Enhancing LLM applications with Identity Resolution using Zingg and LangChain

Retrieval-Augmented Generation (RAG) has revolutionized how large language models (LLMs) interact with domain-specific data. However, traditional RAG approaches struggle with structured datasets containing duplicates or variations. Zingg, an open-source entity resolution tool, addresses these challenges by enabling accurate identity resolution. Paired with LangChain, it unlocks powerful, context-aware information retrieval. Let’s explore how Zingg elevates RAG applications.

The Need for Identity Resolution in RAG

LLMs excel at generating human-like text but lack innate awareness of entity relationships in structured data. Consider a customer service chatbot:

• Without entity resolution, slight variations in names (e.g., "John Doe" vs. "Jon Do") or typos can lead to incorrect data retrieval.

• Duplicate records from disparate sources create ambiguity and reducing response accuracy.

Zingg solves these issues by clustering records representing the same entity, enabling LLMs to retrieve unified, context-rich information.

Introducing Zingg: Scalable Entity Resolution

Zingg specializes in fuzzy matching, deduplication, and clustering across structured datasets. Key features include:

• Fuzzy Matching: Handles typos, abbreviations, and phonetic variations.

• Cluster Analysis: Groups related records (e.g., "Benjamin Koerbin" and "Benjamine Koerbin" under one cluster).

• Scalability: Processes large datasets efficiently.

• Customizable Models: Train matching rules tailored to your data schema.

By integrating Zingg with LangChain, developers can build Identity RAG systems that combine LLMs’ generative power with precise entity resolution.

Implementing Identity RAG with Zingg and LangChain

The provided code demonstrates a workflow for resolving identities and enhancing LLM responses.

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"zingg-out.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess: Create a combined text field and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def preprocess(row):\n",
    "    text = (\n",
    "        f\"First Name: {row['FNAME']}, Last Name: {row['LNAME']}, \"\n",
    "        f\"Date of Birth: {row['DOB']}, Address: {row['STNO']} {row['ADD1']}, \"\n",
    "        f\"{row['ADD2']}, {row['AREA_CODE']} {row['STATE']}\"\n",
    "    )\n",
    "    metadata = {\n",
    "        \"cluster\": row[\"Z_CLUSTER\"],\n",
    "        \"ssn\": row[\"SSN\"],\n",
    "        \"state\": row[\"STATE\"],\n",
    "        \"dob\": row[\"DOB\"]\n",
    "    }\n",
    "    return {\"text\": text, \"metadata\": metadata}\n",
    "\n",
    "documents = [preprocess(row) for _, row in df.iterrows()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split documents into texts and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = [doc[\"text\"] for doc in documents]\n",
    "metadatas = [doc[\"metadata\"] for doc in documents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_ollama import OllamaEmbeddings\n",
    "embeddings = OllamaEmbeddings(model=\"nomic-embed-text\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vector Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_chroma import Chroma\n",
    "\n",
    "vector_store = Chroma.from_texts(\n",
    "    texts=texts,\n",
    "    embedding=embeddings,\n",
    "    metadatas=metadatas,\n",
    "    persist_directory=\"./chroma_db\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = vector_store.as_retriever(search_kwargs={\"k\": 2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"[INST] <<SYS>>\n",
    "You are an identity resolution expert. Analyze the following cluster information to:\n",
    "1. Match records to the query **based on name, DOB, and other attributes**\n",
    "2. Handle typos/name variations (e.g., \"Schulz\" vs \"Schultz\")\n",
    "3. Strictly compare DOBs in YYYY-MM-DD format\n",
    "4. If no exact match, show closest candidates with differences. \n",
    "5. Always mention Cluster ID and record count\n",
    "\n",
    "\n",
    "Format response as:\n",
    "- Query \n",
    "- Closest Match\n",
    "- Cluster ID | Record Count | Match Confidence\n",
    "  - Details (Show differences in main attributes between records)\n",
    "<</SYS>>\n",
    "\n",
    "Context: {context}\n",
    "\n",
    "Question: {question} [/INST]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_ollama import ChatOllama\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(template)\n",
    "llm = ChatOllama(model=\"llama3:8b\")\n",
    "\n",
    "# Chain\n",
    "chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Query: Find records of benjamin koerbin born on 19210210\n",
      "- Closest Match: Both documents match the query\n",
      "- Cluster ID | Record Count | Match Confidence\n",
      "  - Details:\n",
      "    + Exact matches found in main attributes (name, DOB)\n",
      "\n",
      "Cluster ID: 21 | Record Count: 2 | Match Confidence: 100%\n",
      "\n",
      "* Document 1: benjamin koerbin, 19210210\n",
      "* Document 2: benjamin koerbin, 19210210\n",
      "\n",
      "No differences found in main attributes.\n"
     ]
    }
   ],
   "source": [
    "response = chain.invoke(\"Find records of benjamin koerbin born on 19210210\")\n",
    "print(response)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

Let’s break down the key steps:

1. Preprocess Data with Entity Clustering

Zingg clusters records from a CSV (zingg-out.csv) into entity groups. Each cluster receives a Z_CLUSTER ID, enabling unified retrieval:

2. Embeddings and Vector Store

Using Ollama embeddings and Chroma, text representations are stored with metadata for context-aware retrieval:

3. Retriever with Cluster Awareness

The retriever prioritizes cluster context, fetching the top 2 matches to handle variations:

4. Prompt Engineering for Identity Resolution

A structured prompt guides the LLM to analyze clusters, compare attributes, and highlight discrepancies:

5. LangChain Integration

The chain combines retrieval, LLM processing, and structured output:

Real-World Example: Customer Data Retrieval

Query:

The query searches for records related to "benjamin koerbin" born on "19210210" using the identity-aware retrieval system.

Output:

The system successfully retrieves two matching records from the same cluster (Cluster ID: 21) with 100% match confidence, confirming that both documents correspond to the queried entity without discrepancies.

Query 2:

The query searches for records of "jakson eling," which contains a potential typo in the last name.

Output 2:

The system identifies and retrieves three matching records under the same cluster, despite the typo in the query. It correctly maps "eling" to "eglinton" using fuzzy matching which is ensuring accurate identity resolution.

Zingg’s clustering ensures both records are retrieved despite potential input variations, while the LLM generates a concise and structured response.

Why Zingg Stands Out

• Accuracy: Resolves entities using configurable rules, reducing false positives.

• Transparency: Cluster IDs and metadata provide auditable traces for LLM responses.

• Cost-Effective: Open-source with minimal infrastructure requirements.

Use Cases for Zingg + LangChain

1. Customer Service Chatbots: Resolve customer identities across fragmented databases.

2. Healthcare Systems: Link patient records from multiple providers.

3. Financial Compliance: Detects duplicate transactions or accounts for fraud prevention.

4. E-Commerce: Unify customer profiles for personalized recommendations.

Conclusion: The Future of Context-Aware AI

The Zingg-LangChain integration bridges the gap between entity resolution and generative AI, enabling LLMs to deliver precise, context-rich responses. By clustering records and enriching metadata, developers can build systems that understand real-world complexity—not just text patterns.

Explore the Zingg GitHub repository to start implementing Identity RAG, and leverage the attached notebook to integrate these capabilities into your LangChain workflows.

As AI evolves, tools like Zingg will become indispensable for applications requiring accuracy, transparency, and deep contextual awareness. The era of truly intelligent, entity-aware LLMs is here—and it’s powered by identity resolution.

‍

‍

‍

‍