LangChain

pip install langchain-parallel

export PARALLEL_API_KEY="your-api-key-here"

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_parallel.chat_models import ChatParallelWeb

# Initialize the chat model
chat = ChatParallelWeb(
    model="speed",  # Parallel's chat model
    temperature=0.7,  # Optional: ignored by Parallel
    max_tokens=None,  # Optional: ignored by Parallel
)

# Create messages
messages = [
    SystemMessage(content="You are a helpful assistant with access to real-time web information."),
    HumanMessage(content="What are the latest developments in artificial intelligence?")
]

# Get response
response = chat.invoke(messages)
print(response.content)

# Stream responses for real-time output
for chunk in chat.stream(messages):
    if chunk.content:
        print(chunk.content, end="", flush=True)

import asyncio

async def main():
    # Async invoke
    response = await chat.ainvoke(messages)
    print(response.content)

    # Async streaming
    async for chunk in chat.astream(messages):
        if chunk.content:
            print(chunk.content, end="", flush=True)

asyncio.run(main())

# Maintain conversation history
messages = [
    SystemMessage(content="You are a helpful assistant.")
]

# First turn
messages.append(HumanMessage(content="What is machine learning?"))
response = chat.invoke(messages)
messages.append(response)  # Add assistant response

# Second turn with context
messages.append(HumanMessage(content="How does it work?"))
response = chat.invoke(messages)
print(response.content)

# Example: Current events
messages = [
    SystemMessage(content="You are a research assistant with access to real-time web data."),
    HumanMessage(content="What happened in the stock market today?")
]

response = chat.invoke(messages)
print(response.content)  # Gets real-time market information

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

# Create a chain
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant with access to real-time web information."),
    ("human", "{question}")
])

chain = prompt | chat | StrOutputParser()

# Use the chain
result = chain.invoke({"question": "What are the latest AI breakthroughs?"})
print(result)

from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

# Create an agent with web research capabilities
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to real-time web information."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

# Use with tools for additional capabilities
# agent = create_openai_functions_agent(chat, tools, prompt)
# agent_executor = AgentExecutor(agent=agent, tools=tools)

from langchain_parallel import ParallelWebSearchTool

# Initialize the search tool
search_tool = ParallelWebSearchTool()

# Search with an objective
result = search_tool.invoke({
    "objective": "What are the latest developments in renewable energy?",
    "mode": "fast"
})

print(result)
# {
#     "search_id": "search_123...",
#     "results": [
#         {
#             "url": "https://example.com/renewable-energy",
#             "title": "Latest Renewable Energy Developments",
#             "excerpts": [
#                 "Solar energy has seen remarkable growth...",
#                 "Wind power capacity increased by 15%..."
#             ]
#         }
#     ]
# }

# Search with specific queries
result = search_tool.invoke({
    "search_queries": [
        "renewable energy 2024",
        "solar power developments",
        "wind energy statistics"
    ],
    "mode": "fast"
})

from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

# Create agent with search capabilities
tools = [search_tool]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a research assistant. Use the search tool to find current information."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_openai_functions_agent(chat, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# Run the agent
result = agent_executor.invoke({
    "input": "What are the latest developments in artificial intelligence?"
})
print(result["output"])

from langchain_parallel import ParallelExtractTool

# Initialize the extract tool
extract_tool = ParallelExtractTool()

# Extract from a single URL
result = extract_tool.invoke({
    "urls": ["https://en.wikipedia.org/wiki/Artificial_intelligence"]
})

print(result)
# [
#     {
#         "url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
#         "title": "Artificial intelligence - Wikipedia",
#         "content": "# Artificial intelligence\n\nMain content in markdown...",
#         "publish_date": "2024-01-15"  # Optional
#     }
# ]

# Extract content focused on a specific objective with excerpt settings
result = extract_tool.invoke({
    "urls": ["https://en.wikipedia.org/wiki/Artificial_intelligence"],
    "search_objective": "What are the main applications and ethical concerns of AI?",
    "excerpts": {"max_chars_per_result": 2000},
    "full_content": False,
    "fetch_policy": {
        "max_age_seconds": 86400,
        "timeout_seconds": 60,
        "disable_cache_fallback": False
    }
})

# Returns relevant excerpts focused on the objective
print(result[0]["excerpts"])  # List of relevant text excerpts

# Extract content focused on specific queries
result = extract_tool.invoke({
    "urls": [
        "https://en.wikipedia.org/wiki/Machine_learning",
        "https://en.wikipedia.org/wiki/Deep_learning"
    ],
    "search_queries": ["neural networks", "training algorithms", "applications"],
    "excerpts": True
})

for item in result:
    print(f"Title: {item['title']}")
    print(f"Relevant excerpts: {len(item['excerpts'])}")
    print()

# Control full content length per extraction
result = extract_tool.invoke({
    "urls": ["https://en.wikipedia.org/wiki/Quantum_computing"],
    "full_content": {"max_chars_per_result": 3000}
})

print(f"Content length: {len(result[0]['content'])} characters")

# Mix of valid and invalid URLs
result = extract_tool.invoke({
    "urls": [
        "https://en.wikipedia.org/wiki/Python_(programming_language)",
        "https://this-domain-does-not-exist-12345.com/"
    ]
})

for item in result:
    if "error_type" in item:
        print(f"Failed: {item['url']} - {item['content']}")
    else:
        print(f"Success: {item['url']} - {len(item['content'])} chars")

import asyncio

async def extract_async():
    result = await extract_tool.ainvoke({
        "urls": ["https://en.wikipedia.org/wiki/Artificial_intelligence"]
    })
    return result

# Run async extraction
result = asyncio.run(extract_async())

try:
    response = chat.invoke(messages)
    print(response.content)
except ValueError as e:
    if "API key not found" in str(e):
        print("Please set your PARALLEL_API_KEY environment variable")
    else:
        print(f"API Error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")