> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# ParallelSearchRetriever integration

> Integrate with the ParallelSearchRetriever retriever using LangChain Python.

[`ParallelSearchRetriever`](https://reference.langchain.com/python/langchain-parallel/retrievers/ParallelSearchRetriever) is a LangChain [`BaseRetriever`](/oss/python/langchain/retrieval) backed by [Parallel](https://platform.parallel.ai/)'s [Search API](https://docs.parallel.ai/search/search-quickstart). It returns `list[Document]` with rich `metadata` (`url`, `title`, `publish_date`, `search_id`, `excerpts`, `query`) and slots into any RAG pipeline.

<Note>
  Looking for an LLM-callable tool that returns the raw search response instead of `Document`s? See [ParallelSearchTool](/oss/python/integrations/tools/parallel_search).
</Note>

## Overview

### Integration details

| Class                                                                                                                     | Package                                                                            | JS support |                                                                                                                   Package latest                                                                                                                   |
| :------------------------------------------------------------------------------------------------------------------------ | :--------------------------------------------------------------------------------- | :--------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [`ParallelSearchRetriever`](https://reference.langchain.com/python/langchain-parallel/retrievers/ParallelSearchRetriever) | [`langchain-parallel`](https://reference.langchain.com/python/langchain-parallel/) |      ❌     | <a href="https://pypi.org/project/langchain-parallel/" target="_blank"><img src="https://img.shields.io/pypi/v/langchain-parallel?style=flat-square&label=%20&color=orange" alt="PyPI - Latest version" noZoom height="100" class="rounded" /></a> |

## Setup

The integration lives in the `langchain-parallel` package.

<CodeGroup>
  ```bash pip theme={null}
  pip install -U langchain-parallel
  ```

  ```bash uv theme={null}
  uv add langchain-parallel
  ```
</CodeGroup>

### Credentials

Head to [Parallel](https://platform.parallel.ai) to sign up and generate an API key. Set `PARALLEL_API_KEY` in your environment:

```python theme={null}
import getpass
import os

if not os.environ.get("PARALLEL_API_KEY"):
    os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n")
```

## Instantiation

```python theme={null}
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`:

```python theme={null}
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")
```

```text theme={null}
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 ...
```

### Configured search

Pass an `objective` for a richer retrieval target alongside the keyword `search_queries`. The retriever forwards source and fetch policies to the underlying Search API.

```python theme={null}
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

```python theme={null}
docs = await retriever.ainvoke("Latest GLP-1 trial results 2025")
```

## Use within a chain

`ParallelSearchRetriever` plugs into any LangChain chain:

```python theme={null}
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-4-8")

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`](https://reference.langchain.com/python/langchain-parallel/retrievers/ParallelSearchRetriever) API reference or the [Parallel Search API guides](https://docs.parallel.ai/search/search-quickstart).

***

<div className="source-links">
  <Callout icon="terminal-2">
    [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  </Callout>

  <Callout icon="edit">
    [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/retrievers/parallel.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  </Callout>
</div>