langchain-cala 0.1.0

LangChain integration for Cala — the verified knowledge graph API for AI agents

langchain-cala

LangChain retriever for Cala — the verified knowledge graph API for AI agents.

Cala returns structured, typed, source-cited data instead of raw web scrapes. Every document includes source and verified metadata fields so your agent can reason over facts with full traceability.

pip install langchain-cala

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Why Cala?

Web search APIs	Cala
Returns URLs + scraped HTML	Returns structured, typed JSON
Agent must parse, deduplicate, guess	Agent reasons directly over clean data
No source tracing	Every fact cites its source
Hallucination risk	Verified entity graph

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Quickstart

from langchain_cala import CalaRetriever

retriever = CalaRetriever(api_key="YOUR_CALA_API_KEY")
docs = retriever.invoke("Latest funding rounds for Barcelona AI startups")

for doc in docs:
    print(doc.page_content)
    print(doc.metadata["source"])

Get your API key at console.cala.ai.
You can also set it as an environment variable: export CALA_API_KEY=your-key

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Modes

CalaRetriever supports three modes matching Cala's API:

"search" (default) — Natural language questions

Ask a free-text question and get a synthesised answer with source citations.

retriever = CalaRetriever(api_key="...", mode="search")
docs = retriever.invoke("Who are the key investors in European AI infrastructure?")

# docs[0] → synthesised answer
# docs[1:] → source citations with URLs

"query" — Structured dot-notation queries

Query the entity graph directly using Cala's dot-notation syntax.

retriever = CalaRetriever(api_key="...", mode="query")
docs = retriever.invoke("OpenAI.founded.year")
# → "2015"

docs = retriever.invoke("Mistral.headquarters.city")
# → "Paris"

"entities" — Entity discovery

Find and explore entities by name across companies, people, products, research papers, laws, and places.

retriever = CalaRetriever(api_key="...", mode="entities")
docs = retriever.invoke("Factorial HR")
# → Entity card: name, type, description, fields (funding, location, founded…)

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Parameters

Parameter	Type	Default	Description
api_key	str	CALA_API_KEY env	Your Cala API key
mode	"search" | "query" | "entities"	"search"	API mode
k	int	5	Max documents returned
timeout	int	30	Request timeout (seconds)

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Use in a RAG chain

from langchain_cala import CalaRetriever
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

retriever = CalaRetriever(api_key="...")

prompt = ChatPromptTemplate.from_template("""
Answer the question based only on the context below.
Every fact in your answer must be traceable to a source.

Context:
{context}

Question: {question}
""")

def format_docs(docs):
    return "\n\n".join(
        f"{doc.page_content}\n[Source: {doc.metadata.get('source', 'cala')}]"
        for doc in docs
    )

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)

answer = chain.invoke("Which EU AI startups raised Series A in 2024?")
print(answer)

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Use as a LangGraph tool

from langchain_cala import CalaRetriever
from langchain_core.tools import create_retriever_tool

retriever = CalaRetriever(api_key="...")

cala_tool = create_retriever_tool(
    retriever,
    name="cala_knowledge",
    description=(
        "Search Cala's verified knowledge graph for structured, "
        "source-cited facts about companies, people, funding, research, and more. "
        "Returns typed data your agent can reason over immediately."
    ),
)

# Add to your LangGraph agent's tools list
tools = [cala_tool, ...]

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Document metadata

Every Document returned includes metadata for full traceability:

{
    "source": "https://techcrunch.com/...",   # or "cala:knowledge_search"
    "verified": True,
    "type": "answer" | "citation" | "structured_result" | "entity",
    "query": "original query string",
    # mode-specific extras:
    "entity": "OpenAI",          # query mode
    "field": "founded.year",     # query mode
    "entity_id": "ent_xxx",      # entities mode
    "entity_type": "Company",    # entities mode
}

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Installation

pip install langchain-cala

Requirements: Python ≥ 3.9, langchain-core ≥ 0.2, requests ≥ 2.28, pydantic ≥ 2.0

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Development

git clone https://github.com/your-username/langchain-cala
cd langchain-cala
pip install -e ".[dev]"
pytest tests/ -v

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Links

• Cala documentation
• Get an API key
• LangChain integrations directory
• Report an issue

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License

MIT