Cohere observability

pip install -U 'lmnr[all]' cohere python-dotenv

# .env file
LMNR_PROJECT_API_KEY=your-laminar-project-api-key
COHERE_API_KEY=your-cohere-api-key

from lmnr import Laminar
import cohere
import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# This single line instruments all Cohere API calls
Laminar.initialize()

# Initialize Cohere client as usual (v2 client)
co = cohere.ClientV2(os.environ["COHERE_API_KEY"])

response = co.chat(
    model="command-a-03-2025",
    messages=[
        {"role": "user", "content": "Write a one-sentence intro for my new startup teammates."}
    ],
)

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

res = co.chat_stream(
    model="command-a-03-2025",
    messages=[
        {"role": "user", "content": "Write a one-sentence intro for my new startup teammates."}
    ],
)

for chunk in res:
    if chunk and getattr(chunk, "type", None) == "content-delta":
        print(chunk.delta.message.content.text, end="")

from lmnr import observe

@observe(name="retrieve_documents")
def retrieve_documents(query: str):
    return [
        {"data": {"text": "Reimbursing Travel Expenses: Submit expenses via the finance tool."}},
        {"data": {"text": "Working from Abroad: Coordinate with your manager and keep core hours."}},
        {"data": {"text": "Health Benefits: Gym memberships, on-site yoga, and health insurance."}},
    ]

@observe(name="generate_answer")
def generate_answer(query: str, documents: list):
    return co.chat(
        model="command-a-03-2025",
        messages=[{"role": "user", "content": query}],
        documents=documents,
    )

@observe(name="rag_pipeline")
def rag_pipeline(query: str):
    docs = retrieve_documents(query)
    return generate_answer(query, docs)

resp = rag_pipeline("Are there health benefits?")
print(resp.message.content[0].text)

if resp.message.citations:
    for citation in resp.message.citations:
        print(citation, "\n")

query = "observability for LLM applications"
documents = [
    "Cohere provides an Embed API for creating vector representations of text.",
    "Laminar enables tracing and monitoring of LLM calls.",
    "OpenAI offers GPT models for chat and text generation."
]

rerank_response = co.rerank(
    model="rerank-v3.5",
    query=query,
    documents=documents,
    top_n=3,
)

for result in rerank_response.results:
    print(result.index, result.relevance_score, documents[result.index])

import numpy as np

docs = [
    "Laminar provides LLM tracing and analytics.",
    "Cohere offers models for chat, embedding, and reranking.",
    "OpenTelemetry is an observability framework for cloud software.",
]

# Embed documents
doc_emb = co.embed(
    model="embed-v4.0",
    texts=docs,
    input_type="search_document",
).embeddings

# Embed query
query_emb = co.embed(
    model="embed-v4.0",
    texts=["observability for LLM applications"],
    input_type="search_query",
).embeddings[0]

# Compute cosine similarity
def cosine(a, b):
    a = np.array(a)
    b = np.array(b)
    return float(a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b)))

sims = [(i, cosine(query_emb, e)) for i, e in enumerate(doc_emb)]
for i, score in sorted(sims, key=lambda x: x[1], reverse=True):
    print(i, round(score, 4), docs[i])