mirror of
https://github.com/hwchase17/langchain
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a042e804b4
- Updated docs to have an example to use Jamba instead of J2 --------- Co-authored-by: Asaf Gardin <asafg@ai21.com> Co-authored-by: Erick Friis <erick@langchain.dev>
123 lines
3.2 KiB
Markdown
123 lines
3.2 KiB
Markdown
# 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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## Installation and Setup
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- Install the AI21 partner package
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```bash
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pip install langchain-ai21
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```
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- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`)
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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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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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```
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Then initialize
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```python
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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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messages = [HumanMessage(content="Hello from AI21")]
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chat.invoke(messages)
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```
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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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```python
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from langchain.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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template = """Question: {question}
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Answer: Let's think step by step."""
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prompt = PromptTemplate.from_template(template)
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chain = prompt | llm
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question = "Which scientist discovered relativity?"
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print(chain.invoke({"question": question}))
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```
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## Embeddings
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You can use AI21's embeddings models as:
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### Query
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```python
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from langchain_ai21 import AI21Embeddings
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embeddings = AI21Embeddings()
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embeddings.embed_query("Hello! This is some query")
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```
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### Document
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```python
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from langchain_ai21 import AI21Embeddings
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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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### 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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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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```python
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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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```
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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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from langchain_ai21 import AI21ContextualAnswers
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from langchain_core.output_parsers import StrOutputParser
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tsm = AI21ContextualAnswers()
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chain = tsm | StrOutputParser()
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response = chain.invoke(
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{"context": "Your context", "question": "Your question"},
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)
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```
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## Text Splitters
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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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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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```python
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from langchain_ai21 import AI21SemanticTextSplitter
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splitter = AI21SemanticTextSplitter()
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response = splitter.split_text("Your text")
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``` |