Nimble Extract

import getpass
import os

if not os.environ.get("NIMBLE_API_KEY"):
    os.environ["NIMBLE_API_KEY"] = getpass.getpass("Nimble API key:\n")

from langchain_nimble import NimbleExtractTool

# Basic usage
tool = NimbleExtractTool()

import os
import getpass

from langchain_nimble import NimbleExtractTool
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

if not os.environ.get("OPENAI_API_KEY"):
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API key:\n")
if not os.environ.get("NIMBLE_API_KEY"):
    os.environ["NIMBLE_API_KEY"] = getpass.getpass("Nimble API key:\n")

# Initialize Nimble Extract Tool
extract_tool = NimbleExtractTool(
    parsing_type="markdown"
)

# Create agent with the tool
model = init_chat_model(model="gpt-4o", model_provider="openai", temperature=0)
agent = create_agent(model, [extract_tool])

# Ask the agent to extract and analyze content from LangChain documentation
user_input = "Extract and summarize the key concepts from these LangChain docs: https://python.langchain.com/docs/concepts/retrievers/, https://python.langchain.com/docs/concepts/tools/"

for step in agent.stream(
    {"messages": user_input},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

================================ Human Message =================================

Extract and summarize the key concepts from these LangChain docs: https://python.langchain.com/docs/concepts/retrievers/, https://python.langchain.com/docs/concepts/tools/

================================== Ai Message ==================================
Tool Calls:
  nimble_extract (call_abc123)
 Call ID: call_abc123
  Args:
    links: ['https://python.langchain.com/docs/concepts/retrievers/', 'https://python.langchain.com/docs/concepts/tools/']
    parsing_type: markdown

================================= Tool Message =================================
Name: nimble_extract

[{"title": "Retrievers | LangChain", "url": "https://python.langchain.com/docs/concepts/retrievers/", "content": "# Retrievers\n\nA retriever is an interface that returns documents given an unstructured query...\n\n## Key Concepts\n- Document retrieval from various sources\n- Integration with vector stores...", "metadata": {"extracted_at": "2025-12-10T..."}}, {"title": "Tools | LangChain", "url": "https://python.langchain.com/docs/concepts/tools/", "content": "# Tools\n\nTools are interfaces that agents can use to interact with the world...", "metadata": {...}}]

================================== Ai Message ==================================

Based on the extracted LangChain documentation, here are the key concepts:

**Retrievers:**
- Interface for returning documents based on unstructured queries
- Supports various data sources including vector stores
- Core component for RAG (Retrieval Augmented Generation) applications
- Enables semantic search over document collections

**Tools:**
- Interfaces enabling agents to interact with external systems
- Can be used for web search, API calls, calculations, and more
- Agents use tools to extend their capabilities beyond text generation
- Support both synchronous and asynchronous execution