Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
ParallelSearchRetriever is a LangChain BaseRetriever backed by Parallel’s Search API. It returns list[Document] with rich metadata (url, title, publish_date, search_id, excerpts, query) and slots into any RAG pipeline.
Looking for an LLM-callable tool that returns the raw search response instead of Documents? See ParallelSearchTool. Overview
Integration details
Setup
The integration lives in the langchain-parallel package.
pip install -U langchain-parallel
Credentials
Head to Parallel to sign up and generate an API key. Set PARALLEL_API_KEY in your environment:
import getpass
import os
if not os.environ.get("PARALLEL_API_KEY"):
os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n")
Instantiation
from langchain_parallel import ParallelSearchRetriever
retriever = ParallelSearchRetriever(
max_results=3,
excerpts={"max_chars_per_result": 800},
)
Usage
Each returned Document has its excerpts joined into page_content and exposes the source URL, title, and publish date in metadata:
docs = retriever.invoke("breakthroughs in fusion energy 2025")
for d in docs:
print(d.metadata.get("title"), "—", d.metadata.get("url"))
print(d.page_content[:200], "...\n")
Net energy gain in fusion: NIF results — https://www.nature.com/articles/...
The National Ignition Facility achieved net energy gain on December 5, 2022 ...
Commonwealth Fusion's SPARC milestone — https://news.mit.edu/...
SPARC is on track for first plasma in 2026 ...
objective for a richer retrieval target alongside the keyword search_queries. The retriever forwards source and fetch policies to the underlying Search API.
configured = ParallelSearchRetriever(
max_results=5,
excerpts={"max_chars_per_result": 1500},
mode="basic", # 'basic' (lower latency) or 'advanced' (higher quality)
source_policy={
"include_domains": ["nature.com", "science.org", "iter.org"],
},
)
docs = configured.invoke(
"What's the latest peer-reviewed result on net-energy-gain fusion?"
)
Async
docs = await retriever.ainvoke("Latest GLP-1 trial results 2025")
Use within a chain
ParallelSearchRetriever plugs into any LangChain chain:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain.chat_models import init_chat_model
llm = init_chat_model(model="claude-opus-4-7")
prompt = ChatPromptTemplate.from_messages([
("system", "Answer using only the context below. Cite URLs."),
("human", "Context:\n{context}\n\nQuestion: {question}"),
])
def format_docs(docs):
return "\n\n".join(
f"[{d.metadata.get('url')}] {d.page_content[:500]}" for d in docs
)
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
chain.invoke("What was the most recent fusion energy breakthrough?")
API reference
For detailed documentation, head to the ParallelSearchRetriever API reference or the Parallel Search API guides.
