Issue Station
Sign In
Tools hereToolbox

langchain - 💡(How to fix) Fix Add Cala verified knowledge retriever to integrations docs

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
RAW_BUFFERClick to expand / collapse

Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

Feature request

Motivation

Cala (https://cala.ai) is a verified knowledge graph API for AI agents. It returns structured, source-cited, traceable facts — not raw web scrapes — making it well-suited for production RAG pipelines that require auditable, hallucination-resistant retrieval.

A third-party LangChain integration package (langchain-cala) is already published on PyPI, implementing CalaRetriever as a native BaseRetriever.

Proposed change

Add a provider documentation page: docs/docs/integrations/providers/cala.mdx

The file follows the existing provider page format and includes:

  • A short description of Cala
  • Installation instructions (pip install langchain-cala)
  • Usage examples for search, structured query, and RAG chain modes

Links

Use Case

Developers building RAG pipelines with LangChain currently rely on web-search retrievers (Tavily, Exa) that return unverified, unstructured content. This forces them to add extra validation layers or accept hallucination risk in production.

Cala solves this directly: it returns verified, source-cited, entity-structured facts via a simple API, with full traceability compatible with EU AI Act Article 13 requirements.

A native CalaRetriever in the LangChain integrations hub means any developer building a knowledge-grounded agent can do pip install langchain-cala and drop it into their existing chain — no scraping, no validation overhead, no hallucination risk from stale web content.

Proposed Solution

No response

Alternatives Considered

No response

Additional Context

No response

extent analysis

Fix Plan

To add a provider documentation page for Cala, follow these steps:

  • Create a new file cala.mdx in the docs/docs/integrations/providers directory.
  • Add a short description of Cala, including its benefits and features.
  • Provide installation instructions using pip install langchain-cala.
  • Include usage examples for search, structured query, and RAG chain modes.

Example cala.mdx content:

# Cala
Cala is a verified knowledge graph API for AI agents, returning structured, source-cited, traceable facts.

## Installation
To use Cala with LangChain, install the `langchain-cala` package:
```bash
pip install langchain-cala

Usage

Search

from langchain import CalaRetriever

retriever = CalaRetriever()
results = retriever.search("example query")

Structured Query

from langchain import CalaRetriever

retriever = CalaRetriever()
results = retriever.query("example query", structured=True)

RAG Chain Mode

from langchain import CalaRetriever, LLMChain

retriever = CalaRetriever()
llm = LLMChain(llm=CalaRetriever())
results = llm({"input": "example query"})

Verification

To verify the fix, check that the cala.mdx file is correctly rendered in the LangChain documentation and that the usage examples work as expected.

Extra Tips

  • Make sure to update the langchain-cala package to the latest version.
  • Consider adding more usage examples and documentation for advanced features.
  • Review the EU AI Act Article 13 requirements to ensure compliance.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…
#api#ssr#installation#tensor shape#autograd error#tool integration#LLM response

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING

langchain - 💡(How to fix) Fix Add Cala verified knowledge retriever to integrations docs