An integration package connecting Cohere and LangChain
Project description
Cohere
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
Installation and Setup
- Install the Python SDK :
pip install langchain-cohere
Get a Cohere api key and set it as an environment variable (COHERE_API_KEY)
Cohere langchain integrations
| API | description | Endpoint docs | Import | Example usage |
|---|---|---|---|---|
| Chat | Build chat bots | chat | from langchain_cohere import ChatCohere |
cohere.ipynb |
| LLM | Generate text | generate | from langchain_cohere import Cohere |
cohere.ipynb |
| RAG Retriever | Connect to external data sources | chat + rag | from langchain.retrievers import CohereRagRetriever |
cohere.ipynb |
| Text Embedding | Embed strings to vectors | embed | from langchain_cohere import CohereEmbeddings |
cohere.ipynb |
| Rerank Retriever | Rank strings based on relevance | rerank | from langchain.retrievers.document_compressors import CohereRerank |
cohere.ipynb |
Quick copy examples
Chat
from langchain_cohere import ChatCohere
from langchain_core.messages import HumanMessage
chat = ChatCohere()
messages = [HumanMessage(content="knock knock")]
print(chat(messages))
LLM
from langchain_cohere import Cohere
llm = Cohere(model="command")
print(llm.invoke("Come up with a pet name"))
ReAct Agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_cohere import ChatCohere, create_cohere_react_agent
from langchain.prompts import ChatPromptTemplate
from langchain.agents import AgentExecutor
llm = ChatCohere()
internet_search = TavilySearchResults(max_results=4)
internet_search.name = "internet_search"
internet_search.description = "Route a user query to the internet"
prompt = ChatPromptTemplate.from_template("{input}")
agent = create_cohere_react_agent(
llm,
[internet_search],
prompt
)
agent_executor = AgentExecutor(agent=agent, tools=[internet_search], verbose=True)```
agent_executor.invoke({
"input": "In what year was the company that was founded as Sound of Music added to the S&P 500?",
})
RAG Retriever
from langchain_cohere import ChatCohere
from langchain.retrievers import CohereRagRetriever
from langchain_core.documents import Document
rag = CohereRagRetriever(llm=ChatCohere())
print(rag.get_relevant_documents("What is cohere ai?"))
Text Embedding
from langchain_cohere import CohereEmbeddings
embeddings = CohereEmbeddings(model="embed-english-light-v3.0")
print(embeddings.embed_documents(["This is a test document."]))
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