> For the complete documentation index, see [llms.txt](https://agent-gpt.gitbook.io/agent-gpt/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agent-gpt.gitbook.io/agent-gpt/integration-examples/cohere.md). # Cohere **Architecture Blueprint** ```mermaid flowchart LR subgraph Firewall[Zero-Trust Perimeter] FW1[Cohere Model Zoo] FW2[AgentGPT CIPHER] end subgraph RAG[RAG 3.0 Stack] V1[Vectorizer 3000T] -->|768D Embed| R[Recall Optimizer] R -->|Context Boost| R2[Re-Ranker X8] end subgraph Detectors[Veracity Subsystem] D1[Hallucination Scanner] D2[Confidence Calibrator] --> FW2 D3[Fact-Check Legion] end FW1 <--> RAG RAG --> Detectors --> User{Agent} ``` *** #### **1. Cohere Turbocharge Configuration** **Enterprise RAG Pipeline (YAML):** ```yaml # cohere-agent.yaml integration: cohere: models: embed: "embed-multilingual-v3" rerank: "rerank-english-v3" generate: default: "command-r-plus" fallback: "command-r" cache: strategy: "semantic-faiss" ttl: "48h" agentgpt: security: knowledge_guard: max_similarity: 0.82 forbidden_topics: - "PII" - "Trade Secrets/*" workflows: rag: stages: - "vectorization@hybrid" - "recall_boost" - "veracity_mesh" - "redaction_gate" ``` *** #### **2. Multi-Tenant Knowledge Clustering** **Secure Tenant Isolation (Python):** ```python from cohere import Client from agentgpt.security import HyperplaneIsolator class CohereMultitenantRouter: def __init__(self): self.co = Client(api_key=os.getenv("COHERE_AP")) self.isolator = HyperplaneIsolator( dimensions=1024, isolation_strategy="radial_orthogonal" ) async def embed_with_tenant(self, text: str, tenant_id: str): base_embed = await self.co.embed(texts=[text], model="multilingual-2025") return self.isolator.project( vector=base_embed.embeddings[0], tenant_id=tenant_id ) def calculate_tenant_similarity(self, vec1, vec2) -> float: return self.isolator.cross_tenant_distance(vec1, vec2) ``` *** #### **3. Veracity Mesh Workflow** **Hallucination Interceptor (Rust):** ```rust // agentgpt-veracity/src/lib.rs pub struct VeracityScoring { pub factual_consistency: f32, pub source_citation_score: f32, pub temporal_freshness: i64, } impl VeracityScoring { pub fn verify(&self) -> Result<(), VeracityError> { if self.factual_consistency < 0.82 { return Err(VeracityError::InconsistencyThreshold); } if chrono::Utc::now().timestamp() - self.temporal_freshness > 86400 * 30 { return Err(VeracityError::StaleKnowledge); } Ok(()) } } pub async fn cross_check_sources(sources: Vec) -> VeracityScoring { // Quantum-resistant verification logic } ``` *** #### **4. Cognitive Load Management** **Batch Processing Configuration:** ```bash agentgpt cohere optimize \ --model command-r-plus \ --max-concurrency 25 \ --min-tokens 50 \ --max-latency 850ms \ --strategy "burst_mitigation" ``` **Output Telemetry**: ``` [COHERE ANALYTICS] Batch Throughput: 1.2M tokens/min Error Budget Consumption: 11% Hallucination Rate: 0.0007% Average Veracity Score: 93.4 [COST OPTIMIZATION] Total Tokens: 48M Estimated Savings: $2,811 (vs v3 API) ``` *** #### **5. Security & Compliance** **Sensitive Data Scrubbing:** ```python from agentgpt.redaction import CIPHEREngine class CohereSanitizer: def __init__(self): self.cipher = CIPHEREngine( mode="AES-GCM-SIV", kms_arn="aws:kms:us-gov-1" ) def process_input(self, user_input: str) -> str: clean_text = self.redact_pii(user_input) return self.cipher.encrypt(clean_text) def redact_pii(self, text: str) -> str: patterns = [ r"\d{3}-\d{2}-\d{4}", # SSN r"[\w\.-]+@[\w\.-]+\.\w+" # Email ] return re.sub( pattern=f"({'|'.join(patterns)})", repl="[REDACTED]", string=text ) ``` *** #### **6. Troubleshooting** | **Failure Mode** | **War Room Protocol** | | ------------------------- | ---------------------------------------------------- | | `Contamination Incident` |

1. Freeze model
2. Rollback RAG checkpoint

| | `Knowledge Decay` |

1. Trigger re-embedding wave
2. Adjust TTL

| | `Veracity False Positive` |

1. Lower threshold 0.05
2. Human oversight

| | `Cross-Tenant Leakage` |

1. Rotate isolation vectors
2. Audit logs

| *** #### **7. Cost Governance** **Token Budget Enforcement:** ```json { "cost_rules": { "monthly_budget": 25000, "alert_thresholds": [0.5, 0.8, 0.95], "auto_scaling": { "scale_down_model": "command-r", "emergency_protocol": "circuit_breaker" }, "sharding": { "max_shard_size_mb": 200, "shard_strategy": "semantic_clustering" } } } ``` *** **Best Practices**: 1. **Quantum-Resistant Embeddings**: ```bash agentgpt cohere upgrade-security \ --encryption pqc-kyber-1024 \ --rotation-interval 72h ``` 2. **Cold Knowledge Archiving**: ```python from agentgpt.storage import GlacierCognitiveVault vault = GlacierCognitiveVault( retention_policy="10y", retrieval_tier="expedited" ) vault.archive_embeddings( vectors=legacy_vectors, metadata=cognitive_tags ) ``` 3. **Multi-Model Voting**: ```yaml truth_consensus: voters: - "command-r-plus" - "chatgpt-4-turbo" - "anthropic-claude-3-opus" agreement_threshold: 2/3 tiebreaker: "agentgpt-adjudicator" ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://agent-gpt.gitbook.io/agent-gpt/integration-examples/cohere.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.