deepadata-langchain-adapter

EDM enrichment layer for LangChain.

LangChain stores conversation context. EDM captures the emotional governance of that context — as a portable, schema-validated artifact.

Installation

npm install deepadata-langchain-adapter deepadata-edm-sdk

Usage

import { enrichWithEDM } from 'deepadata-langchain-adapter';
import { ConversationBufferMemory } from 'langchain/memory';

const memory = new ConversationBufferMemory();
const input = "I'm feeling overwhelmed with the project deadline. Nothing seems to be going right.";
const output = "I understand that feeling. Let's break this down into smaller, manageable tasks.";

// EDM enrichment (emotional context)
const { edmArtifact } = await enrichWithEDM(input, { profile: 'essential' });

// LangChain memory (unchanged — LangChain does what LangChain does)
await memory.saveContext({ input }, { output });

// Now you have both:
// - LangChain memory for conversation context
// - EDM artifact for emotional governance and portability

Works With Any Memory Type

EDM enrichment works alongside any LangChain memory:

// ConversationBufferMemory
const buffer = new ConversationBufferMemory();
const { edmArtifact } = await enrichWithEDM(input);
await buffer.saveContext({ input }, { output });

// ConversationSummaryMemory
const summary = new ConversationSummaryMemory({ llm });
const { edmArtifact } = await enrichWithEDM(input);
await summary.saveContext({ input }, { output });

// VectorStoreRetrieverMemory
const vectorMemory = new VectorStoreRetrieverMemory({ vectorStoreRetriever });
const { edmArtifact } = await enrichWithEDM(input);
await vectorMemory.saveContext({ input }, { output });

// The EDM artifact travels alongside, not replacing, the LangChain memory

What You Get

From the same text input, you now have:

| LangChain | EDM | |-----------|-----| | Stores conversation | Captures emotional context | | Enables retrieval | Enables governance | | Platform-specific | Portable standard | | What was said | Why it mattered |

API

enrichWithEDM(text, options?)

const { edmArtifact, confidence, model, profile } = await enrichWithEDM(text, {
  // EDM profile (default: "core")
  profile: "essential",  // ~20 fields, lightweight for LangChain

  // LLM provider (default: "anthropic")
  provider: "anthropic",  // or "openai" | "kimi"

  // Governance
  subjectId: "user123",
  jurisdiction: "GDPR",
  consentBasis: "consent",

  // Classification
  visibility: "private",
  piiTier: "moderate",
  tags: ["support", "emotional"],
});

Profiles

| Profile | Fields | Use Case | |---------|--------|----------| | core | ~20 | LangChain memory enrichment (recommended) | | extended | ~45 | Journaling, wellness applications | | full | 96 | Clinical, therapeutic applications |

For LangChain integrations, core profile is recommended — lightweight enough to not impact chain performance.

Why Both?

LangChain memory modules are excellent for conversation persistence and retrieval. But production deployments need answers to:

• "What schema does the memory follow?"
• "Who owns this conversation data?"
• "How do I handle a GDPR deletion request?"
• "Can users export their data?"

EDM answers these with a governed artifact:

• Schema: 20 validated fields in core profile
• Governance: Consent basis, retention policy, subject rights
• Portability: Standard format, not locked to LangChain
• MIT licensed: No vendor dependency

Environment Variables

| Variable | Description | |----------|-------------| | ANTHROPIC_API_KEY | Claude extraction (default) | | OPENAI_API_KEY | GPT extraction | | MOONSHOT_API_KEY | Kimi extraction |

Related

• deepadata-edm-sdk — Core SDK
• deepadata-ddna-tools — Signing and verification
• deepadata-mem0-adapter — Mem0 enrichment
• deepadata-zep-adapter — Zep enrichment

License

MIT