How should rag_chatbot_es be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

rag_chatbot_es answer-first brief

Rag Chatbot based on a CrewAI Flow Policy RAG Chatbot CrewAI webinar demo for embedding synthetic policy PDFs into MongoDB Atlas Vector Search and chatting over them with a deployed CrewAI Flow. All PDFs are read recursively from crews/embedder/data/ and embedded into one explicit MongoDB collection. Requirements - Python >=3.10, <3.14 - uv - MongoDB Atlas connection string - OpenAI API key Setup Copy .env.example to either ../.env or .env and fill in Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

rag_chatbot_es is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

rag_chatbot_es

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Crewaiinc Fde

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Crewaiinc Fde

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

cp .env.example .env

bash

MONGODB_CONNECTION_STRING=mongodb+srv://<user>:<password>@<cluster>.mongodb.net/
OPENAI_API_KEY=sk-...

# Required MongoDB target for ingestion.
EMBEDDER_DB_NAME=webinar_policies
EMBEDDER_COLLECTION_NAME=documents_rag

# Required MongoDB target for the chat flow. Use the same values above
# unless you intentionally query a different collection.
RAG_DB_NAME=webinar_policies
RAG_COLLECTION_NAME=documents_rag

bash

uv sync

bash

uv run crewai run

bash

EMBEDDER_DB_NAME=webinar_policies \
EMBEDDER_COLLECTION_NAME=documents_rag \
uv run crewai run

python

from embedder.main import PdfEmbeddingFlow

PdfEmbeddingFlow().kickoff(inputs={
    "db_name": "webinar_policies",
    "collection_name": "documents_rag",
})

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

Policy RAG Chatbot

CrewAI webinar demo for embedding synthetic policy PDFs into MongoDB Atlas Vector Search and chatting over them with a deployed CrewAI Flow.

All PDFs are read recursively from crews/embedder/data/ and embedded into one explicit MongoDB collection.

Requirements

Python >=3.10, <3.14
uv
MongoDB Atlas connection string
OpenAI API key

Setup

Copy .env.example to either ../.env or .env and fill in the values:

cp .env.example .env

MONGODB_CONNECTION_STRING=mongodb+srv://<user>:<password>@<cluster>.mongodb.net/
OPENAI_API_KEY=sk-...

# Required MongoDB target for ingestion.
EMBEDDER_DB_NAME=webinar_policies
EMBEDDER_COLLECTION_NAME=documents_rag

# Required MongoDB target for the chat flow. Use the same values above
# unless you intentionally query a different collection.
RAG_DB_NAME=webinar_policies
RAG_COLLECTION_NAME=documents_rag

Install dependencies:

uv sync

Usage

Run from the repository root. All PDFs found recursively under crews/embedder/data/ are embedded:

uv run crewai run

MongoDB Target

Set the MongoDB target without editing code. The data folder is fixed:

EMBEDDER_DB_NAME=webinar_policies \
EMBEDDER_COLLECTION_NAME=documents_rag \
uv run crewai run

Direct Python usage still works:

from embedder.main import PdfEmbeddingFlow

PdfEmbeddingFlow().kickoff(inputs={
    "db_name": "webinar_policies",
    "collection_name": "documents_rag",
})

Output Shape

Each chunk is stored as:

{
  "_id": "<page_hash>:<chunk_index>",
  "text": "...",
  "embedding": [0.0],
  "metadata": {
    "file_name": "...",
    "file_path": "...",
    "file_md5": "...",
    "page": 1,
    "total_pages": 3,
    "page_hash": "...",
    "chunk_index": 0,
    "page_chunk_count": 2,
    "title": "politica_garantias",
    "category": "policies",
    "source_dir": "/absolute/path/to/data/policies"
  }
}

The Flow creates a MongoDB Atlas vector search index named vector_index on the embedding field.

Implementation Notes

Embedding model: text-embedding-3-large
Vector dimensions: 3072
Similarity: cosine
Chunking: semchunk + tiktoken, 512 tokens, 20% overlap
Idempotency: unchanged PDF pages are skipped using deterministic page hashes

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

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Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T20:57:08.556Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Crewaiinc Fde",
    "href": "https://github.com/CrewAIInc-FDE/rag_chatbot_es",
    "sourceUrl": "https://github.com/CrewAIInc-FDE/rag_chatbot_es",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T18:18:12.409Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T18:18:12.409Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-crewaiinc-fde-rag-chatbot-es/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]