Agent Integrations

pip install langchain-mixpeek

from langchain_mixpeek import MixpeekRetriever, MixpeekToolkit

retriever = MixpeekRetriever(
    api_key="YOUR_API_KEY",
    retriever_id="ret_xxx",
    namespace_id="ns_xxx"
)

# Use as a retriever in any RAG chain
docs = retriever.invoke("product demo walkthrough")

# Or as a full toolkit with ingest, search, classify, cluster, alert tools
toolkit = MixpeekToolkit(api_key="YOUR_API_KEY", namespace_id="ns_xxx")
tools = toolkit.get_tools()

{
  "mcpServers": {
    "mixpeek-retrieval": {
      "url": "https://mcp.mixpeek.com/retrieval/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

import json, openai, requests

def search_documents(query: str) -> list:
    resp = requests.post(
        f"https://api.mixpeek.com/v1/retrievers/{RETRIEVER_ID}/execute",
        headers={"Authorization": f"Bearer {API_KEY}", "X-Namespace": NS_ID, "Content-Type": "application/json"},
        json={"inputs": {"query_text": query}, "limit": 5},
    )
    return resp.json()["results"]

retriever_tool = {
    "type": "function",
    "function": {
        "name": "search_documents",
        "description": "Search the knowledge base using multimodal retrieval",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string", "description": "Natural language search query"}},
            "required": ["query"],
        },
    },
}

def mixpeek_search(query: str, limit: int = 10) -> list:
    resp = requests.post(
        f"https://api.mixpeek.com/v1/retrievers/{RETRIEVER_ID}/execute",
        headers={"Authorization": f"Bearer {API_KEY}", "X-Namespace": NS_ID, "Content-Type": "application/json"},
        json={"inputs": {"query_text": query}, "limit": limit},
    )
    resp.raise_for_status()
    return resp.json()["results"]