mirror of https://github.com/hwchase17/langchain
cohere, docs: update imports and installs to langchain_cohere (#19918)
cohere: update imports and installs to langchain_cohere --------- Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>pull/19926/head
billytrend-cohere
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# langchain-cohere
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# Cohere
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>[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models
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> that help companies improve human-machine interactions.
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## Installation and Setup
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- Install the Python SDK :
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```bash
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pip install langchain-cohere
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```
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Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environment variable (`COHERE_API_KEY`)
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## Cohere langchain integrations
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| API | description | Endpoint docs | Import | Example usage |
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| ---------------- | -------------------------------- | ------------------------------------------------------ | -------------------------------------------------------------------- | ------------------------------------------------------------- |
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| Chat | Build chat bots | [chat](https://docs.cohere.com/reference/chat) | `from langchain_cohere import ChatCohere` | [cohere.ipynb](/docs/integrations/chat/cohere) |
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| LLM | Generate text | [generate](https://docs.cohere.com/reference/generate) | `from langchain_cohere import Cohere` | [cohere.ipynb](/docs/integrations/llms/cohere) |
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| RAG Retriever | Connect to external data sources | [chat + rag](https://docs.cohere.com/reference/chat) | `from langchain.retrievers import CohereRagRetriever` | [cohere.ipynb](/docs/integrations/retrievers/cohere) |
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| Text Embedding | Embed strings to vectors | [embed](https://docs.cohere.com/reference/embed) | `from langchain_cohere import CohereEmbeddings` | [cohere.ipynb](/docs/integrations/text_embedding/cohere) |
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| Rerank Retriever | Rank strings based on relevance | [rerank](https://docs.cohere.com/reference/rerank) | `from langchain.retrievers.document_compressors import CohereRerank` | [cohere.ipynb](/docs/integrations/retrievers/cohere-reranker) |
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## Quick copy examples
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### Chat
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```python
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from langchain_cohere import ChatCohere
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from langchain_core.messages import HumanMessage
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chat = ChatCohere()
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messages = [HumanMessage(content="knock knock")]
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print(chat(messages))
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```
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### LLM
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```python
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from langchain_cohere import Cohere
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llm = Cohere(model="command")
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print(llm.invoke("Come up with a pet name"))
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```
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### ReAct Agent
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```python
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langchain_cohere import ChatCohere, create_cohere_react_agent
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from langchain.prompts import ChatPromptTemplate
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from langchain.agents import AgentExecutor
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llm = ChatCohere()
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internet_search = TavilySearchResults(max_results=4)
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internet_search.name = "internet_search"
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internet_search.description = "Route a user query to the internet"
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prompt = ChatPromptTemplate.from_template("{input}")
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agent = create_cohere_react_agent(
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llm,
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[internet_search],
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prompt
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)
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agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)```
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agent_executor.invoke({
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"input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
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})
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```
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### RAG Retriever
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```python
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from langchain_cohere import ChatCohere
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from langchain.retrievers import CohereRagRetriever
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from langchain_core.documents import Document
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rag = CohereRagRetriever(llm=ChatCohere())
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print(rag.get_relevant_documents("What is cohere ai?"))
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```
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### Text Embedding
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```python
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from langchain_cohere import CohereEmbeddings
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embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
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print(embeddings.embed_documents(["This is a test document."]))
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```
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