Ollama: Integração com LangChain

from langchain_ollama import ChatOllama

llm = ChatOllama(model="qwen2.5", temperature=0.2)
result = llm.invoke("Analyze the 20% drop in MRR last month")

pip install langchain langchain-ollama langchain-community

from langchain_ollama import ChatOllama
from langchain_core.messages import HumanMessage, SystemMessage

# Initialize ChatOllama
llm = ChatOllama(model="qwen2.5", temperature=0.3)

# Simple invocation
response = llm.invoke("What is RAG?")
print(response.content)

# With system message
response = llm.invoke([
    SystemMessage(content="You are a data analyst. Be concise."),
    HumanMessage(content="What causes MRR to drop?")
])
print(response.content)

# Execution successful

from langchain_ollama import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model="qwen2.5", temperature=0.3)

# Define prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a {role}. Be concise and professional."),
    ("human", "{input}")
])

# Build chain: prompt -> llm -> parser
chain = prompt | llm | StrOutputParser()

# Invoke with variables
result = chain.invoke({
    "role": "customer service agent for e-commerce",
    "input": "Customer wants to cancel order #12345"
})
print(result)

# Execution successful

from langchain_ollama import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field

# Define output schema
class CustomerIntent(BaseModel):
    intent: str = Field(description="Customer intent category")
    confidence: float = Field(description="Confidence score 0-1")
    order_number: str | None = Field(description="Order number if mentioned")

# Create parser
parser = PydanticOutputParser(pydantic_object=CustomerIntent)

# Build chain with format instructions
llm = ChatOllama(model="qwen2.5", temperature=0.1, format="json")
prompt = ChatPromptTemplate.from_messages([
    ("system", "Classify the customer intent.\n{format_instructions}"),
    ("human", "{query}")
])

chain = prompt | llm | parser

# Invoke
result = chain.invoke({
    "query": "I want to track my order #88765",
    "format_instructions": parser.get_format_instructions()
})
print(f"Intent: {result.intent}")
print(f"Confidence: {result.confidence}")
print(f"Order: {result.order_number}")

# Execution successful

from langchain_ollama import ChatOllama
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate

# Define tools
@tool
def get_order_status(order_id: str) -> str:
    """Get the status of an order by its ID."""
    # Simulated database lookup
    orders = {
        "12345": "Shipped, arriving in 2 days",
        "88765": "Processing, shipping tomorrow",
        "99999": "Delivered on 2024-01-15"
    }
    return orders.get(order_id, f"Order {order_id} not found")

@tool
def check_product_stock(product_name: str) -> str:
    """Check if a product is in stock."""
    stock = {
        "wireless headphones": "In stock: 45 units",
        "laptop stand": "Out of stock, restock in 3 days",
        "usb cable": "In stock: 200 units"
    }
    return stock.get(product_name.lower(), f"Product '{product_name}' not found")

# Build agent
llm = ChatOllama(model="qwen2.5", temperature=0)
tools = [get_order_status, check_product_stock]

prompt = PromptTemplate.from_template(
    """Answer the following question. You have access to these tools:
{tools}

Use this format:
Question: the input question
Thought: what to do
Action: tool name (one of [{tool_names}])
Action Input: tool input
Observation: tool output
... (repeat Thought/Action/Action Input/Observation)
Thought: I know the final answer
Final Answer: the final answer

Question: {input}
{agent_scratchpad}"""
)

agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run agent
result = agent_executor.invoke({
    "input": "What is the status of order 88765?"
})
print(result["output"])

# Function defined successfully

# ============================================
# Comprehensive: SaaS metrics analysis agent
# Uses LangChain + Ollama for automated analysis
# ============================================

from langchain_ollama import ChatOllama
from langchain_core.tools import tool
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import PromptTemplate
import json

# Simulated metrics database
METRICS_DB = {
    "mrr": {"current": 85000, "previous": 106250, "change": -20},
    "churn_rate": {"current": 8.5, "previous": 5.2, "change": 63},
    "arr": {"current": 1020000, "previous": 1275000, "change": -20},
    "active_users": {"current": 12500, "previous": 11800, "change": 6},
    "cac": {"current": 250, "previous": 180, "change": 39},
    "ltv": {"current": 2400, "previous": 3200, "change": -25}
}

@tool
def get_metric(metric_name: str) -> str:
    """Get a SaaS metric value. Available: mrr, churn_rate, arr, active_users, cac, ltv."""
    data = METRICS_DB.get(metric_name.lower())
    if data:
        return json.dumps(data)
    return f"Metric '{metric_name}' not found. Available: {list(METRICS_DB.keys())}"

@tool
def compare_metrics(metric1: str, metric2: str) -> str:
    """Compare two metrics and identify correlations."""
    m1 = METRICS_DB.get(metric1.lower())
    m2 = METRICS_DB.get(metric2.lower())
    if not m1 or not m2:
        return "One or both metrics not found."
    return json.dumps({
        "metric1": {"name": metric1, **m1},
        "metric2": {"name": metric2, **m2},
        "correlation_note": "Both metrics show negative trends"
    })

# Build analysis agent
llm = ChatOllama(model="qwen2.5", temperature=0.2)
tools = [get_metric, compare_metrics]

prompt = PromptTemplate.from_template(
    """You are a SaaS metrics analyst. Analyze data and provide insights.
Available tools: {tools}

Format:
Question: the input
Thought: what to do
Action: tool name from [{tool_names}]
Action Input: tool input
Observation: tool output
... (repeat as needed)
Thought: I have enough information
Final Answer: detailed analysis with actionable recommendations

Question: {input}
{agent_scratchpad}"""
)

agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True, max_iterations=5)

# Run analysis
if __name__ == "__main__":
    result = executor.invoke({
        "input": "MRR dropped 20% last month. Analyze the key metrics and identify likely causes with recommendations."
    })
    print("\n=== Analysis Report ===")
    print(result["output"])