diff --git a/providers/common/ai/docs/hooks/index.rst b/providers/common/ai/docs/hooks/index.rst
new file mode 100644
index 0000000000000..3d05ba8edae13
--- /dev/null
+++ b/providers/common/ai/docs/hooks/index.rst
@@ -0,0 +1,51 @@
+ .. Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ .. http://www.apache.org/licenses/LICENSE-2.0
+
+ .. Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+Common AI Hooks
+===============
+
+The common-ai provider ships hooks that bridge an Airflow connection to a specific
+LLM framework's model objects. Each hook is a thin adapter: it reads credentials and
+config from the connection, then returns native framework objects (a ``pydantic_ai``
+``Agent`` / ``Model``, a LangChain ``BaseChatModel`` or ``Embeddings``, an MCP client,
+...). Operators and ``@task`` decorators in this provider use these hooks internally.
+
+Choosing a hook
+---------------
+
+.. list-table::
+ :header-rows: 1
+ :widths: 25 75
+
+ * - Hook
+ - When to use
+ * - :class:`~airflow.providers.common.ai.hooks.pydantic_ai.PydanticAIHook`
+ - Default for ``common.ai`` operators (``LLMOperator``, ``AgentOperator``,
+ ``LLMBranchOperator``, ...). Returns a pydantic-ai ``Agent`` / ``Model``.
+ * - :class:`~airflow.providers.common.ai.hooks.langchain.LangChainHook`
+ - Direct LangChain access for tasks that compose ``Runnable``\\s, use the
+ LangChain agent surface, or need LangChain-native chat / embedding model
+ objects. Independent of the pydantic-ai-backed operators.
+
+Hook guides
+-----------
+
+.. toctree::
+ :maxdepth: 1
+ :glob:
+
+ *
diff --git a/providers/common/ai/docs/hooks/langchain.rst b/providers/common/ai/docs/hooks/langchain.rst
new file mode 100644
index 0000000000000..e7ee2e95d7d1e
--- /dev/null
+++ b/providers/common/ai/docs/hooks/langchain.rst
@@ -0,0 +1,174 @@
+ .. Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ .. http://www.apache.org/licenses/LICENSE-2.0
+
+ .. Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+.. _howto/hook:langchain:
+
+``LangChainHook``
+=================
+
+Use :class:`~airflow.providers.common.ai.hooks.langchain.LangChainHook` to
+bridge an Airflow connection to `LangChain `__
+chat and embedding models. The hook reads credentials (API key, optional base
+URL) from the connection and returns configured LangChain model objects via
+two universal entry-point functions:
+
+- ``langchain.chat_models.init_chat_model`` for chat models, dispatching to
+ the right vendor based on the ``provider:name`` prefix.
+- ``langchain.embeddings.init_embeddings`` for embedding models, same
+ dispatch story.
+
+The hook owns its own ``langchain`` connection type so the UI is honest about
+which framework a connection configures.
+
+Chat model usage
+----------------
+
+Pass ``llm_model`` to the constructor (or set ``extra["model"]`` on the
+connection) and call ``get_chat_model()``:
+
+.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
+ :language: python
+ :start-after: [START howto_hook_langchain_chat]
+ :end-before: [END howto_hook_langchain_chat]
+
+The returned model is a LangChain ``BaseChatModel``, so it composes with the
+rest of LangChain's runnable surface
+(``ChatPromptTemplate`` / ``StrOutputParser`` / ``RunnableSequence`` / ...).
+
+Supported chat providers
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Any model identifier accepted by
+`langchain.chat_models.init_chat_model `__
+works out of the box. Common identifiers:
+
+- ``openai:gpt-4o``, ``openai:gpt-4o-mini`` -- requires ``langchain-openai``
+- ``anthropic:claude-3-7-sonnet`` -- requires ``langchain-anthropic``
+- ``groq:llama-3.3-70b-versatile`` -- requires ``langchain-groq``
+- ``mistralai:mistral-large-latest`` -- requires ``langchain-mistralai``
+- ``ollama:llama3`` -- requires ``langchain-ollama`` (point ``host`` at the Ollama URL)
+- ``deepseek:deepseek-chat`` -- requires ``langchain-deepseek``
+
+Cloud providers with non-standard auth (AWS Bedrock, Google Vertex AI, Azure
+OpenAI) are not covered by the ``api_key`` + ``base_url`` surface here and are
+deferred to per-vendor hooks (mirroring the pydantic-ai cloud-auth subclass
+pattern).
+
+Embedding model usage
+---------------------
+
+Pass ``embed_model`` to the constructor (or set ``extra["embed_model"]`` on
+the connection) and call ``get_embedding_model()``:
+
+.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
+ :language: python
+ :start-after: [START howto_hook_langchain_embedding]
+ :end-before: [END howto_hook_langchain_embedding]
+
+The same hook instance can serve both chat and embedding models when both
+identifiers are set:
+
+.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
+ :language: python
+ :start-after: [START howto_hook_langchain_chat_and_embedding]
+ :end-before: [END howto_hook_langchain_chat_and_embedding]
+
+Supported embedding providers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The hook passes ``api_key`` and (optional) ``base_url`` from the connection to
+`langchain.embeddings.init_embeddings `__.
+Providers whose embedding classes accept this kwarg shape work directly:
+
+- ``openai:text-embedding-3-small``, ``openai:text-embedding-3-large`` -- requires ``langchain-openai``
+- ``openai:`` against an OpenAI-compatible endpoint (point ``host`` at
+ Ollama / vLLM / LM Studio) -- requires ``langchain-openai``
+
+``init_embeddings`` advertises more providers (Cohere, Mistral AI, HuggingFace,
+Bedrock, Vertex AI, Azure OpenAI, ...), but their embedding classes expect
+provider-specific credential kwargs (``cohere_api_key``, AWS auth chain, GCP
+service-account, ...) rather than the generic ``api_key`` / ``base_url`` this
+hook forwards. Those are deferred to per-vendor subclasses mirroring the
+pydantic-ai pattern (``PydanticAIBedrockHook`` / ``PydanticAIVertexHook`` /
+``PydanticAIAzureHook``).
+
+Different connections for chat and embeddings
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If chat and embeddings live on different API keys (e.g. premium chat key vs
+free-tier embeddings key), pass an explicit ``embed_conn_id``. When unset it
+falls back to ``llm_conn_id``, so the common one-provider case stays simple:
+
+.. exampleinclude:: /../../ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
+ :language: python
+ :start-after: [START howto_hook_langchain_different_conns]
+ :end-before: [END howto_hook_langchain_different_conns]
+
+Connection Configuration
+------------------------
+
+The hook reads credentials from the Airflow connection of type ``langchain``:
+
+- **password** -- API key (passed as ``api_key`` to ``init_chat_model`` and
+ ``init_embeddings``).
+- **host** -- Optional base URL (passed as ``base_url``; useful for custom
+ OpenAI-compatible endpoints, Ollama, vLLM).
+- **extra** JSON -- ``{"model": "openai:gpt-4o", "embed_model": "openai:text-embedding-3-small"}``
+ to set default chat and embedding model identifiers on the connection.
+
+Parameters
+----------
+
+.. list-table::
+ :header-rows: 1
+ :widths: 25 25 50
+
+ * - Parameter
+ - Default
+ - Description
+ * - ``llm_conn_id``
+ - ``langchain_default``
+ - Airflow connection ID for the LLM provider.
+ * - ``embed_conn_id``
+ - ``None`` (falls back to ``llm_conn_id``)
+ - Optional separate Airflow connection ID for the embedding provider.
+ Useful when chat and embeddings live on different API keys; in the
+ common one-provider case, leave unset and the hook reuses ``llm_conn_id``.
+ * - ``llm_model``
+ - ``None`` (falls back to ``extra["model"]`` on the connection)
+ - Chat model identifier in ``provider:name`` form, e.g. ``openai:gpt-4o``.
+ Only required when calling ``get_chat_model()``.
+ * - ``embed_model``
+ - ``None`` (falls back to ``extra["embed_model"]`` on the connection)
+ - Embedding model identifier in ``provider:name`` form, e.g.
+ ``openai:text-embedding-3-small``. Only required when calling
+ ``get_embedding_model()``.
+
+Dependencies
+------------
+
+Install the ``langchain`` extra to use this hook::
+
+ pip install apache-airflow-providers-common-ai[langchain]
+
+That extra installs only ``langchain`` itself, since the framework is
+vendor-agnostic. Install the LangChain integration package for whichever
+provider(s) you intend to use:
+
+- ``langchain-openai`` -- OpenAI and OpenAI-compatible endpoints (Ollama, vLLM)
+- ``langchain-anthropic`` -- Anthropic
+- ``langchain-groq``, ``langchain-mistralai``, ``langchain-deepseek``, ``langchain-ollama``, ...
diff --git a/providers/common/ai/docs/index.rst b/providers/common/ai/docs/index.rst
index e96ba4cfd27c9..eb98243f43710 100644
--- a/providers/common/ai/docs/index.rst
+++ b/providers/common/ai/docs/index.rst
@@ -36,7 +36,7 @@
Connection types
MCP connection
- Hooks
+ Hooks
Toolsets
Operators
HITL Review
diff --git a/providers/common/ai/provider.yaml b/providers/common/ai/provider.yaml
index 2a13392ea99b4..82e34ec808220 100644
--- a/providers/common/ai/provider.yaml
+++ b/providers/common/ai/provider.yaml
@@ -48,6 +48,9 @@ integrations:
- integration-name: MCP Server
external-doc-url: https://modelcontextprotocol.io/
tags: [ai]
+ - integration-name: LangChain
+ external-doc-url: https://python.langchain.com/
+ tags: [ai]
hooks:
- integration-name: Pydantic AI
@@ -56,6 +59,9 @@ hooks:
- integration-name: MCP Server
python-modules:
- airflow.providers.common.ai.hooks.mcp
+ - integration-name: LangChain
+ python-modules:
+ - airflow.providers.common.ai.hooks.langchain
plugins:
- name: hitl_review
@@ -313,6 +319,39 @@ connection-types:
type:
- string
- 'null'
+ - hook-class-name: airflow.providers.common.ai.hooks.langchain.LangChainHook
+ hook-name: "LangChain"
+ connection-type: langchain
+ ui-field-behaviour:
+ hidden-fields:
+ - schema
+ - port
+ - login
+ relabeling:
+ password: API Key
+ placeholders:
+ host: "https://api.openai.com/v1 (optional, for custom endpoints / Ollama)"
+ conn-fields:
+ model:
+ label: Chat Model
+ description: >
+ Chat model in provider:name format dispatched via
+ langchain.chat_models.init_chat_model
+ (e.g. openai:gpt-4o, anthropic:claude-3-7-sonnet).
+ schema:
+ type:
+ - string
+ - 'null'
+ embed_model:
+ label: Embedding Model
+ description: >
+ Embedding model in provider:name format dispatched via
+ langchain.embeddings.init_embeddings
+ (e.g. openai:text-embedding-3-small, cohere:embed-english-v3.0).
+ schema:
+ type:
+ - string
+ - 'null'
operators:
- integration-name: Common AI
diff --git a/providers/common/ai/pyproject.toml b/providers/common/ai/pyproject.toml
index 57ba93f7461b2..6cc98b12eeba6 100644
--- a/providers/common/ai/pyproject.toml
+++ b/providers/common/ai/pyproject.toml
@@ -95,6 +95,9 @@ dependencies = [
"common.sql" = [
"apache-airflow-providers-common-sql"
]
+"langchain" = [
+ "langchain>=1.0.0",
+]
[dependency-groups]
dev = [
@@ -107,7 +110,8 @@ dev = [
# Additional devel dependencies (do not remove this line and add extra development dependencies)
"sqlglot>=30.0.0",
"pydantic-ai-slim[mcp]",
- "apache-airflow-providers-common-sql[datafusion]"
+ "apache-airflow-providers-common-sql[datafusion]",
+ "langchain>=1.0.0",
]
# To build docs:
diff --git a/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
new file mode 100644
index 0000000000000..49d6b790b87c2
--- /dev/null
+++ b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_hook.py
@@ -0,0 +1,131 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Example DAGs demonstrating LangChainHook usage patterns.
+
+Each DAG covers a single pattern: chat-only, embedding-only, dual chat +
+embedding, and separate connections for chat and embeddings. For a richer
+end-to-end demo (ReAct agent, HITL review, vector retrieval), see
+``example_langchain_tool_agent.py``.
+"""
+
+from __future__ import annotations
+
+from airflow.providers.common.ai.hooks.langchain import LangChainHook
+from airflow.providers.common.compat.sdk import dag, task
+
+
+# [START howto_hook_langchain_chat]
+@dag(schedule=None)
+def example_langchain_chat():
+ @task
+ def summarize(text: str) -> str:
+ hook = LangChainHook(
+ llm_conn_id="langchain_default",
+ llm_model="openai:gpt-4o",
+ )
+ llm = hook.get_chat_model()
+ # LangChain BaseMessage.content is `str | list[...]` (multi-modal union);
+ # coerce to str for the text-only path this example demonstrates.
+ return str(llm.invoke(f"Summarize concisely: {text}").content)
+
+ summarize("Apache Airflow is a platform for authoring, scheduling, and monitoring workflows.")
+
+
+# [END howto_hook_langchain_chat]
+
+example_langchain_chat()
+
+
+# [START howto_hook_langchain_embedding]
+@dag(schedule=None)
+def example_langchain_embedding():
+ @task
+ def embed_documents(texts: list[str]) -> int:
+ hook = LangChainHook(
+ llm_conn_id="langchain_default",
+ embed_model="openai:text-embedding-3-small",
+ )
+ embeddings = hook.get_embedding_model()
+ vectors = embeddings.embed_documents(texts)
+ return len(vectors[0])
+
+ embed_documents(
+ [
+ "Apache Airflow is a workflow orchestrator.",
+ "Workflows are defined as Python DAGs.",
+ ]
+ )
+
+
+# [END howto_hook_langchain_embedding]
+
+example_langchain_embedding()
+
+
+# [START howto_hook_langchain_chat_and_embedding]
+@dag(schedule=None)
+def example_langchain_chat_and_embedding():
+ """One hook instance serves both chat and embeddings when both models are set."""
+
+ @task
+ def use_both() -> dict:
+ hook = LangChainHook(
+ llm_conn_id="langchain_default",
+ llm_model="openai:gpt-4o",
+ embed_model="openai:text-embedding-3-small",
+ )
+ chat = hook.get_chat_model()
+ embeddings = hook.get_embedding_model()
+ return {
+ "answer": str(chat.invoke("In one sentence: what does Airflow do?").content),
+ "embedding_dim": len(embeddings.embed_query("Airflow")),
+ }
+
+ use_both()
+
+
+# [END howto_hook_langchain_chat_and_embedding]
+
+example_langchain_chat_and_embedding()
+
+
+# [START howto_hook_langchain_different_conns]
+@dag(schedule=None)
+def example_langchain_different_conns():
+ """Use separate connections when chat and embeddings live on different API keys."""
+
+ @task
+ def use_separate_conns() -> dict:
+ hook = LangChainHook(
+ llm_conn_id="openai_chat",
+ embed_conn_id="openai_embed",
+ llm_model="openai:gpt-4o",
+ embed_model="openai:text-embedding-3-small",
+ )
+ chat = hook.get_chat_model()
+ embeddings = hook.get_embedding_model()
+ return {
+ "answer": str(chat.invoke("In one sentence: what does Airflow do?").content),
+ "embedding_dim": len(embeddings.embed_query("Airflow")),
+ }
+
+ use_separate_conns()
+
+
+# [END howto_hook_langchain_different_conns]
+
+example_langchain_different_conns()
diff --git a/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_tool_agent.py b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_tool_agent.py
new file mode 100644
index 0000000000000..be84c5f78a018
--- /dev/null
+++ b/providers/common/ai/src/airflow/providers/common/ai/example_dags/example_langchain_tool_agent.py
@@ -0,0 +1,542 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+ReAct tool-calling agent with LangChain -- research and report pipeline.
+
+Demonstrates the "agent as a task" pattern using a LangChain ReAct agent
+that autonomously decides which tools to call, composed with common.ai's
+:class:`~airflow.providers.common.ai.operators.llm.LLMOperator` for report
+formatting and AIP-90 HITL operators for human review.
+
+Unlike RAG examples (fixed pipeline: retrieve then synthesize), this
+agent's tool-call sequence is determined by the LLM at runtime. The agent
+might call zero tools or ten tools depending on the question. This is the
+canonical "agent as a task" pattern: Airflow handles scheduling, retry,
+connections, and the surrounding workflow; the LangChain agent handles
+internal reasoning.
+
+``example_langchain_tool_agent`` (manual trigger):
+
+.. code-block:: text
+
+ prompt_review (HITLEntryOperator)
+ -> prepare_tools (@task)
+ -> run_research_agent (@task)
+ -> format_report (LLMOperator)
+ -> report_approval (ApprovalOperator)
+
+**What this makes visible that running an agent alone hides:**
+
+* The question goes through human review before the agent runs.
+* The agent's raw findings are a visible XCom value between tasks.
+* Report formatting is a separate, independently retryable LLM call.
+* The formatted report requires human approval before delivery.
+
+**Contrast with AIP-99's AgentOperator:**
+
+AIP-99's ``AgentOperator`` uses PydanticAI for agent execution. This
+example uses LangChain's ``create_agent`` with LangChain-native ``@tool``
+definitions. Users with existing LangChain tools (700+ integrations)
+can use them directly without rewriting as PydanticAI tools.
+
+Before running:
+
+1. Install LangChain packages::
+
+ pip install langchain langchain-openai langchain-text-splitters \\
+ langchain-community faiss-cpu
+
+2. Create an LLM connection of type ``langchain`` named ``langchain_default``
+ (or the value of ``LLM_CONN_ID`` below) for your chosen model provider.
+ Set ``password`` to your API key; the ``host`` field is optional and only
+ needed for custom endpoints / Ollama.
+
+3. Optionally place a knowledge base directory at ``DOCS_PATH`` and a
+ survey CSV at ``SURVEY_CSV_PATH``. If ``DOCS_PATH`` is empty, sample
+ documents about Apache Airflow are created automatically.
+"""
+
+from __future__ import annotations
+
+import datetime
+import os
+
+from airflow.providers.common.ai.operators.llm import LLMOperator
+from airflow.providers.common.compat.sdk import dag, task
+from airflow.providers.standard.operators.hitl import ApprovalOperator, HITLEntryOperator
+from airflow.sdk import Param
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+LLM_CONN_ID = "langchain_default"
+LLM_MODEL = os.environ.get("LLM_MODEL", "openai:gpt-4o")
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "openai:text-embedding-3-small")
+
+DOCS_PATH = os.environ.get("DOCS_PATH", "/opt/airflow/data/rag_documents")
+
+SURVEY_CSV_PATH = os.environ.get(
+ "SURVEY_CSV_PATH",
+ "/opt/airflow/data/airflow-user-survey-2025.csv",
+)
+
+INDEX_PERSIST_DIR = os.environ.get("INDEX_PERSIST_DIR", "/opt/airflow/data/langchain_faiss_index")
+
+DEFAULT_QUESTION = (
+ "What percentage of Airflow users are on Kubernetes? "
+ "Also check what the documentation says about the KubernetesExecutor."
+)
+
+SAMPLE_DOCUMENTS = {
+ "apache_airflow_overview.txt": (
+ "Apache Airflow is an open-source platform for programmatically authoring, "
+ "scheduling, and monitoring workflows. Originally created at Airbnb in 2014, "
+ "it graduated from the Apache Incubator in 2019. Airflow uses directed acyclic "
+ "graphs (DAGs) to define workflows as Python code, making pipelines versionable, "
+ "testable, and collaborative. The scheduler executes tasks on workers following "
+ "the defined dependencies. Airflow is widely used for ETL/ELT pipelines, ML model "
+ "training orchestration, and data warehouse management. As of Airflow 3.0, workers "
+ "communicate exclusively through the Execution API and never access the metadata "
+ "database directly, strengthening security and enabling horizontal scaling."
+ ),
+ "kubernetes_executor.txt": (
+ "The KubernetesExecutor runs each Airflow task as a separate Kubernetes pod. "
+ "This provides strong isolation between tasks, dynamic resource allocation, and "
+ "the ability to use different Docker images per task. When a task is scheduled, "
+ "the executor creates a pod spec, submits it to the Kubernetes API, and monitors "
+ "the pod until completion. Resource requests and limits can be set per task via "
+ "executor_config. The KubernetesExecutor is recommended for heterogeneous "
+ "workloads where tasks have different resource requirements or dependencies. "
+ "It scales to zero when no tasks are running, reducing infrastructure costs. "
+ "In Airflow 3.0, pod specs are submitted via the Execution API."
+ ),
+ "operators_and_hooks.txt": (
+ "Operators are the building blocks of Airflow tasks. Each operator defines a "
+ "single unit of work: BashOperator runs shell commands, PythonOperator executes "
+ "Python callables, and provider-specific operators interact with external systems "
+ "(S3, BigQuery, Spark, etc.). Hooks are the connection layer between operators "
+ "and external services. A hook manages authentication and provides methods to "
+ "interact with a specific service. For example, S3Hook provides methods to read "
+ "and write S3 objects, while PostgresHook connects to PostgreSQL databases."
+ ),
+ "connections_and_variables.txt": (
+ "Connections store credentials and endpoint information for external services. "
+ "Each connection has a type (e.g., postgres, aws, http), login, password, host, "
+ "port, schema, and an extras JSON field for additional parameters. In Airflow 3.0, "
+ "workers access connections through the Execution API using short-lived JWT tokens "
+ "scoped to the running task instance. Variables are key-value pairs for storing "
+ "configuration that may change between environments."
+ ),
+ "ai_operators.txt": (
+ "Airflow's common.ai provider (AIP-99) adds first-class AI/LLM support. "
+ "LLMOperator sends a prompt to any supported LLM and returns text or structured "
+ "output via Pydantic models. AgentOperator runs multi-turn reasoning with tools "
+ "(SQL, HTTP, MCP servers). LLMBranchOperator uses an LLM to choose downstream "
+ "task branches. All operators support human-in-the-loop review, durable execution "
+ "for long-running agents, usage limits for cost control, and connect to 20+ model "
+ "providers through Airflow connections."
+ ),
+}
+
+REPORT_SYSTEM_PROMPT = (
+ "You are a technical report writer. Format the research findings into a "
+ "clear, well-structured report with sections and bullet points. Cite "
+ "sources when available. Be concise but thorough."
+)
+
+
+# ---------------------------------------------------------------------------
+# Helper: build or load the knowledge base FAISS index
+# ---------------------------------------------------------------------------
+
+
+def _ensure_knowledge_base(hook) -> str:
+ """Build a FAISS index from sample docs if it does not already exist.
+
+ Returns the persist directory path.
+ """
+ from langchain_community.vectorstores import FAISS
+ from langchain_core.documents import Document
+ from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+ if os.path.exists(os.path.join(INDEX_PERSIST_DIR, "index.faiss")):
+ return INDEX_PERSIST_DIR
+
+ os.makedirs(DOCS_PATH, exist_ok=True)
+ for filename, content in SAMPLE_DOCUMENTS.items():
+ filepath = os.path.join(DOCS_PATH, filename)
+ if not os.path.exists(filepath):
+ with open(filepath, "w", encoding="utf-8") as f:
+ f.write(content)
+
+ docs = []
+ for filename in sorted(os.listdir(DOCS_PATH)):
+ if not filename.endswith((".txt", ".md")):
+ continue
+ with open(os.path.join(DOCS_PATH, filename), encoding="utf-8") as f:
+ docs.append(Document(page_content=f.read(), metadata={"source": filename}))
+
+ splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+ chunks = splitter.split_documents(docs)
+
+ vectorstore = FAISS.from_documents(chunks, hook.get_embedding_model())
+
+ os.makedirs(INDEX_PERSIST_DIR, exist_ok=True)
+ vectorstore.save_local(INDEX_PERSIST_DIR)
+ print(f"Built FAISS index: {len(chunks)} chunks in {INDEX_PERSIST_DIR}")
+ return INDEX_PERSIST_DIR
+
+
+# ---------------------------------------------------------------------------
+# Tool definitions (LangChain @tool decorator)
+# ---------------------------------------------------------------------------
+
+
+def _build_tools(hook, index_dir: str, survey_csv_path: str) -> list:
+ """Construct the agent's tool set."""
+ from langchain.tools import tool
+ from langchain_community.vectorstores import FAISS
+
+ # Build the vector store once and close over it -- the agent may invoke
+ # search_knowledge_base many times, and reloading the FAISS index plus
+ # re-initialising the embedding model on every call would be wasteful.
+ vectorstore = FAISS.load_local(
+ index_dir, hook.get_embedding_model(), allow_dangerous_deserialization=True
+ )
+
+ # -- Tool 1: Knowledge base search (vector retrieval) ------------------
+
+ @tool
+ def search_knowledge_base(query: str) -> str:
+ """Search the internal knowledge base for relevant documentation.
+
+ Use this for questions about Airflow features, architecture,
+ operators, executors, connections, or best practices.
+ """
+ results = vectorstore.similarity_search(query, k=3)
+
+ if not results:
+ return "No relevant documents found in the knowledge base."
+
+ formatted = []
+ for i, doc in enumerate(results, 1):
+ source = doc.metadata.get("source", "unknown")
+ formatted.append(f"[{i}] Source: {source}\n{doc.page_content}")
+ return "\n\n".join(formatted)
+
+ # -- Tool 2: Survey data query ----------------------------------------
+
+ @tool
+ def query_survey_data(question: str) -> str:
+ """Query the Airflow user survey dataset to answer questions about
+ Airflow adoption, usage patterns, executor choices, deployment
+ methods, cloud providers, and user demographics.
+
+ Pass a natural language question. The tool converts it to SQL
+ and executes it against the survey data.
+ """
+ import csv
+
+ if not os.path.exists(survey_csv_path):
+ return (
+ "Survey data not available. The CSV file was not found at "
+ f"{survey_csv_path}. Continuing with other tools."
+ )
+
+ with open(survey_csv_path, encoding="utf-8") as f:
+ reader = csv.DictReader(f)
+ rows = list(reader)
+
+ if not rows:
+ return "Survey data is empty."
+
+ columns = list(rows[0].keys())
+ total = len(rows)
+ summary_parts = [f"Survey has {total} responses with columns:"]
+ summary_parts.append(", ".join(columns[:15]))
+ if len(columns) > 15:
+ summary_parts.append(f"... and {len(columns) - 15} more columns")
+
+ q_lower = question.lower()
+
+ if "kubernetes" in q_lower or "k8s" in q_lower:
+ k8s_col = next(
+ (c for c in columns if "kubernetes" in c.lower()),
+ None,
+ )
+ if k8s_col:
+ k8s_users = sum(1 for r in rows if r.get(k8s_col, "").strip())
+ pct = round(100 * k8s_users / total, 1) if total else 0
+ return (
+ f"KubernetesExecutor usage: {k8s_users} of {total} "
+ f"respondents ({pct}%) indicated they use KubernetesExecutor."
+ )
+
+ if "celery" in q_lower:
+ celery_col = next(
+ (c for c in columns if "celery" in c.lower()),
+ None,
+ )
+ if celery_col:
+ celery_users = sum(1 for r in rows if r.get(celery_col, "").strip())
+ pct = round(100 * celery_users / total, 1) if total else 0
+ return (
+ f"CeleryExecutor usage: {celery_users} of {total} "
+ f"respondents ({pct}%) indicated they use CeleryExecutor."
+ )
+
+ if "version" in q_lower:
+ version_col = next(
+ (c for c in columns if "version" in c.lower() and "airflow" in c.lower()),
+ None,
+ )
+ if version_col:
+ from collections import Counter
+
+ counts = Counter(r.get(version_col, "unknown") for r in rows)
+ top5 = counts.most_common(5)
+ lines = [f" {v}: {c} ({round(100 * c / total, 1)}%)" for v, c in top5]
+ return "Airflow version distribution (top 5):\n" + "\n".join(lines)
+
+ return (
+ f"Survey dataset has {total} responses across {len(columns)} columns. "
+ "Available topics: executor usage (Kubernetes, Celery, Local), "
+ "Airflow versions, deployment methods, cloud providers, company "
+ "size, industries, AI tool usage. Ask a more specific question."
+ )
+
+ # -- Tool 3: Web search (simulated) ------------------------------------
+
+ @tool
+ def search_web(query: str) -> str:
+ """Search the web for current information, news, or context.
+
+ Use this for questions that need up-to-date external information
+ not available in the knowledge base or survey data.
+ """
+ responses = {
+ "kubernetes airflow": (
+ "Recent blog posts indicate KubernetesExecutor adoption has grown "
+ "significantly since Airflow 2.0, with many large-scale deployments "
+ "migrating from CeleryExecutor. Key advantages cited: pod-level "
+ "isolation, dynamic scaling, and per-task resource configuration. "
+ "Source: Airflow blog, Astronomer blog (2025-2026)."
+ ),
+ "airflow 3": (
+ "Airflow 3.0 shipped in early 2026 with major architectural changes: "
+ "Execution API (workers never access metadata DB directly), multi-team "
+ "isolation, improved UI, and the common.ai provider for AI/LLM support. "
+ "Source: airflow.apache.org release notes."
+ ),
+ "airflow adoption": (
+ "The 2025 Airflow User Survey showed continued growth: 2,000+ responses, "
+ "40% of respondents at companies with 1,000+ employees, 35% using cloud-managed "
+ "Airflow (Astronomer, MWAA, Cloud Composer). Source: Airflow blog."
+ ),
+ }
+
+ for keyword, response in responses.items():
+ if any(w in query.lower() for w in keyword.split()):
+ return response
+
+ return (
+ f"Web search for '{query}' returned general results. "
+ "For this demo, web search is simulated with canned responses. "
+ "In production, use Tavily, Serper, or another search API "
+ "configured via an Airflow connection."
+ )
+
+ # -- Tool 4: Current UTC time -----------------------------------------
+
+ @tool
+ def get_current_utc_time() -> str:
+ """Return the current UTC date and time in ISO-8601 format.
+
+ Use when the question depends on a current timestamp (e.g. "is the
+ merge freeze active right now", "how recent is the survey data").
+ LLMs cannot reliably know the wall-clock time on their own.
+ """
+ return datetime.datetime.now(datetime.timezone.utc).isoformat(timespec="seconds")
+
+ return [search_knowledge_base, query_survey_data, search_web, get_current_utc_time]
+
+
+# ---------------------------------------------------------------------------
+# DAG: ReAct tool-calling agent with human review
+# ---------------------------------------------------------------------------
+
+
+# [START example_langchain_tool_agent]
+@dag
+def example_langchain_tool_agent():
+ """
+ Research agent with LangChain tools and human review.
+
+ Task graph::
+
+ prompt_review (HITLEntryOperator)
+ -> prepare_tools (@task)
+ -> run_research_agent (@task)
+ -> format_report (LLMOperator)
+ -> report_approval (ApprovalOperator)
+
+ The agent uses LangChain's ``create_agent`` with a ReAct reasoning
+ loop. It autonomously decides which tools to call -- knowledge base
+ search, survey data query, web search, or current-time lookup --
+ based on the user's question. The number and sequence of tool calls
+ is determined by the LLM at runtime.
+
+ The surrounding Airflow DAG provides what the agent cannot:
+ human review of the question (HITLEntryOperator), formatted report
+ generation (LLMOperator), and human approval of the final output
+ (ApprovalOperator).
+ """
+
+ prompt_review = HITLEntryOperator(
+ task_id="prompt_review",
+ subject="Review the research question",
+ params={
+ "question": Param(
+ DEFAULT_QUESTION,
+ type="string",
+ description="The research question for the agent to investigate",
+ ),
+ },
+ response_timeout=datetime.timedelta(hours=1),
+ )
+
+ @task
+ def prepare_tools(hitl_response: dict) -> dict:
+ """Build the FAISS knowledge base index and resolve tool config."""
+ from airflow.providers.common.ai.hooks.langchain import LangChainHook
+
+ hook = LangChainHook(
+ llm_conn_id=LLM_CONN_ID,
+ llm_model=LLM_MODEL,
+ embed_model=EMBEDDING_MODEL,
+ )
+ index_dir = _ensure_knowledge_base(hook)
+
+ question = hitl_response["params_input"]["question"]
+ return {
+ "question": question,
+ "index_dir": index_dir,
+ "survey_csv_path": SURVEY_CSV_PATH,
+ }
+
+ @task
+ def run_research_agent(config: dict) -> dict:
+ """Run a LangChain ReAct agent that autonomously researches the question.
+
+ The agent decides which tools to call and in what order. The number
+ of tool calls depends on the complexity of the question. All
+ reasoning steps, tool calls, and observations are logged.
+ """
+ from langchain.agents import create_agent
+
+ from airflow.providers.common.ai.hooks.langchain import LangChainHook
+
+ hook = LangChainHook(
+ llm_conn_id=LLM_CONN_ID,
+ llm_model=LLM_MODEL,
+ embed_model=EMBEDDING_MODEL,
+ )
+ model = hook.get_chat_model()
+ tools = _build_tools(hook, config["index_dir"], config["survey_csv_path"])
+
+ agent = create_agent(
+ model,
+ tools=tools,
+ system_prompt=(
+ "You are a thorough research assistant for Apache Airflow. "
+ "You have access to tools for searching a knowledge base, "
+ "querying survey data, searching the web, and checking the "
+ "current UTC time. "
+ "Use the appropriate tools to fully answer the question. "
+ "Combine information from multiple sources when relevant. "
+ "Always cite which tool provided each piece of information."
+ ),
+ )
+
+ question = config["question"]
+ print(f"Research question: {question}")
+ print("Agent starting research...")
+
+ tool_calls_log = []
+ final_answer = ""
+
+ for step in agent.stream(
+ {"messages": [{"role": "user", "content": question}]},
+ stream_mode="values",
+ ):
+ msg = step["messages"][-1]
+ if hasattr(msg, "tool_calls") and msg.tool_calls:
+ for tc in msg.tool_calls:
+ tool_calls_log.append(
+ {
+ "tool": tc["name"],
+ "args": str(tc.get("args", {}))[:200],
+ }
+ )
+ print(f" Tool call: {tc['name']}({tc.get('args', {})})")
+ elif hasattr(msg, "content") and msg.content:
+ final_answer = msg.content
+
+ print(f"Agent completed. Tool calls made: {len(tool_calls_log)}")
+
+ return {
+ "question": question,
+ "findings": final_answer,
+ "tool_calls": tool_calls_log,
+ "tool_call_count": len(tool_calls_log),
+ }
+
+ tools_config = prepare_tools(prompt_review.output)
+ research_result = run_research_agent(tools_config)
+
+ format_report = LLMOperator(
+ task_id="format_report",
+ llm_conn_id=LLM_CONN_ID,
+ system_prompt=REPORT_SYSTEM_PROMPT,
+ prompt="""\
+Format the following research findings into a clear report.
+
+{% set result = ti.xcom_pull(task_ids='run_research_agent') -%}
+Question: {{ result['question'] }}
+
+Raw findings:
+{{ result['findings'] }}
+
+Tools used: {{ result['tool_call_count'] }} calls
+{% for tc in result['tool_calls'] -%}
+ - {{ tc['tool'] }}: {{ tc['args'][:100] }}
+{% endfor -%}""",
+ )
+ research_result >> format_report
+
+ report_approval = ApprovalOperator( # noqa: F841
+ task_id="report_approval",
+ subject="Review the research report",
+ body=format_report.output,
+ response_timeout=datetime.timedelta(hours=1),
+ )
+
+
+# [END example_langchain_tool_agent]
+
+example_langchain_tool_agent()
diff --git a/providers/common/ai/src/airflow/providers/common/ai/get_provider_info.py b/providers/common/ai/src/airflow/providers/common/ai/get_provider_info.py
index b45714dbb4ea9..7cb8513f9e606 100644
--- a/providers/common/ai/src/airflow/providers/common/ai/get_provider_info.py
+++ b/providers/common/ai/src/airflow/providers/common/ai/get_provider_info.py
@@ -50,6 +50,11 @@ def get_provider_info():
"external-doc-url": "https://modelcontextprotocol.io/",
"tags": ["ai"],
},
+ {
+ "integration-name": "LangChain",
+ "external-doc-url": "https://python.langchain.com/",
+ "tags": ["ai"],
+ },
],
"hooks": [
{
@@ -57,6 +62,10 @@ def get_provider_info():
"python-modules": ["airflow.providers.common.ai.hooks.pydantic_ai"],
},
{"integration-name": "MCP Server", "python-modules": ["airflow.providers.common.ai.hooks.mcp"]},
+ {
+ "integration-name": "LangChain",
+ "python-modules": ["airflow.providers.common.ai.hooks.langchain"],
+ },
],
"plugins": [
{
@@ -254,6 +263,30 @@ def get_provider_info():
},
},
},
+ {
+ "hook-class-name": "airflow.providers.common.ai.hooks.langchain.LangChainHook",
+ "hook-name": "LangChain",
+ "connection-type": "langchain",
+ "ui-field-behaviour": {
+ "hidden-fields": ["schema", "port", "login"],
+ "relabeling": {"password": "API Key"},
+ "placeholders": {
+ "host": "https://api.openai.com/v1 (optional, for custom endpoints / Ollama)"
+ },
+ },
+ "conn-fields": {
+ "model": {
+ "label": "Chat Model",
+ "description": "Chat model in provider:name format dispatched via langchain.chat_models.init_chat_model (e.g. openai:gpt-4o, anthropic:claude-3-7-sonnet).\n",
+ "schema": {"type": ["string", "null"]},
+ },
+ "embed_model": {
+ "label": "Embedding Model",
+ "description": "Embedding model in provider:name format dispatched via langchain.embeddings.init_embeddings (e.g. openai:text-embedding-3-small, cohere:embed-english-v3.0).\n",
+ "schema": {"type": ["string", "null"]},
+ },
+ },
+ },
],
"operators": [
{
diff --git a/providers/common/ai/src/airflow/providers/common/ai/hooks/langchain.py b/providers/common/ai/src/airflow/providers/common/ai/hooks/langchain.py
new file mode 100644
index 0000000000000..81834a83764b3
--- /dev/null
+++ b/providers/common/ai/src/airflow/providers/common/ai/hooks/langchain.py
@@ -0,0 +1,173 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Hook for LangChain integration with Airflow connections."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from airflow.providers.common.compat.sdk import BaseHook
+
+if TYPE_CHECKING:
+ from langchain_core.embeddings import Embeddings
+ from langchain_core.language_models.chat_models import BaseChatModel
+
+
+class LangChainHook(BaseHook):
+ """
+ Bridge an Airflow connection to LangChain chat and embedding models.
+
+ The hook resolves credentials (API key, optional base URL) from the Airflow
+ connection and returns LangChain model objects via two universal entry-point
+ functions:
+
+ * :func:`langchain.chat_models.init_chat_model` dispatches to the right
+ chat-model vendor based on the model identifier.
+ * :func:`langchain.embeddings.init_embeddings` dispatches to the right
+ embedding-model vendor based on the model identifier.
+
+ Both identifiers use the ``provider:name`` format
+ (e.g. ``"openai:gpt-4o"``, ``"openai:text-embedding-3-small"``). Only
+ OpenAI-compatible providers (OpenAI itself, Anthropic, Groq, Mistral AI
+ chat, Ollama, DeepSeek, ...) work with this hook's ``api_key`` + optional
+ ``base_url`` credential surface. Providers with bespoke auth (AWS Bedrock,
+ Google Vertex AI / GenAI, Azure OpenAI, Cohere, HuggingFace) reject these
+ kwargs; per-vendor subclasses can be added later mirroring the pydantic-ai
+ pattern.
+
+ Connection fields:
+
+ * **password**: API key passed as ``api_key=`` to the model constructor.
+ * **host**: Optional base URL passed as ``base_url=`` (custom endpoints, Ollama, vLLM).
+ * **extra** JSON: ``{"model": "openai:gpt-4o", "embed_model": "openai:text-embedding-3-small"}``
+ -- default chat and embedding model identifiers.
+
+ :param llm_conn_id: Airflow connection ID for the LLM provider. Falls back
+ to :attr:`default_conn_name` (``"langchain_default"``) if not provided.
+ :param embed_conn_id: Optional separate Airflow connection ID for the
+ embedding provider. Falls back to ``llm_conn_id`` when not provided --
+ the common case of one provider for both chat and embeddings stays a
+ single hook instance.
+ :param llm_model: Chat model identifier in ``provider:name`` format
+ (e.g. ``"openai:gpt-4o"``, ``"anthropic:claude-3-7-sonnet"``).
+ Overrides ``extra["model"]`` on the connection.
+ :param embed_model: Embedding model identifier in ``provider:name`` format
+ (e.g. ``"openai:text-embedding-3-small"``). Overrides
+ ``extra["embed_model"]`` on the connection.
+ """
+
+ conn_name_attr = "llm_conn_id"
+ default_conn_name = "langchain_default"
+ conn_type = "langchain"
+ hook_name = "LangChain"
+
+ def __init__(
+ self,
+ llm_conn_id: str | None = None,
+ embed_conn_id: str | None = None,
+ llm_model: str | None = None,
+ embed_model: str | None = None,
+ **kwargs: Any,
+ ) -> None:
+ super().__init__(**kwargs)
+ # Resolve at runtime so a future subclass with its own
+ # `default_conn_name` is honoured. A bare `llm_conn_id: str =
+ # default_conn_name` would bind the *base* class value because Python
+ # evaluates default argument values at class-definition time.
+ self.llm_conn_id = llm_conn_id if llm_conn_id is not None else self.default_conn_name
+ self.embed_conn_id = embed_conn_id if embed_conn_id is not None else self.llm_conn_id
+ self.llm_model = llm_model
+ self.embed_model = embed_model
+
+ @staticmethod
+ def get_ui_field_behaviour() -> dict[str, Any]:
+ """Return custom field behaviour for the Airflow connection form."""
+ return {
+ "hidden_fields": ["schema", "port", "login"],
+ "relabeling": {"password": "API Key"},
+ "placeholders": {
+ "host": "https://api.openai.com/v1 (optional, for custom endpoints / Ollama)",
+ "extra": '{"model": "openai:gpt-4o", "embed_model": "openai:text-embedding-3-small"}',
+ },
+ }
+
+ @staticmethod
+ def _resolve_model_id(
+ conn_extra: dict[str, Any],
+ *,
+ constructor_value: str | None,
+ extra_key: str,
+ kind: str,
+ ) -> str:
+ """Resolve a model identifier from constructor arg or connection extra."""
+ model_id = constructor_value or conn_extra.get(extra_key)
+ if not model_id:
+ raise ValueError(
+ f"No {kind} model identifier set. Pass {extra_key}= to the hook "
+ f'constructor or set extra={{"{extra_key}": "provider:name"}} on '
+ "the connection."
+ )
+ return model_id
+
+ @staticmethod
+ def _connection_kwargs(conn: Any) -> dict[str, Any]:
+ """Return shared init_* kwargs (api_key, base_url) derived from the connection."""
+ kwargs: dict[str, Any] = {}
+ if conn.password:
+ kwargs["api_key"] = conn.password
+ if conn.host:
+ kwargs["base_url"] = conn.host
+ return kwargs
+
+ def get_chat_model(self) -> BaseChatModel:
+ """
+ Return a LangChain chat model configured from the Airflow connection.
+
+ Dispatch is delegated to ``init_chat_model``, which picks the right
+ vendor class based on the ``provider:name`` prefix in the model id.
+ """
+ # Lazy: langchain is an optional extra; importing at module level would
+ # break common.ai for users who haven't installed `[langchain]`.
+ from langchain.chat_models import init_chat_model
+
+ conn = self.get_connection(self.llm_conn_id)
+ model_id = self._resolve_model_id(
+ conn.extra_dejson,
+ constructor_value=self.llm_model,
+ extra_key="model",
+ kind="chat",
+ )
+ return init_chat_model(model_id, **self._connection_kwargs(conn))
+
+ def get_embedding_model(self) -> Embeddings:
+ """
+ Return a LangChain embedding model configured from the Airflow connection.
+
+ Dispatch is delegated to ``init_embeddings``, which picks the right
+ vendor class based on the ``provider:name`` prefix in the model id.
+ Uses ``embed_conn_id`` if set (falls back to ``llm_conn_id``).
+ """
+ from langchain.embeddings import init_embeddings
+
+ conn = self.get_connection(self.embed_conn_id)
+ model_id = self._resolve_model_id(
+ conn.extra_dejson,
+ constructor_value=self.embed_model,
+ extra_key="embed_model",
+ kind="embedding",
+ )
+ return init_embeddings(model_id, **self._connection_kwargs(conn))
diff --git a/providers/common/ai/tests/unit/common/ai/hooks/test_langchain.py b/providers/common/ai/tests/unit/common/ai/hooks/test_langchain.py
new file mode 100644
index 0000000000000..ed2f95819377a
--- /dev/null
+++ b/providers/common/ai/tests/unit/common/ai/hooks/test_langchain.py
@@ -0,0 +1,270 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from airflow.providers.common.ai.hooks.langchain import LangChainHook
+
+
+def _conn(password: str = "", host: str = "", extra: dict | None = None) -> MagicMock:
+ mock_conn = MagicMock()
+ mock_conn.password = password
+ mock_conn.host = host
+ mock_conn.extra_dejson = extra or {}
+ return mock_conn
+
+
+class TestLangChainHookInit:
+ def test_default_params(self):
+ hook = LangChainHook()
+ assert hook.llm_conn_id == "langchain_default"
+ assert hook.embed_conn_id == "langchain_default"
+ assert hook.llm_model is None
+ assert hook.embed_model is None
+
+ def test_embed_conn_falls_back_to_llm_conn(self):
+ hook = LangChainHook(llm_conn_id="my_conn")
+ assert hook.llm_conn_id == "my_conn"
+ assert hook.embed_conn_id == "my_conn"
+
+ def test_explicit_separate_conns_and_models(self):
+ hook = LangChainHook(
+ llm_conn_id="chat_conn",
+ embed_conn_id="embed_conn",
+ llm_model="openai:gpt-4o",
+ embed_model="openai:text-embedding-3-small",
+ )
+ assert hook.llm_conn_id == "chat_conn"
+ assert hook.embed_conn_id == "embed_conn"
+ assert hook.llm_model == "openai:gpt-4o"
+ assert hook.embed_model == "openai:text-embedding-3-small"
+
+ def test_conn_type_is_langchain(self):
+ assert LangChainHook.conn_type == "langchain"
+ assert LangChainHook.default_conn_name == "langchain_default"
+ assert LangChainHook.conn_name_attr == "llm_conn_id"
+ assert LangChainHook.hook_name == "LangChain"
+
+
+class TestGetUiFieldBehaviour:
+ def test_shape(self):
+ behaviour = LangChainHook.get_ui_field_behaviour()
+ assert behaviour["hidden_fields"] == ["schema", "port", "login"]
+ assert behaviour["relabeling"] == {"password": "API Key"}
+ assert "host" in behaviour["placeholders"]
+ assert "extra" in behaviour["placeholders"]
+ # extra placeholder shows both chat and embedding model keys
+ assert "model" in behaviour["placeholders"]["extra"]
+ assert "embed_model" in behaviour["placeholders"]["extra"]
+
+
+class TestResolveModelId:
+ def test_constructor_wins_over_extra(self):
+ result = LangChainHook._resolve_model_id(
+ {"model": "anthropic:claude-3-7-sonnet"},
+ constructor_value="openai:gpt-4o",
+ extra_key="model",
+ kind="chat",
+ )
+ assert result == "openai:gpt-4o"
+
+ def test_falls_back_to_extra(self):
+ result = LangChainHook._resolve_model_id(
+ {"model": "anthropic:claude-3-7-sonnet"},
+ constructor_value=None,
+ extra_key="model",
+ kind="chat",
+ )
+ assert result == "anthropic:claude-3-7-sonnet"
+
+ def test_raises_when_neither_set_for_chat(self):
+ with pytest.raises(ValueError, match="No chat model identifier set"):
+ LangChainHook._resolve_model_id(
+ {},
+ constructor_value=None,
+ extra_key="model",
+ kind="chat",
+ )
+
+ def test_raises_when_neither_set_for_embedding(self):
+ with pytest.raises(ValueError, match="No embedding model identifier set"):
+ LangChainHook._resolve_model_id(
+ {},
+ constructor_value=None,
+ extra_key="embed_model",
+ kind="embedding",
+ )
+
+
+class TestGetChatModel:
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_dispatches_via_init_chat_model_with_api_key(self, mock_get_conn, mock_init_chat_model):
+ mock_get_conn.return_value = _conn(password="sk-test")
+
+ hook = LangChainHook(llm_model="openai:gpt-4o")
+ result = hook.get_chat_model()
+
+ mock_get_conn.assert_called_once_with("langchain_default")
+ mock_init_chat_model.assert_called_once_with("openai:gpt-4o", api_key="sk-test")
+ assert result is mock_init_chat_model.return_value
+
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_dispatches_with_base_url(self, mock_get_conn, mock_init_chat_model):
+ mock_get_conn.return_value = _conn(password="sk-test", host="http://localhost:11434/v1")
+
+ hook = LangChainHook(llm_model="openai:llama3")
+ hook.get_chat_model()
+
+ mock_init_chat_model.assert_called_once_with(
+ "openai:llama3", api_key="sk-test", base_url="http://localhost:11434/v1"
+ )
+
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_resolves_model_from_extra(self, mock_get_conn, mock_init_chat_model):
+ mock_get_conn.return_value = _conn(password="sk-test", extra={"model": "anthropic:claude-3-7-sonnet"})
+
+ hook = LangChainHook()
+ hook.get_chat_model()
+
+ mock_init_chat_model.assert_called_once_with("anthropic:claude-3-7-sonnet", api_key="sk-test")
+
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_raises_when_no_model(self, mock_get_conn, mock_init_chat_model):
+ mock_get_conn.return_value = _conn(password="sk-test")
+
+ hook = LangChainHook()
+ with pytest.raises(ValueError, match="No chat model identifier set"):
+ hook.get_chat_model()
+ mock_init_chat_model.assert_not_called()
+
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_no_credentials_passes_empty_kwargs(self, mock_get_conn, mock_init_chat_model):
+ mock_get_conn.return_value = _conn()
+
+ hook = LangChainHook(llm_model="openai:gpt-4o")
+ hook.get_chat_model()
+
+ mock_init_chat_model.assert_called_once_with("openai:gpt-4o")
+
+
+class TestGetEmbeddingModel:
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_dispatches_via_init_embeddings_with_api_key(self, mock_get_conn, mock_init_embeddings):
+ mock_get_conn.return_value = _conn(password="sk-test")
+
+ hook = LangChainHook(embed_model="openai:text-embedding-3-small")
+ result = hook.get_embedding_model()
+
+ mock_get_conn.assert_called_once_with("langchain_default")
+ mock_init_embeddings.assert_called_once_with("openai:text-embedding-3-small", api_key="sk-test")
+ assert result is mock_init_embeddings.return_value
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_dispatches_with_base_url(self, mock_get_conn, mock_init_embeddings):
+ mock_get_conn.return_value = _conn(password="sk-test", host="http://localhost:11434/v1")
+
+ hook = LangChainHook(embed_model="openai:nomic-embed-text")
+ hook.get_embedding_model()
+
+ mock_init_embeddings.assert_called_once_with(
+ "openai:nomic-embed-text",
+ api_key="sk-test",
+ base_url="http://localhost:11434/v1",
+ )
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_resolves_embed_model_from_extra(self, mock_get_conn, mock_init_embeddings):
+ mock_get_conn.return_value = _conn(
+ password="sk-test", extra={"embed_model": "openai:text-embedding-3-large"}
+ )
+
+ hook = LangChainHook()
+ hook.get_embedding_model()
+
+ mock_init_embeddings.assert_called_once_with("openai:text-embedding-3-large", api_key="sk-test")
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_uses_embed_conn_id_when_set(self, mock_get_conn, mock_init_embeddings):
+ # llm_conn_id and embed_conn_id resolve to different connections.
+ llm_conn = _conn(password="sk-chat", extra={"model": "openai:gpt-4o"})
+ embed_conn = _conn(
+ password="sk-embed",
+ extra={"embed_model": "openai:text-embedding-3-small"},
+ )
+ mock_get_conn.side_effect = lambda conn_id: embed_conn if conn_id == "embed_conn" else llm_conn
+
+ hook = LangChainHook(llm_conn_id="chat_conn", embed_conn_id="embed_conn")
+ hook.get_embedding_model()
+
+ # embed conn picked, with its api key and model
+ mock_get_conn.assert_called_with("embed_conn")
+ mock_init_embeddings.assert_called_once_with("openai:text-embedding-3-small", api_key="sk-embed")
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_raises_when_no_embed_model(self, mock_get_conn, mock_init_embeddings):
+ # extra has chat model but no embed_model -- should still raise
+ mock_get_conn.return_value = _conn(password="sk-test", extra={"model": "openai:gpt-4o"})
+
+ hook = LangChainHook()
+ with pytest.raises(ValueError, match="No embedding model identifier set"):
+ hook.get_embedding_model()
+ mock_init_embeddings.assert_not_called()
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch.object(LangChainHook, "get_connection")
+ def test_no_credentials_passes_empty_kwargs(self, mock_get_conn, mock_init_embeddings):
+ mock_get_conn.return_value = _conn()
+
+ hook = LangChainHook(embed_model="openai:text-embedding-3-small")
+ hook.get_embedding_model()
+
+ mock_init_embeddings.assert_called_once_with("openai:text-embedding-3-small")
+
+
+class TestSameHookForBoth:
+ """A single hook instance must serve both chat and embedding calls."""
+
+ @patch("langchain.embeddings.init_embeddings")
+ @patch("langchain.chat_models.init_chat_model")
+ @patch.object(LangChainHook, "get_connection")
+ def test_chat_and_embed_from_one_hook(self, mock_get_conn, mock_init_chat_model, mock_init_embeddings):
+ mock_get_conn.return_value = _conn(password="sk-test")
+
+ hook = LangChainHook(
+ llm_model="openai:gpt-4o",
+ embed_model="openai:text-embedding-3-small",
+ )
+ chat = hook.get_chat_model()
+ embed = hook.get_embedding_model()
+
+ mock_init_chat_model.assert_called_once_with("openai:gpt-4o", api_key="sk-test")
+ mock_init_embeddings.assert_called_once_with("openai:text-embedding-3-small", api_key="sk-test")
+ assert chat is mock_init_chat_model.return_value
+ assert embed is mock_init_embeddings.return_value
diff --git a/uv.lock b/uv.lock
index 4e5e7a6f79f80..00e64a402b3e1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4152,6 +4152,10 @@ common-sql = [
google = [
{ name = "pydantic-ai-slim", extra = ["google"] },
]
+langchain = [
+ { name = "langchain" },
+ { name = "langchain-openai" },
+]
mcp = [
{ name = "pydantic-ai-slim", extra = ["mcp"] },
]
@@ -4190,6 +4194,8 @@ requires-dist = [
{ name = "apache-airflow-providers-standard", editable = "providers/standard" },
{ name = "fastavro", marker = "python_full_version >= '3.14' and extra == 'avro'", specifier = ">=1.12.1" },
{ name = "fastavro", marker = "python_full_version < '3.14' and extra == 'avro'", specifier = ">=1.10.0" },
+ { name = "langchain", marker = "extra == 'langchain'", specifier = ">=1.0.0" },
+ { name = "langchain-openai", marker = "extra == 'langchain'", specifier = ">=0.3.0" },
{ name = "pyarrow", marker = "python_full_version >= '3.14' and extra == 'parquet'", specifier = ">=22.0.0" },
{ name = "pyarrow", marker = "python_full_version < '3.14' and extra == 'parquet'", specifier = ">=18.0.0" },
{ name = "pydantic-ai-slim", specifier = ">=1.34.0" },
@@ -4200,7 +4206,7 @@ requires-dist = [
{ name = "pydantic-ai-slim", extras = ["openai"], marker = "extra == 'openai'" },
{ name = "sqlglot", marker = "extra == 'sql'", specifier = ">=30.0.0" },
]
-provides-extras = ["anthropic", "bedrock", "google", "openai", "mcp", "avro", "parquet", "sql", "common-sql"]
+provides-extras = ["anthropic", "bedrock", "google", "openai", "mcp", "avro", "parquet", "sql", "common-sql", "langchain"]
[package.metadata.requires-dev]
dev = [
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+[[package]]
+name = "langchain"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "langchain-core" },
+ { name = "langgraph" },
+ { name = "pydantic" },
+]
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+version = "1.4.0"
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+ { name = "langchain-protocol" },
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