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Small bug fixes (#23353)
Small bug fixes according to your comments --------- Signed-off-by: Joffref <mariusjoffre@gmail.com> Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Baskar Gopinath <73015364+baskargopinath@users.noreply.github.com> Co-authored-by: Chester Curme <chester.curme@gmail.com> Co-authored-by: Mathis Joffre <51022808+Joffref@users.noreply.github.com> Co-authored-by: Baur <baur.krykpayev@gmail.com> Co-authored-by: Nuradil <nuradil.maksut@icloud.com> Co-authored-by: Nuradil <133880216+yaksh0nti@users.noreply.github.com> Co-authored-by: Jacob Lee <jacoblee93@gmail.com> Co-authored-by: Rave Harpaz <rave.harpaz@oracle.com> Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com> Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: RUO <61719257+comsa33@users.noreply.github.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Luis Rueda <userlerueda@gmail.com> Co-authored-by: Jib <Jibzade@gmail.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: S M Zia Ur Rashid <smziaurrashid@gmail.com> Co-authored-by: Ikko Eltociear Ashimine <eltociear@gmail.com> Co-authored-by: yuncliu <lyc1990@qq.com> Co-authored-by: wenngong <76683249+wenngong@users.noreply.github.com> Co-authored-by: gongwn1 <gongwn1@lenovo.com> Co-authored-by: Mirna Wong <89008547+mirnawong1@users.noreply.github.com> Co-authored-by: Rahul Triptahi <rahul.psit.ec@gmail.com> Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com> Co-authored-by: maang-h <55082429+maang-h@users.noreply.github.com> Co-authored-by: asafg <asafg@ai21.com> Co-authored-by: Asaf Joseph Gardin <39553475+Josephasafg@users.noreply.github.com>
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joshc-ai21
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@ -18,7 +18,9 @@
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"# ChatAI21\n",
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"\n",
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"This notebook covers how to get started with AI21 chat models.\n",
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"\n",
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"Note that different chat models support different parameters. See the ",
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"[AI21 documentation](https://docs.ai21.com/reference) to learn more about the parameters in your chosen model.\n",
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"[See all AI21's LangChain components.](https://pypi.org/project/langchain-ai21/) \n",
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"## Installation"
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]
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},
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@ -44,7 +46,8 @@
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"source": [
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"## Environment Setup\n",
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"\n",
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"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
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"We'll need to get an [AI21 API key](https://docs.ai21.com/) and set the ",
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"`AI21_API_KEY` environment variable:\n"
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]
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},
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{
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@ -17,7 +17,9 @@
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"source": [
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"# AI21LLM\n",
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"\n",
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"This example goes over how to use LangChain to interact with `AI21` models.\n",
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"This example goes over how to use LangChain to interact with `AI21` Jurassic models. To use the Jamba model, use the [ChatAI21 object](https://python.langchain.com/v0.2/docs/integrations/chat/ai21/) instead.\n",
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"\n",
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"[See a full list of AI21 models and tools on LangChain.](https://pypi.org/project/langchain-ai21/)\n",
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"\n",
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"## Installation"
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]
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@ -1,6 +1,6 @@
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# langchain-ai21
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This package contains the LangChain integrations for [AI21](https://docs.ai21.com/) through their [AI21](https://pypi.org/project/ai21/) SDK.
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This package contains the LangChain integrations for [AI21](https://docs.ai21.com/) models and tools.
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## Installation and Setup
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@ -13,9 +13,10 @@ pip install langchain-ai21
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## Chat Models
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This package contains the `ChatAI21` class, which is the recommended way to interface with AI21 Chat models.
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This package contains the `ChatAI21` class, which is the recommended way to interface with AI21 chat models, including Jamba-Instruct
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and any Jurassic chat models.
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To use, install the requirements, and configure your environment.
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To use, install the requirements and configure your environment.
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```bash
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export AI21_API_KEY=your-api-key
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@ -27,7 +28,7 @@ Then initialize
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from langchain_core.messages import HumanMessage
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from langchain_ai21.chat_models import ChatAI21
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chat = ChatAI21(model="jamab-instruct")
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chat = ChatAI21(model="jamba-instruct-preview")
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messages = [HumanMessage(content="Hello from AI21")]
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chat.invoke(messages)
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```
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@ -35,10 +36,12 @@ chat.invoke(messages)
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For a list of the supported models, see [this page](https://docs.ai21.com/reference/python-sdk#chat)
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## LLMs
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You can use AI21's generative AI models as Langchain LLMs:
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You can use AI21's Jurassic generative AI models as LangChain LLMs.
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To use the newer Jamba model, use the [ChatAI21 chat model](#chat-models), which
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supports single-turn instruction/question answering capabilities.
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```python
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from langchain.prompts import PromptTemplate
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from langchain_core.prompts import PromptTemplate
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from langchain_ai21 import AI21LLM
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llm = AI21LLM(model="j2-ultra")
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@ -56,7 +59,7 @@ print(chain.invoke({"question": question}))
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## Embeddings
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You can use AI21's embeddings models as:
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You can use AI21's [embeddings model](https://docs.ai21.com/reference/embeddings-ref) as shown here:
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### Query
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@ -76,12 +79,12 @@ embeddings = AI21Embeddings()
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embeddings.embed_documents(["Hello! This is document 1", "And this is document 2!"])
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```
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## Task Specific Models
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## Task-Specific Models
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### Contextual Answers
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You can use AI21's contextual answers model to receives text or document, serving as a context,
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and a question and returns an answer based entirely on this context.
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You can use AI21's [contextual answers model](https://docs.ai21.com/reference/contextual-answers-ref) to parse
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given text and answer a question based entirely on the provided information.
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This means that if the answer to your question is not in the document,
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the model will indicate it (instead of providing a false answer)
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@ -91,7 +94,7 @@ from langchain_ai21 import AI21ContextualAnswers
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tsm = AI21ContextualAnswers()
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response = tsm.invoke(input={"context": "Your context", "question": "Your question"})
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response = tsm.invoke(input={"context": "Lots of information here", "question": "Your question about the context"})
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```
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You can also use it with chains and output parsers and vector DBs:
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```python
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@ -110,8 +113,8 @@ response = chain.invoke(
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### Semantic Text Splitter
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You can use AI21's semantic text splitter to split a text into segments.
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Instead of merely using punctuation and newlines to divide the text, it identifies distinct topics that will work well together and will form a coherent piece of text.
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You can use AI21's semantic [text segmentation model](https://docs.ai21.com/reference/text-segmentation-ref) to split a text into segments by topic.
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Text is split at each point where the topic changes.
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For a list for examples, see [this page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/modules/data_connection/document_transformers/semantic_text_splitter.ipynb).
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@ -19,7 +19,10 @@ from langchain_ai21.chat.chat_factory import create_chat_adapter
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class ChatAI21(BaseChatModel, AI21Base):
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"""ChatAI21 chat model.
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"""ChatAI21 chat model. Different model types support different parameters and
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different parameter values. Please read the [AI21 reference documentation]
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(https://docs.ai21.com/reference) for your model to understand which parameters
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are available.
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Example:
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.. code-block:: python
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@ -27,7 +30,10 @@ class ChatAI21(BaseChatModel, AI21Base):
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from langchain_ai21 import ChatAI21
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model = ChatAI21()
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model = ChatAI21(
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# defaults to os.environ.get("AI21_API_KEY")
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api_key="my_api_key"
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)
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"""
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model: str
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@ -42,7 +48,8 @@ class ChatAI21(BaseChatModel, AI21Base):
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"""The maximum number of tokens to generate for each response."""
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min_tokens: int = 0
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"""The minimum number of tokens to generate for each response."""
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"""The minimum number of tokens to generate for each response.
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_Not supported for all models._"""
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temperature: float = 0.7
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"""A value controlling the "creativity" of the model's responses."""
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@ -51,17 +58,20 @@ class ChatAI21(BaseChatModel, AI21Base):
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"""A value controlling the diversity of the model's responses."""
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top_k_return: int = 0
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"""The number of top-scoring tokens to consider for each generation step."""
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"""The number of top-scoring tokens to consider for each generation step.
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_Not supported for all models._"""
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frequency_penalty: Optional[Any] = None
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"""A penalty applied to tokens that are frequently generated."""
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"""A penalty applied to tokens that are frequently generated.
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_Not supported for all models._"""
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presence_penalty: Optional[Any] = None
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""" A penalty applied to tokens that are already present in the prompt."""
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""" A penalty applied to tokens that are already present in the prompt.
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_Not supported for all models._"""
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count_penalty: Optional[Any] = None
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"""A penalty applied to tokens based on their frequency
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in the generated responses."""
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in the generated responses. _Not supported for all models._"""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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@ -19,14 +19,24 @@ from langchain_ai21.ai21_base import AI21Base
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class AI21LLM(BaseLLM, AI21Base):
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"""AI21 large language models.
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"""AI21 large language models. Different model types support different parameters
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and different parameter values. Please read the [AI21 reference documentation]
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(https://docs.ai21.com/reference) for your model to understand which parameters
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are available.
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AI21LLM supports only the older Jurassic models.
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We recommend using ChatAI21 with the newest models, for better results and more
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features.
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Example:
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.. code-block:: python
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from langchain_ai21 import AI21LLM
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model = AI21LLM()
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model = AI21LLM(
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# defaults to os.environ.get("AI21_API_KEY")
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api_key="my_api_key"
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)
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"""
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model: str
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@ -40,7 +50,8 @@ class AI21LLM(BaseLLM, AI21Base):
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"""The maximum number of tokens to generate for each response."""
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min_tokens: int = 0
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"""The minimum number of tokens to generate for each response."""
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"""The minimum number of tokens to generate for each response.
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_Not supported for all models._"""
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temperature: float = 0.7
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"""A value controlling the "creativity" of the model's responses."""
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@ -49,17 +60,20 @@ class AI21LLM(BaseLLM, AI21Base):
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"""A value controlling the diversity of the model's responses."""
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top_k_return: int = 0
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"""The number of top-scoring tokens to consider for each generation step."""
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"""The number of top-scoring tokens to consider for each generation step.
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_Not supported for all models._"""
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frequency_penalty: Optional[Any] = None
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"""A penalty applied to tokens that are frequently generated."""
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"""A penalty applied to tokens that are frequently generated.
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_Not supported for all models._"""
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presence_penalty: Optional[Any] = None
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""" A penalty applied to tokens that are already present in the prompt."""
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""" A penalty applied to tokens that are already present in the prompt.
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_Not supported for all models._"""
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count_penalty: Optional[Any] = None
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"""A penalty applied to tokens based on their frequency
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in the generated responses."""
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in the generated responses. _Not supported for all models._"""
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custom_model: Optional[str] = None
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epoch: Optional[int] = None
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