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ai21[patch]: Update API reference documentation (#25302)
Issue: https://github.com/langchain-ai/langchain/issues/24856
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@ -16,9 +16,17 @@ class AI21Base(BaseModel):
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client: Any = Field(default=None, exclude=True) #: :meta private:
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api_key: Optional[SecretStr] = None
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"""API key for AI21 API."""
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api_host: Optional[str] = None
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"""Host URL"""
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timeout_sec: Optional[float] = None
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"""Timeout in seconds.
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If not set, it will default to the value of the environment
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variable `AI21_TIMEOUT_SEC` or 300 seconds.
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"""
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num_retries: Optional[int] = None
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"""Maximum number of retries for API requests before giving up."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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@ -15,18 +15,64 @@ def _split_texts_into_batches(texts: List[str], batch_size: int) -> Iterator[Lis
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class AI21Embeddings(Embeddings, AI21Base):
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"""AI21 embedding model.
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"""AI21 embedding model integration.
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To use, you should have the 'AI21_API_KEY' environment variable set
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or pass as a named parameter to the constructor.
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Install ``langchain_ai21`` and set environment variable ``AI21_API_KEY``.
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Example:
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.. code-block:: bash
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pip install -U langchain_ai21
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export AI21_API_KEY="your-api-key"
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Key init args — client params:
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api_key: Optional[SecretStr]
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batch_size: int
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The number of texts that will be sent to the API in each batch.
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Use larger batch sizes if working with many short texts. This will reduce
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the number of API calls made, and can improve the time it takes to embed
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a large number of texts.
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num_retries: Optional[int]
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Maximum number of retries for API requests before giving up.
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timeout_sec: Optional[float]
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Timeout in seconds for API requests. If not set, it will default to the
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value of the environment variable `AI21_TIMEOUT_SEC` or 300 seconds.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_ai21 import AI21Embeddings
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embeddings = AI21Embeddings()
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query_result = embeddings.embed_query("Hello embeddings world!")
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embed = AI21Embeddings(
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# api_key="...",
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# batch_size=128,
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)
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Embed single text:
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.. code-block:: python
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input_text = "The meaning of life is 42"
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vector = embed.embed_query(input_text)
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print(vector[:3])
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.. code-block:: python
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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Embed multiple texts:
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.. code-block:: python
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input_texts = ["Document 1...", "Document 2..."]
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vectors = embed.embed_documents(input_texts)
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print(len(vectors))
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# The first 3 coordinates for the first vector
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print(vectors[0][:3])
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.. code-block:: python
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2
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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"""
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batch_size: int = _DEFAULT_BATCH_SIZE
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