LangChain Parallel Web Integration

This package provides LangChain integrations for Parallel, enabling real-time web research and AI capabilities through an OpenAI-compatible interface.

Features

• Chat Models: ChatParallelWeb - Real-time web research chat completions
• Search Tools: ParallelWebSearchTool - Direct access to Parallel's Search API
• Extract Tools: ParallelExtractTool - Clean content extraction from web pages
• Streaming Support: Real-time response streaming
• Async/Await: Full asynchronous operation support
• OpenAI Compatible: Uses familiar OpenAI SDK patterns
• LangChain Integration: Seamless integration with LangChain ecosystem

Installation

pip install langchain-parallel-web

Setup

1. Get your API key from Parallel
2. Set your API key as an environment variable:

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

Chat Models

ChatParallelWeb

The ChatParallelWeb class provides access to Parallel's Chat API, which combines language models with real-time web research capabilities.

Basic Usage

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_parallel_web.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)

Streaming Responses

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

Async Operations

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())

Conversation Context

# 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)

Configuration Options

Parameter	Type	Default	Description
model	str	"speed"	Parallel model name
api_key	Optional[SecretStr]	None	API key (uses PARALLEL_API_KEY env var if not provided)
base_url	str	"https://api.parallel.ai"	API base URL
temperature	Optional[float]	None	Sampling temperature (ignored by Parallel)
max_tokens	Optional[int]	None	Max tokens (ignored by Parallel)
timeout	Optional[float]	None	Request timeout
max_retries	int	2	Max retry attempts

Real-Time Web Research

Parallel's Chat API provides real-time access to web information, making it perfect for:

• Current Events: Get up-to-date information about recent events
• Market Data: Access current stock prices, market trends
• Research: Find the latest research papers, developments
• Weather: Get current weather conditions
• News: Access breaking news and recent articles

# 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

Integration with LangChain

Chains

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)

Agents

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)

Search API

The Search API provides direct access to Parallel's web search capabilities, returning structured, compressed excerpts optimized for LLM consumption.

ParallelWebSearchTool

The search tool provides direct access to Parallel's Search API:

from langchain_parallel_web 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?",
    "max_results": 5
})

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 API Configuration

Parameter	Type	Default	Description
objective	Optional[str]	None	Natural-language description of research goal
search_queries	Optional[List[str]]	None	Specific search queries (max 5, 200 chars each)
max_results	int	10	Maximum results to return (1-40)
max_chars_per_result	int	1500	Maximum characters per result (min 100)
api_key	Optional[SecretStr]	None	API key (uses env var if not provided)
base_url	str	"https://api.parallel.ai"	API base URL

Search with Specific Queries

You can provide specific search queries instead of an objective:

# Search with specific queries
result = search_tool.invoke({
    "search_queries": [
        "renewable energy 2024",
        "solar power developments",
        "wind energy statistics"
    ],
    "max_results": 8
})

Tool Usage in Agents

The search tool works seamlessly with LangChain agents:

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"])

Extract API

The Extract API provides clean content extraction from web pages, returning structured markdown-formatted content optimized for LLM consumption.

ParallelExtractTool

The extract tool extracts clean, structured content from web pages:

from langchain_parallel_web 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 with Search Objective

Focus extraction on specific topics using search objectives:

# Extract content focused on a specific objective
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": True,
    "full_content": False
})

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

Extract with Search Queries

Extract content relevant to specific search queries:

# 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()

Content Length Control

# Control content length per extraction
extract_tool = ParallelExtractTool(max_chars_per_extract=2000)

result = extract_tool.invoke({
    "urls": ["https://en.wikipedia.org/wiki/Quantum_computing"]
})

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

Extract API Configuration

Parameter	Type	Default	Description
urls	List[str]	Required	List of URLs to extract content from
search_objective	Optional[str]	None	Natural language objective to focus extraction
search_queries	Optional[List[str]]	None	Specific keyword queries to focus extraction
excerpts	Union[bool, ExcerptSettings]	True	Include relevant excerpts (focused on objective/queries if provided)
full_content	Union[bool, FullContentSettings]	False	Include full page content
fetch_policy	Optional[FetchPolicy]	None	Cache vs live content policy
max_chars_per_extract	Optional[int]	None	Maximum characters per extraction (tool-level setting)
api_key	Optional[SecretStr]	None	API key (uses env var if not provided)
base_url	str	"https://api.parallel.ai"	API base URL

Error Handling

The extract tool gracefully handles failed extractions:

# 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")

Async Extract

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())

Error Handling

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}")

Examples

See the examples/ and docs/ directories for complete working examples:

• examples/chat_example.py - Chat model usage examples
• docs/search_tool.ipynb - Search tool examples and tutorials
• docs/extract_tool.ipynb - Extract tool examples and tutorials
• Basic synchronous usage
• Streaming responses
• Async operations
• Conversation management
• Tool usage in agents

API Compatibility

This integration provides access to two Parallel APIs:

Chat API Compatibility

The Chat API uses Parallel's OpenAI-compatible interface:

• Supported: Messages, streaming, response_format (JSON schema)
• Ignored: temperature, max_tokens, top_p, stop, most OpenAI-specific parameters
• Not Supported: Function calling, multimodal inputs (images/audio), tool usage

Search API Features

The Search API provides direct web search capabilities:

• Supported: Objective-based search, query-based search, two processor tiers
• Output: Structured results with URLs, titles, and relevant excerpts
• Integration: Works with LangChain tools, retrievers, and agents

Extract API Features

The Extract API provides clean content extraction from web pages:

• Supported: Batch URL extraction, content length control, markdown formatting
• Output: Clean, structured content with metadata (title, publish date, etc.)
• Integration: Works with LangChain tools and agents
• Error Handling: Gracefully handles failed extractions with detailed error info

Performance & Rate Limits

Chat API

• Default Rate Limit: 300 requests per minute
• Performance: 3 second p50 TTFT (time to first token) with streaming
• Use Cases: Interactive chat, real-time responses

Search API

• Default Rate Limit: Contact Parallel for rate limit information
• Performance: Varies based on query complexity and result count
• Use Cases: Real-time web information, research, content discovery

Production Usage

Contact Parallel for:

• Higher rate limits
• Enterprise features
• Custom configurations

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Documentation

• Parallel Documentation
• Chat API Reference
• Search API Reference
• LangChain Documentation

Getting Help

• GitHub Issues
• Parallel Support

Changelog

v0.1.0

• Initial release
• Chat Models: ChatParallelWeb with real-time web research
• Search Tools: ParallelWebSearchTool for direct API access
• Extract Tools: ParallelExtractTool for clean content extraction
• Streaming and async/await support
• Batch URL extraction with error handling
• Full LangChain ecosystem compatibility