LangChain

The langchain-mixpeek package gives LangChain agents the ability to see video, hear audio, search images, and act on unstructured content, all through Mixpeek’s multimodal infrastructure.

Connect LangChain to your own content

Create a managed namespace to get the api_key, namespace, and retriever_id that every example on this page needs.

Installation

pip install langchain-mixpeek

Quick Start

1. Search (Retriever)

from langchain_mixpeek import MixpeekRetriever

retriever = MixpeekRetriever(
    api_key="mxp_...",
    retriever_id="ret_abc123",
    namespace="my-namespace",
)
docs = retriever.invoke("find the red cup")

Each result is a LangChain Document with page_content and metadata (document_id, score, namespace).

2. Agent Tool

from langchain_mixpeek import MixpeekRetriever

retriever = MixpeekRetriever(
    api_key="mxp_...",
    retriever_id="ret_abc123",
    namespace="my-namespace",
)

# One line: the retriever becomes an agent tool
tool = retriever.as_tool()

3. Full Toolkit (search + ingest + classify + cluster + alert)

from langchain_mixpeek import MixpeekToolkit
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

toolkit = MixpeekToolkit(
    api_key="mxp_...",
    namespace="my-namespace",
    bucket_id="bkt_abc123",
    collection_id="col_def456",
    retriever_id="ret_ghi789",
)

agent = create_react_agent(
    ChatAnthropic(model="claude-sonnet-4-20250514"),
    toolkit.get_tools(),
)

result = agent.invoke({
    "messages": [("user", "Scan these product URLs and alert me about counterfeits")]
})

The toolkit gives your agent 6 capabilities:

Tool	What it does
`mixpeek_search`	Search video, images, audio, documents by natural language
`mixpeek_ingest`	Upload text, images, video, audio, PDFs, spreadsheets
`mixpeek_process`	Trigger feature extraction (embedding, OCR, transcription, face detection)
`mixpeek_classify`	Run taxonomy classification on documents
`mixpeek_cluster`	Group similar documents (kmeans, dbscan, hdbscan, etc.)
`mixpeek_alert`	Set up monitoring with webhook, Slack, or email notifications

4. VectorStore (full pipeline)

from langchain_mixpeek import MixpeekVectorStore

store = MixpeekVectorStore(
    api_key="mxp_...",
    namespace="my-namespace",
    bucket_id="bkt_abc123",
    collection_id="col_def456",
    retriever_id="ret_ghi789",
)

# Ingest any content type
store.add_texts(["product description..."])
store.add_images(["https://example.com/photo.jpg"])
store.add_videos(["https://example.com/clip.mp4"])
store.add_audio(["https://example.com/recording.mp3"])
store.add_pdfs(["https://example.com/doc.pdf"])
store.add_excel(["https://example.com/data.xlsx"])

# Trigger processing (embedding, OCR, face detection, etc.)
store.trigger_processing()

# Search
docs = store.similarity_search("red cup on the table")

# Convert to agent tools anytime
tool = store.as_tool()
toolkit = store.as_toolkit()
retriever = store.as_retriever()

5. Search-Only (minimal config)

If you only need search, skip the bucket/collection config:

store = MixpeekVectorStore.from_retriever(
    api_key="mxp_...",
    namespace="my-namespace",
    retriever_id="ret_abc123",
)
docs = store.similarity_search("red cup")

Configuration

Parameter	Type	Default	Description
`api_key`	str	required	Mixpeek API key (`mxp_...`)
`retriever_id`	str	required	Retriever ID for search (`ret_...`)
`namespace`	str	required	Namespace to operate in
`bucket_id`	str	required*	Bucket for uploads (`bkt_...`)
`collection_id`	str	required*	Collection for processing (`col_...`)
`top_k`	int	`10` / `5`	Max results (retriever / tool)
`content_field`	str	`"text"`	Field to use as `page_content`
`filters`	dict	`None`	Attribute filters (retriever only)

*Required for ingest/processing. Not needed for search-only via from_retriever().

The content_field can reference any field in your retriever results, including enrichment fields like trend_insight or brand_alignment. If the field contains a dict with a text key, the text is automatically extracted.

Examples

Brand Protection Agent

An agent that scans marketplace listings and alerts on counterfeits:

from langchain_mixpeek import MixpeekToolkit
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

toolkit = MixpeekToolkit(
    api_key="mxp_...",
    namespace="brand-protection",
    bucket_id="bkt_...",
    collection_id="col_...",
    retriever_id="ret_...",
)

# Only give the agent the tools it needs
agent = create_react_agent(
    ChatAnthropic(model="claude-sonnet-4-20250514"),
    toolkit.get_tools(actions=["search", "ingest", "process", "alert"]),
    prompt="You are a brand protection agent. Scan products and flag counterfeits.",
)

result = agent.invoke({
    "messages": [("user", "Check if these 5 Amazon listings are selling counterfeit Stanley cups")]
})

RAG Chain

Standard retrieval-augmented generation:

from langchain_core.prompts import ChatPromptTemplate
from langchain_anthropic import ChatAnthropic
from langchain_mixpeek import MixpeekRetriever

retriever = MixpeekRetriever(
    api_key="mxp_...",
    retriever_id="ret_...",
    namespace="my-namespace",
)
llm = ChatAnthropic(model="claude-sonnet-4-20250514")

prompt = ChatPromptTemplate.from_template(
    "Answer using this context:\n{context}\n\nQuestion: {question}"
)

chain = {"context": retriever, "question": lambda x: x} | prompt | llm
response = chain.invoke("what happens at 2 minutes?")

Multi-Retriever Agent

Different retrievers for different content types:

from langchain_mixpeek import MixpeekTool
from langgraph.prebuilt import create_react_agent

video_search = MixpeekTool(
    api_key="mxp_...",
    retriever_id="ret_video_archive",
    namespace="archive",
    name="search_video_archive",
    description="Search video archive for specific scenes, faces, or moments.",
)

image_search = MixpeekTool(
    api_key="mxp_...",
    retriever_id="ret_product_images",
    namespace="catalog",
    name="search_product_images",
    description="Search product image catalog by visual similarity.",
)

agent = create_react_agent(llm, [video_search, image_search])

Platform Features

The VectorStore exposes the full Mixpeek platform:

Taxonomies (document classification)

# Create a taxonomy
store.create_taxonomy(
    name="product-categories",
    config={
        "taxonomy_type": "flat",
        "retriever_id": "ret_...",
        "collection_id": "col_...",
        "input_mappings": [...],
        "enrichment_fields": [...],
    },
)

# List and execute
taxonomies = store.list_taxonomies()
results = store.execute_taxonomy("tax_abc123")

Clusters (unsupervised grouping)

# Create and run clustering
cluster = store.create_cluster(
    cluster_type="vector",
    vector_config={
        "algorithm": "kmeans",  # or dbscan, hdbscan, spectral, etc.
        "algorithm_params": {"n_clusters": 10},
    },
)
store.execute_cluster(cluster["cluster_id"])
groups = store.get_cluster_groups(cluster["cluster_id"])

Alerts (match notifications)

# Create an alert with webhook + Slack
store.create_alert(
    name="counterfeit-detection",
    notification_config={
        "channels": [
            {"channel_type": "webhook", "config": {"url": "https://..."}},
            {"channel_type": "slack", "channel_id": "#alerts"},
        ],
        "include_matches": True,
        "include_scores": True,
    },
)

# Check results
results = store.get_alert_results("alt_abc123")

Custom Plugins

# List deployed plugins
plugins = store.list_plugins()

# Check deployment status
status = store.get_plugin_status("plg_abc123")

# Test a realtime plugin
result = store.test_plugin("plg_abc123", inputs={"text": "hello"})

Tips

Selecting Toolkit Actions

Don’t give agents tools they don’t need. Use actions to scope:

# Search-only agent
toolkit.get_tools(actions=["search"])

# Ingest + search agent
toolkit.get_tools(actions=["search", "ingest", "process"])

# Full platform agent
toolkit.get_tools()  # all 6 tools

Error Handling

All toolkit tools catch exceptions and return error strings instead of crashing the agent. The retriever raises exceptions normally.

Token Efficiency

Set top_k to limit results. Large result sets waste tokens without improving quality. Start with top_k=5.

Source Code

PyPI: langchain-mixpeek (Python)
npm: @mixpeek/langchain (JavaScript)
GitHub: mixpeek/langchain-mixpeek
Connector Page: mixpeek.com/connectors/langchain

Next Steps

MCP Server

Connect Claude directly via the Model Context Protocol

OpenAI Function Calling

Wire Mixpeek into OpenAI assistants

Feature Extractors

15+ extractors: text, image, video, audio, face, PDF, web scraper

Python SDK

Full SDK reference

Get started

Connect your data

Extract features

Build retrievers

Enrich & organize

Integrate & operate

Resources

Connect LangChain to your own content

Installation

Quick Start

1. Search (Retriever)

2. Agent Tool

3. Full Toolkit (search + ingest + classify + cluster + alert)

4. VectorStore (full pipeline)

5. Search-Only (minimal config)

Configuration

Examples

Brand Protection Agent

RAG Chain

Multi-Retriever Agent

Platform Features

Taxonomies (document classification)

Clusters (unsupervised grouping)

Alerts (match notifications)

Custom Plugins

Tips

Selecting Toolkit Actions

Error Handling

Token Efficiency

Source Code

Next Steps

MCP Server

OpenAI Function Calling

Feature Extractors

Python SDK