cohere-api

# Python
pip install cohere

# TypeScript/Node
npm install cohere-ai

export COHERE_API_KEY=...

import cohere

co = cohere.ClientV2()  # reads COHERE_API_KEY from the environment

response = co.chat(
    model="command-a-plus-05-2026",
    messages=[
        {"role": "user", "content": "Explain transformer architecture in plain English."},
    ],
)

print(response.message.content[0].text)

import cohere

co = cohere.ClientV2()

# Embed documents for indexing
docs = [
    "Cohere provides enterprise NLP solutions.",
    "Embeddings convert text into dense vectors.",
    "RAG improves LLM answers with retrieved context.",
]

response = co.embed(
    texts=docs,
    model="embed-v4.0",
    input_type="search_document",  # "search_document" for indexing
    embedding_types=["float"],
)  # at most 96 texts per call

embeddings = response.embeddings.float_
print(f"Embedding shape: {len(embeddings)} x {len(embeddings[0])}")  # 3 x 1536 for embed-v4.0 (output_dimension picks 256/512/1024/1536)

import cohere
import numpy as np

co = cohere.ClientV2()

# Query embedding — use "search_query" for queries
query = "How do embeddings work?"

query_response = co.embed(
    texts=[query],
    model="embed-v4.0",
    input_type="search_query",  # Different from "search_document"!
    embedding_types=["float"],
)

query_vector = query_response.embeddings.float_[0]

# Find most similar documents using cosine similarity
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

similarities = [cosine_similarity(query_vector, doc_emb) for doc_emb in doc_embeddings]
top_idx = np.argsort(similarities)[::-1][:5]

import cohere

co = cohere.ClientV2()

query = "What are the benefits of renewable energy?"

documents = [
    "Solar panels convert sunlight into electricity efficiently.",
    "Wind energy reduces carbon emissions significantly.",
    "The history of fossil fuels dates back centuries.",
    "Renewable energy creates jobs in local communities.",
    "Nuclear power is debated as a clean energy source.",
    "Oil prices fluctuate based on global demand.",
]

rerank_response = co.rerank(
    model="rerank-v5.0-pro",
    query=query,
    documents=documents,
    top_n=3,  # Return top 3 most relevant
)

for rank, result in enumerate(rerank_response.results, start=1):
    print(f"#{rank} (document {result.index}): score {result.relevance_score:.3f}")
    print(f"  {documents[result.index]}\n")

import cohere

co = cohere.ClientV2()

# RAG with grounding documents — the model returns cited answers
documents = [
    {"id": "doc1", "data": {"title": "Renewable Energy", "snippet": "Solar energy capacity grew 25% in 2024, reaching 1.5 TW globally."}},
    {"id": "doc2", "data": {"title": "Climate Policy", "snippet": "The EU Green Deal targets 55% emissions reduction by 2030."}},
]

response = co.chat(
    model="command-a-plus-05-2026",
    messages=[{"role": "user", "content": "What is the current state of renewable energy?"}],
    documents=documents,
)

print(response.message.content[0].text)

# Each citation gives the cited span (start/end/text) and the source documents
for citation in response.message.citations or []:
    print(citation.text, [source.id for source in citation.sources])

import cohere
import json

co = cohere.ClientV2()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "Get the current stock price for a ticker symbol",
            "parameters": {
                "type": "object",
                "properties": {
                    "ticker": {"type": "string", "description": "Stock ticker (e.g. AAPL)"},
                },
                "required": ["ticker"],
            },
        },
    }
]

messages = [{"role": "user", "content": "What's the current Apple stock price?"}]

response = co.chat(
    model="command-a-plus-05-2026",
    messages=messages,
    tools=tools,
)

if response.message.tool_calls:
    tool_call = response.message.tool_calls[0]
    args = json.loads(tool_call.function.arguments)
    print(f"Tool: {tool_call.function.name}, Args: {args}")

    # Execute tool and return result
    messages.append(response.message)  # keeps tool_plan and tool_calls
    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": [{"type": "document", "document": {"data": json.dumps({"price": 189.84, "change": "+1.2%"})}}],
    })

    final = co.chat(model="command-a-plus-05-2026", messages=messages, tools=tools)
    print(final.message.content[0].text)

import cohere
import numpy as np

co = cohere.ClientV2()

def build_rag_pipeline(documents: list[str]):
    response = co.embed(
        texts=documents,
        model="embed-v4.0",
        input_type="search_document",
        embedding_types=["float"],
    )
    return response.embeddings.float_

def retrieve_and_rerank(query: str, documents: list[str], doc_embeddings, top_k=10, top_n=3):
    # Step 1: Embed query
    q_resp = co.embed(
        texts=[query],
        model="embed-v4.0",
        input_type="search_query",
        embedding_types=["float"],
    )
    q_vec = q_resp.embeddings.float_[0]

    # Step 2: Cosine similarity search
    sims = [np.dot(q_vec, d) / (np.linalg.norm(q_vec) * np.linalg.norm(d)) for d in doc_embeddings]
    candidates_idx = np.argsort(sims)[::-1][:top_k]
    candidates = [documents[i] for i in candidates_idx]

    # Step 3: Rerank candidates
    reranked = co.rerank(
        model="rerank-v5.0-pro",
        query=query,
        documents=candidates,
        top_n=top_n,
    )

    return [candidates[r.index] for r in reranked.results]

def answer_with_rag(query: str, context_docs: list[str]) -> str:
    docs = [{"data": {"snippet": doc}} for doc in context_docs]
    response = co.chat(
        model="command-a-plus-05-2026",
        messages=[{"role": "user", "content": query}],
        documents=docs,
    )
    return response.message.content[0].text