. code-block:: none</p>
<pre><code>"a philosophical question that has been debated by thinkers and scholars for centuries."
</code></pre>
<h2 id="async">Async</h2>
<p>.. code-block:: python</p>
<pre><code>await llm.ainvoke(input_text)
# stream:
# async for chunk in (await llm.astream(input_text)):
# print(chunk)
# batch:
# await llm.abatch([input_text])
</code></pre>
<p>.. code-block:: none</p>
<pre><code>"a philosophical question that has been debated by thinkers and scholars for centuries."
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class OpenAICompletion(OpenAI):
async def abatch(self, inputs, config=None, *, return_exceptions=False, **kwargs):
if not inputs:
return []
config = get_config_list(config, len(inputs))
max_concurrency = config[0].get("max_concurrency")
if max_concurrency is None:
try:
llm_result = await self.agenerate_prompt(
[self._convert_input(input) for input in inputs],
callbacks=[c.get("callbacks") for c in config],
tags=[c.get("tags") for c in config],
metadata=[c.get("metadata") for c in config],
run_name=[c.get("run_name") for c in config],
**kwargs,
)
outputs = [g[0].text for g in llm_result.generations]
token_usage = [
{
"input_tokens": llm_result.llm_output['token_usage']['prompt_tokens'] / len(outputs),
"output_tokens": llm_result.llm_output['token_usage']['completion_tokens'] / len(outputs),
} for _ in outputs
]
return [
{
"content": output,
"usage_metadata": token_usage[i],
}
for i, output in enumerate(outputs)
]
except Exception as e:
if return_exceptions:
return cast(List[str], [e for _ in inputs])
else:
raise e
else:
batches = [
inputs[i : i + max_concurrency]
for i in range(0, len(inputs), max_concurrency)
]
config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc]
return [
output
for i, batch in enumerate(batches)
for output in await self.abatch(
batch,
config=config[i * max_concurrency : (i + 1) * max_concurrency],
return_exceptions=return_exceptions,
**kwargs,
)
]</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>langchain_openai.llms.base.OpenAI</li>
<li>langchain_openai.llms.base.BaseOpenAI</li>
<li>langchain_core.language_models.llms.BaseLLM</li>
<li>langchain_core.language_models.base.BaseLanguageModel</li>
<li>langchain_core.runnables.base.RunnableSerializable</li>
<li>langchain_core.load.serializable.Serializable</li>
<li>pydantic.v1.main.BaseModel</li>
<li>pydantic.v1.utils.Representation</li>
<li>langchain_core.runnables.base.Runnable</li>
<li>typing.Generic</li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="selection.completion_llms.OpenAICompletion.abatch"><code class="name flex">
<span>async def <span class="ident">abatch</span></span>(<span>self, inputs, config=None, *, return_exceptions=False, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Default implementation runs ainvoke in parallel using asyncio.gather.</p>
<p>The default implementation of batch works well for IO bound runnables.</p>
<p>Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>inputs</code></strong></dt>
<dd>A list of inputs to the Runnable.</dd>
<dt><strong><code>config</code></strong></dt>
<dd>A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None.</dd>
<dt><strong><code>return_exceptions</code></strong></dt>
<dd>Whether to return exceptions instead of raising them.
Defaults to False.</dd>
<dt><strong><code>kwargs</code></strong></dt>
<dd>Additional keyword arguments to pass to the Runnable.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>A list of outputs from the Runnable.</p></div>
</dd>
</dl>
</dd>
<dt id="selection.completion_llms.TogetherLLMCompletion"><code class="flex name class">
<span>class <span class="ident">TogetherLLMCompletion</span></span>
<span>(</span><span>*args: Any, **kwargs: Any)</span>
</code></dt>
<dd>
<div class="desc"><p>LLM models from <code>Together</code>.</p>
<p>To use, you'll need an API key which you can find here:
<a href="https://api.together.ai/settings/api-keys.">https://api.together.ai/settings/api-keys.</a> This can be passed in as init param
<code>together_api_key</code> or set as environment variable <code>TOGETHER_API_KEY</code>.</p>
<p>Together AI API reference: <a href="https://docs.together.ai/reference/completions">https://docs.together.ai/reference/completions</a></p>
<h2 id="example">Example</h2>
<p>.. code-block:: python</p>
<pre><code>from langchain_together import Together
model = Together(model_name="mistralai/Mixtral-8x7B-Instruct-v0.1")
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TogetherLLMCompletion(Together):
echo : bool = False
@property
def default_params(self) -> Dict[str, Any]:
"""Return the default parameters for the Together model.
Returns:
A dictionary containing the default parameters.
"""
return {
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"max_tokens": self.max_tokens,
"repetition_penalty": self.repetition_penalty,
"logprobs": self.logprobs,
"echo": self.echo
}
async def _acall(self, prompt, stop=None, run_manager=None, **kwargs) -> str:
"""Call Together model to get predictions based on the prompt.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
run_manager: The CallbackManager for LLM run, it's not used at the moment.
**kwargs: Additional parameters to pass to the model.
Returns:
The string generated by the model.
"""
headers = {
"Authorization": f"Bearer {self.together_api_key.get_secret_value()}",
"Content-Type": "application/json",
}
stop_to_use = stop[0] if stop and len(stop) == 1 else stop
payload = {
**self.default_params,
"prompt": prompt,
"stop": stop_to_use,
**kwargs,
}
# filter None values to not pass them to the http payload
payload = {k: v for k, v in payload.items() if v is not None}
async with ClientSession() as session:
async with session.post(
self.base_url, json=payload, headers=headers
) as response:
if response.status >= 500:
raise Exception(f"Together Server: Error {response.status}")
elif response.status >= 400:
raise ValueError(
f"Together received an invalid payload: {response.text}"
)
elif response.status != 200:
raise Exception(
f"Together returned an unexpected response with status "
f"{response.status}: {response.text}"
)
response_json = await response.json()
output = self._format_output(response_json)
input_tokens = response_json['usage']['prompt_tokens']
output_tokens = response_json['usage']['completion_tokens']
logprobs = None
if len(response_json['prompt']) > 0:
logprobs = response_json['prompt'][0].get('logprobs')
return {
'content': output,
'usage_metadata': {
'input_tokens': input_tokens,
'output_tokens': output_tokens
},
'response_metadata': {
'logprobs': logprobs
}
}
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager=None,
**kwargs: Any,
) -> LLMResult:
"""Async run the LLM on the given prompt and input."""
generations = []
new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")
for prompt in prompts:
text = (
await self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs)
if new_arg_supported
else await self._acall(prompt, stop=stop, **kwargs)
)
generations.append([Generation(text=text['content'], generation_info=text)])
return LLMResult(generations=generations)
async def abatch(
self,
inputs,
config=None,
*,
return_exceptions: bool = False,
**kwargs: Any,
) -> List[str]:
if not inputs:
return []
config = get_config_list(config, len(inputs))
max_concurrency = config[0].get("max_concurrency")
if max_concurrency is None:
try:
llm_result = await self.agenerate_prompt(
[self._convert_input(input) for input in inputs],
callbacks=[c.get("callbacks") for c in config],
tags=[c.get("tags") for c in config],
metadata=[c.get("metadata") for c in config],
run_name=[c.get("run_name") for c in config],
**kwargs,
)
return [g[0] for g in llm_result.generations]
except Exception as e:
if return_exceptions:
return cast(List[str], [e for _ in inputs])
else:
raise e
else:
batches = [
inputs[i : i + max_concurrency]
for i in range(0, len(inputs), max_concurrency)
]
config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc]
return [
output
for i, batch in enumerate(batches)
for output in await self.abatch(
batch,
config=config[i * max_concurrency : (i + 1) * max_concurrency],
return_exceptions=return_exceptions,
**kwargs,
)
]</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>langchain_together.llms.Together</li>
<li>langchain_core.language_models.llms.LLM</li>
<li>langchain_core.language_models.llms.BaseLLM</li>
<li>langchain_core.language_models.base.BaseLanguageModel</li>
<li>langchain_core.runnables.base.RunnableSerializable</li>
<li>langchain_core.load.serializable.Serializable</li>
<li>pydantic.v1.main.BaseModel</li>
<li>pydantic.v1.utils.Representation</li>
<li>langchain_core.runnables.base.Runnable</li>
<li>typing.Generic</li>
<li>abc.ABC</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="selection.completion_llms.TogetherLLMCompletion.echo"><code class="name">var <span class="ident">echo</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Instance variables</h3>
<dl>
<dt id="selection.completion_llms.TogetherLLMCompletion.default_params"><code class="name">prop <span class="ident">default_params</span> : Dict[str, Any]</code></dt>
<dd>
<div class="desc"><p>Return the default parameters for the Together model.</p>
<h2 id="returns">Returns</h2>
<p>A dictionary containing the default parameters.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def default_params(self) -> Dict[str, Any]:
"""Return the default parameters for the Together model.
Returns:
A dictionary containing the default parameters.
"""
return {
"model": self.model,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"max_tokens": self.max_tokens,
"repetition_penalty": self.repetition_penalty,
"logprobs": self.logprobs,
"echo": self.echo
}</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="selection.completion_llms.TogetherLLMCompletion.abatch"><code class="name flex">
<span>async def <span class="ident">abatch</span></span>(<span>self, inputs, config=None, *, return_exceptions: bool = False, **kwargs: Any) ‑> List[str]</span>
</code></dt>
<dd>
<div class="desc"><p>Default implementation runs ainvoke in parallel using asyncio.gather.</p>
<p>The default implementation of batch works well for IO bound runnables.</p>
<p>Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying Runnable uses an API which supports a batch mode.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>inputs</code></strong></dt>
<dd>A list of inputs to the Runnable.</dd>
<dt><strong><code>config</code></strong></dt>
<dd>A config to use when invoking the Runnable.
The config supports standard keys like 'tags', 'metadata' for tracing
purposes, 'max_concurrency' for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details. Defaults to None.</dd>
<dt><strong><code>return_exceptions</code></strong></dt>
<dd>Whether to return exceptions instead of raising them.
Defaults to False.</dd>
<dt><strong><code>kwargs</code></strong></dt>
<dd>Additional keyword arguments to pass to the Runnable.</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>A list of outputs from the Runnable.</p></div>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="selection" href="index.html">selection</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="selection.completion_llms.AnthropicLLMCompletion" href="#selection.completion_llms.AnthropicLLMCompletion">AnthropicLLMCompletion</a></code></h4>
</li>
<li>
<h4><code><a title="selection.completion_llms.OpenAICompletion" href="#selection.completion_llms.OpenAICompletion">OpenAICompletion</a></code></h4>
<ul class="">
<li><code><a title="selection.completion_llms.OpenAICompletion.abatch" href="#selection.completion_llms.OpenAICompletion.abatch">abatch</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="selection.completion_llms.TogetherLLMCompletion" href="#selection.completion_llms.TogetherLLMCompletion">TogetherLLMCompletion</a></code></h4>
<ul class="">
<li><code><a title="selection.completion_llms.TogetherLLMCompletion.abatch" href="#selection.completion_llms.TogetherLLMCompletion.abatch">abatch</a></code></li>
<li><code><a title="selection.completion_llms.TogetherLLMCompletion.default_params" href="#selection.completion_llms.TogetherLLMCompletion.default_params">default_params</a></code></li>
<li><code><a title="selection.completion_llms.TogetherLLMCompletion.echo" href="#selection.completion_llms.TogetherLLMCompletion.echo">echo</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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