llm-join 0.4.2

Fuzzy join DataFrames using LLM scoring and embedding retrieval

llm-join

The pandas join that understands what your data means.

pd.merge joins on exact values. llm-join joins on meaning using embeddings to find candidates and an LLM you already have to decide if they match.

from llm_join import fuzzy_join

result = fuzzy_join(
    df1, df2,
    left_on="vendor", right_on="supplier_name",
    llm=my_llm, embed_fn=my_embed,
    context="company names",
    llm_concurrency=10,
)

---

Table of Contents

• The Problem
• Why llm-join
• Real-World Use Cases
• Install
• Quick Start
• How It Works
• Cost & Scale
• Performance
• Usage
  • Basic join
  • With domain context
  • See why matches were made
  • Control cost
  • Left join (audit unmatched rows)
  • Multi-column join key
  • Match all results
  • Chaining multiple joins
• Works with Any LLM
• Works with Any Embedding Function
• Features
• Parameters
• License

---

The Problem

You have two DataFrames. Same data, different text:

Your system	Their system
Goldman Sachs & Co.	The Goldman Sachs Group Inc
Sony WH-1000XM5	SONY-WH1000XM5-BLK
MSFT Q4 license renewal	Microsoft Enterprise Agreement Q4-2024
Python programming	Python (language)

pd.merge returns nothing. Fuzzy string matching gets the wrong answer. You end up writing custom logic or doing it by hand.

llm-join solves this in one function call.

---

Why llm-join

vs. pd.merge

Exact string match only. Fails on any variation in naming.

vs. fuzzy string matching (fuzzywuzzy, rapidfuzz)

Character similarity, not meaning. "iPhone 14 Pro" vs "iPhone 14 Pro Max" scores high but they are different products. "CABLE-USBC-200CM-BLK" vs "USB-C charging cable 2m black" scores near zero even though they are the same item.

vs. embedding similarity alone

Fast and cheap, but no reasoning. Can't explain why two values match or catch false positives confidently.

llm-join

Embeddings narrow the field cheaply. Your model scores only the plausible candidates with context you provide. Accurate where it matters, fast where it doesn't.

Works with any provider: OpenAI, Azure, Anthropic, Bedrock, Ollama, or any private model. No data leaves your infrastructure beyond what your own providers already see.

---

Real-World Use Cases

Domain	Left table	Right table	Problem
Supply chain	Buyer product description	Supplier SKU	Match products across 50+ vendor catalogs
Finance	Expense report payee	GL account / vendor master	Reconcile transactions automatically
Legal / M&A	Contract party name	Corporate registry	Identify true legal entity
Compliance	Customer name	OFAC sanctions list	Sanctions screening at scale
Retail / e-commerce	Marketplace product listing	Master product catalog	Deduplicate listings across 50+ sellers
Logistics	Shipment description	Harmonized tariff code	Auto-classify goods at customs
Research	Author name	Citation database	Disambiguate authors
Government	Vendor name	Tax registry	Consolidate procurement spend
Real estate	Raw address input	Property records DB	Standardize and match addresses
Pharma	Drug compound names in adverse event reports	WHO drug dictionary (WHO-DD)	Standardize brand, generic, and abbreviation variants "Tylenol 500mg", "acetaminophen", "APAP" all resolve to the same WHO entry for pharmacovigilance signal detection
HR	Job title in offer letter or LinkedIn import	Internal job architecture / grade catalog	Normalize "Senior Software Engineer II", "Sr. SWE", "L5 Engineer" to canonical job families for compensation benchmarking and workforce analytics

---

Install

pip install llm-join

Or from source:

git clone https://github.com/adityabalki/llm-join.git
cd llm-join
pip install -e .

---

Quick Start

import pandas as pd
import numpy as np
import openai
from llm_join import fuzzy_join

df1 = pd.DataFrame({
    "vendor": ["Goldman Sachs & Co.", "Amazon Web Services", "Microsoft Corp"],
    "spend": [1_200_000, 890_000, 340_000]
})

df2 = pd.DataFrame({
    "supplier_name": ["The Goldman Sachs Group Inc", "Amazon.com Inc.", "Microsoft Corporation"],
    "category": ["Finance", "Cloud", "Software"]
})

client = openai.OpenAI()

def my_llm(prompt):
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content

def my_embed(texts):
    response = client.embeddings.create(model="text-embedding-3-small", input=texts)
    return np.array([d.embedding for d in response.data], dtype="float32")

result = fuzzy_join(
    df1, df2,
    left_on="vendor",
    right_on="supplier_name",
    llm=my_llm,
    embed_fn=my_embed,
    context="company names — match legal entity variants and abbreviations",
    llm_concurrency=10,   # how many LLM calls to run in parallel
    how="inner",
)

print(result)

vendor	spend	supplier_name	category
Goldman Sachs & Co.	1,200,000	The Goldman Sachs Group Inc	Finance
Amazon Web Services	890,000	Amazon.com Inc.	Cloud
Microsoft Corp	340,000	Microsoft Corporation	Software

---

How It Works

Example: A retailer's purchase orders use plain English product names. Their supplier sends a catalog with SKU codes. pd.merge matches nothing. llm-join bridges the gap.

orders_df = pd.DataFrame({"product_name": [
    "USB-C charging cable 2m black",
    "ergonomic mesh office chair",
    "27-inch 4K monitor",
], "qty": [500, 30, 12]})

catalog_df = pd.DataFrame({"sku": [
    "CABLE-USBC-200CM-BLK",
    "CABLE-USBA-200CM-BLK",
    "CHAIR-MESH-ERG-ADJUSTABLE",
    "CHAIR-TASK-FIXED-BLK",
    "MON-27-4K-IPS-HDMI2",
], "unit_price": [8.99, 6.49, 349.00, 189.00, 429.00]})

result = fuzzy_join(
    orders_df, catalog_df,
    left_on="product_name", right_on="sku",
    llm=my_llm, embed_fn=my_embed,
    context="procurement match buyer product descriptions to supplier SKU codes",
    top_k=3, llm_threshold=0.7,
    llm_concurrency=10,
)

Step 1 — Embed both columns

Every value gets converted to a vector. No API call pure math, runs in milliseconds.

Value	Meaning captured
"USB-C charging cable 2m black"	cable / USB-C / length / color
"ergonomic mesh office chair"	seating / ergonomic / mesh
"27-inch 4K monitor"	display / size / resolution
"CABLE-USBC-200CM-BLK"	cable / USB-C / 200cm / black
"CHAIR-MESH-ERG-ADJUSTABLE"	seating / mesh / ergonomic
"MON-27-4K-IPS-HDMI2"	display / 27in / 4K

Step 2 — FAISS retrieves top-K candidates per row (no LLM)

For each left row, FAISS finds the top_k closest right vectors. Everything else is eliminated no LLM call needed.

Query: "USB-C charging cable 2m black" → top_k=3

Rank	Candidate	Embed Score	Reaches LLM?
0	CABLE-USBC-200CM-BLK	0.89	yes
1	CABLE-USBA-200CM-BLK	0.71	yes
2	CHAIR-TASK-FIXED-BLK	0.34	yes (shares "BLK")
—	CHAIR-MESH-ERG-ADJUSTABLE	0.11	eliminated
—	MON-27-4K-IPS-HDMI2	0.08	eliminated

Result: 3 LLM calls instead of 3 × 5 = 15 pair-by-pair calls.

Step 3 — One LLM call per left row scores all candidates

All top-K candidates go into a single prompt. The LLM scores each one and returns JSON one API call per row, all candidates scored together.

Prompt sent for "USB-C charging cable 2m black":

Context: procurement match buyer product descriptions to supplier SKU codes

LEFT: "USB-C charging cable 2m black"

Score each candidate (0.0–1.0):
0. CABLE-USBC-200CM-BLK
1. CABLE-USBA-200CM-BLK
2. CHAIR-TASK-FIXED-BLK

LLM response:

[
  {"index": 0, "score": 0.97, "reasoning": "USBC = USB-C, 200CM = 2m, BLK = black. Exact match on all three specs."},
  {"index": 1, "score": 0.38, "reasoning": "Correct length and color but USBA is USB-A, not USB-C. Wrong connector type."},
  {"index": 2, "score": 0.04, "reasoning": "This is a chair SKU. No relation to a cable."}
]

Apply llm_threshold=0.7:

Candidate	LLM Score	Decision
CABLE-USBC-200CM-BLK	0.97	best match joined
CABLE-USBA-200CM-BLK	0.38	below llm_threshold
CHAIR-TASK-FIXED-BLK	0.04	below llm_threshold

Step 4 — Merge matched rows

print(result)

product_name	qty	sku	unit_price
USB-C charging cable 2m black	500	CABLE-USBC-200CM-BLK	8.99
ergonomic mesh office chair	30	CHAIR-MESH-ERG-ADJUSTABLE	349.00
27-inch 4K monitor	12	MON-27-4K-IPS-HDMI2	429.00

The LLM correctly rejected CABLE-USBA-200CM-BLK (wrong connector) even though the embedding scored it 0.71. That's where the LLM earns its cost.

---

Cost & Scale

If you sent every possible pair to the LLM:

Left rows	Right rows	Pairs to score	Cost (gpt-4o-mini)
1,000	10,000	10,000,000	~$150
10,000	100,000	1,000,000,000	~$15,000
100,000	1,000,000	100,000,000,000	not feasible

llm-join avoids this with a two-stage pipeline.

Stage 1 — Embeddings narrow the search (cheap)

FAISS finds the top-K most similar candidates per row. Pure math, no API calls.

Input pairs	10,000 × 100,000 = 1,000,000,000
After FAISS (top_k=5)	50,000 candidate pairs
Eliminated for free	99.995% of all pairs

Stage 2 — LLM scores only the shortlist (accurate)

Your LLM sees a small batch of plausible candidates not the full cross product. One call per left row, all candidates scored in that call.

Setup	LLM calls	Estimated cost
10k × 100k, top_k=5	50,000	~$0.75
10k × 100k, top_k=3	30,000	~$0.45
10k × 100k, embed_skip_threshold=0.95	~5,000	~$0.08

Cost controls

result = fuzzy_join(
    df1, df2,
    left_on="vendor", right_on="supplier",
    llm=my_llm, embed_fn=my_embed,
    context="...",
    llm_concurrency=10,
    top_k=3,                    # fewer candidates = fewer LLM tokens per row
    embed_skip_threshold=0.95,  # skip LLM entirely if embed score is high enough
    max_llm_calls=1000,         # hard cap — warns and returns partial result if hit
)

---

Performance

Set llm_concurrency based on your API rate limit.

# Sequential use for debugging or if you have strict rate limits
fuzzy_join(..., llm_concurrency=1)

# Good starting point for most APIs
fuzzy_join(..., llm_concurrency=10)

# High throughput — check your rate limits first
fuzzy_join(..., llm_concurrency=50)

API	Suggested concurrency
OpenAI (tier 2+)	20–50
Azure OpenAI	10–30
Anthropic	5–20
Ollama (local)	as high as your machine allows

• Sync function (def my_llm) → ThreadPoolExecutor
• Async function (async def my_llm) → asyncio + semaphore

If a call fails after all retries, the top embedding match is used as fallback.

---

Usage

Basic join

result = fuzzy_join(
    orders_df, catalog_df,
    left_on="product_name",
    right_on="sku",
    llm=my_llm,
    embed_fn=my_embed,
    context="procurement — match buyer product descriptions to supplier SKU codes",
    llm_concurrency=10,
)

With domain context

Give the LLM more detail about each column to improve accuracy.

result = fuzzy_join(
    orders_df, catalog_df,
    left_on="product_name",
    right_on="sku",
    llm=my_llm,
    embed_fn=my_embed,
    context="procurement match buyer product descriptions to supplier SKU codes",
    column_context={
        "product_name": "plain English product description written by a buyer",
        "sku": "supplier stock-keeping unit code, typically uppercase with hyphens",
    },
    llm_concurrency=10,
)

See why matches were made

result = fuzzy_join(
    df1, df2,
    left_on="vendor",
    right_on="supplier_name",
    llm=my_llm,
    embed_fn=my_embed,
    context="company names match legal entity variants",
    llm_concurrency=10,
    return_reasoning=True,
)

print(result[["vendor", "supplier_name", "_llm_score", "_llm_reasoning", "_match_method", "_llm_candidates"]])

vendor	supplier_name	_llm_score	_llm_reasoning	_match_method	_llm_candidates
Goldman Sachs & Co.	The Goldman Sachs Group Inc	0.97	same firm, legal name variant	llm	[{"candidate": "The Goldman Sachs...", "embed_score": 0.94}, ...]

_llm_candidates shows exactly which candidates were sent to the LLM and their embedding scores useful for tuning top_k and threshold.

Control cost

result = fuzzy_join(
    df1, df2,
    left_on="vendor",
    right_on="supplier_name",
    llm=my_llm,
    embed_fn=my_embed,
    context="company names — match legal entity variants",
    llm_concurrency=10,
    embed_skip_threshold=0.95,  # skip LLM if embedding match is strong enough
    max_llm_calls=500,          # hard cap — warns and returns partial result if hit
    top_k=3,               # fewer candidates = fewer LLM tokens
)

Left join (audit unmatched rows)

how="left" keeps all left rows. Unmatched ones get NaN in the right columns useful for finding what failed to match.

result = fuzzy_join(
    df1, df2,
    left_on="vendor", right_on="supplier_name",
    llm=my_llm, embed_fn=my_embed,
    context="company names — match legal entity variants",
    llm_concurrency=10,
    how="left",
)

unmatched = result[result["supplier_name"].isna()]

how="full" is useful for reconciliation unmatched left rows have no match above threshold; unmatched right rows were never selected as a best match.

Multi-column join key

Pass a list to left_on or right_on values are concatenated automatically. Works for any number of columns.

# Match on product name + category combined
result = fuzzy_join(
    orders_df, catalog_df,
    left_on=["product_name", "category"],
    right_on="sku_description",
    llm=my_llm,
    embed_fn=my_embed,
    context="procurement — match buyer product + category to supplier SKU description",
    llm_concurrency=10,
)

Match all results

By default, only the best-scoring match above threshold is returned. Set match_all=True when one left value legitimately maps to multiple right values.

# "aspirin" should match all its known names
result = fuzzy_join(
    drugs_df, synonyms_df,
    left_on="generic_name", right_on="name",
    llm=my_llm, embed_fn=my_embed,
    context="pharmaceutical drug names match generics to all known synonyms",
    llm_concurrency=10,
    match_all=True,
    top_k=10,
)

generic_name	name	_llm_score
aspirin	acetylsalicylic acid	0.98
aspirin	Bayer Aspirin Tablet	0.95
aspirin	aspirin 100mg tablet	0.91

Chaining multiple joins

Each fuzzy_join returns a regular DataFrame pipe them like pd.merge.

# Step 1 — match vendors
step1 = fuzzy_join(
    df1, df2,
    left_on="vendor", right_on="supplier_name",
    llm=my_llm, embed_fn=my_embed,
    context="company names — match legal entity variants",
    llm_concurrency=10,
    how="left", return_reasoning=True,
)

# Step 2 — match products on result of step 1
result = fuzzy_join(
    step1, df3,
    left_on="product", right_on="catalog_item",
    llm=my_llm, embed_fn=my_embed,
    context="product names — match internal descriptions to catalog items",
    llm_concurrency=10,
    how="left", return_reasoning=True,
)

---

Works with Any LLM

Pass any callable that takes a prompt string and returns a string.

# OpenAI
import openai
client = openai.OpenAI()
def my_llm(prompt):
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content

# Anthropic
import anthropic
client = anthropic.Anthropic()
def my_llm(prompt):
    return client.messages.create(
        model="claude-opus-4-5", max_tokens=512,
        messages=[{"role": "user", "content": prompt}]
    ).content[0].text

# Google Gemini
import google.generativeai as genai
model = genai.GenerativeModel("gemini-2.0-flash")
def my_llm(prompt):
    return model.generate_content(prompt).text

# Ollama (local, free)
import ollama
def my_llm(prompt):
    return ollama.chat(
        model="llama3.2", messages=[{"role": "user", "content": prompt}]
    )["message"]["content"]

# Async LLM (uses asyncio path automatically)
async def my_llm(prompt):
    response = await async_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

---

Works with Any Embedding Function

Pass any callable (list[str]) -> np.ndarray (shape [n, dim], dtype float32).

import numpy as np

# OpenAI
import openai
client = openai.OpenAI()
def my_embed(texts):
    response = client.embeddings.create(model="text-embedding-3-small", input=texts)
    return np.array([d.embedding for d in response.data], dtype="float32")

# Cohere
import cohere
co = cohere.Client("YOUR_KEY")
def my_embed(texts):
    response = co.embed(texts=texts, model="embed-english-v3.0", input_type="search_document")
    return np.array(response.embeddings, dtype="float32")

# sentence-transformers (local, no API key needed)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
def my_embed(texts):
    return model.encode(texts, convert_to_numpy=True).astype("float32")

---

Features

• Any provider — OpenAI, Azure, Anthropic, Bedrock, Vertex, Ollama, or any private model. Sync and async supported.
• Enterprise friendly - User supply the LLM and embedding calls. Your data stays within your own infrastructure and chosen providers.
• Two-stage pipeline — embeddings eliminate 99%+ of pairs; your model scores only the shortlist
• Domain context — context and column_context inject column-level descriptions into every prompt
• Full join semantics — inner, left, right, full same as pd.merge
• Multi-column keys — pass a list to left_on / right_on
• No duplicate output rows — one best match per left row; ties broken by embedding rank
• Reasoning output — return_reasoning=True adds _llm_score, _llm_reasoning, _embed_rank, _match_method, _llm_candidates
• Cost controls — top_k, embed_skip_threshold, max_llm_calls
• Multi-match mode — match_all=True returns all candidates above threshold
• Parallel scoring — llm_concurrency controls how many calls run at once
• Retry with backoff — failed calls retry at 1s, 2s, 4s; falls back to top embed match on total failure
• MIT license

---

Parameters

Parameter	Default	Description
left_on	required	Column name(s) in df1. Pass a list for multi-column keys any number of columns supported.
right_on	required	Column name(s) in df2. Pass a list for multi-column keys any number of columns supported.
llm	required	Your LLM function. Sync: (str) -> str. Async: async (str) -> str.
embed_fn	required	Your embedding function. (list[str]) -> np.ndarray of shape [n, dim], dtype float32.
context	required	Describe what the columns represent and what kind of match to make. Injected into every LLM prompt.
llm_concurrency	required	How many LLM calls to run in parallel. 1 = sequential. Start with 10 and adjust based on your API rate limit.
column_context	{}	Per-column descriptions {"col": "description"} adds extra detail to the prompt beyond context.
top_k	5	How many embedding candidates to retrieve per left row before LLM scoring.
llm_threshold	0.7	Minimum LLM score (0–1) to accept a match. Rows where LLM scores below this are not joined.
how	"inner"	Join type: inner / left / right / full (full outer join).
embed_skip_threshold	1.0	Skip LLM when top embed similarity is at or above this value. Default 1.0 means only identical vectors (exact same text) skip LLM. Lower it (e.g. 0.92) to skip LLM for near-identical matches and save cost.
max_llm_calls	None	Hard cap on total LLM calls. Emits a warning and returns a partial result if hit.
max_retries	3	How many times to retry a failed LLM call (exponential backoff). Set 0 to disable. Falls back to top embed candidate on total failure.
batch_size	32	Batch size for embed_fn calls.
match_all	False	Return all candidates above threshold, not just the best. Use when one left value maps to multiple right values.
return_reasoning	False	Add debug columns: _llm_score, _llm_reasoning, _embed_rank, _match_method, _llm_candidates.

---

License

MIT