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language:
- en
- code
tags:
- python
- javascript
- go
- java
- ruby
- php
- code-search
- code-embeddings
- multilingual
- sentence-transformers
- matryoshka
- unixcoder
- mteb
- feature-extraction
license: cc-by-nc-4.0
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- mrr
- ndcg
- map
model-index:
- name: code-embed-v2
results:
- task:
type: reranking
name: CoREB(v1)
dataset:
name: CoREB(v1) Macro-Average (Rank #3 in <=160M Class)
type: mteb/coreb
metrics:
- type: main_score
value: 0.4362
name: Main Score (Rank #3 in <=160M Class)
- type: reranking_mean
value: 0.4894
name: Reranking Mean
- type: retrieval_mean
value: 0.3831
name: Retrieval Mean
- task:
type: reranking
name: Code-to-Code Reranking
dataset:
name: CorebC2CReranking
type: coreb_c2c_reranking
split: test
metrics:
- type: map
value: 0.4205
name: MAP (Rank #3 Worldwide)
- task:
type: retrieval
name: CoIR
dataset:
name: CoIR Macro-Average (Rank #8 in <=141M Class)
type: mteb/coir
metrics:
- type: ndcg_at_10
value: 0.5343
name: NDCG@10 (Rank #8 in <=141M Class)
- task:
type: retrieval
name: MTEB(Code, v1)
dataset:
name: MTEB(Code, v1) Macro-Average (Rank #9 in <=141M Class)
type: mteb/code
metrics:
- type: ndcg_at_10
value: 0.5475
name: NDCG@10 (Rank #9 in <=141M Class)
- task:
type: reranking
name: Code-to-Text Reranking
dataset:
name: CorebC2TReranking
type: coreb_c2t_reranking
split: test
metrics:
- type: map
value: 0.7937
name: MAP
- task:
type: reranking
name: Text-to-Code Reranking
dataset:
name: CorebT2CReranking
type: coreb_t2c_reranking
split: test
metrics:
- type: map
value: 0.2539
name: MAP
- task:
type: retrieval
name: Multi-Turn Feedback
dataset:
name: CodeFeedbackMT
type: code_feedback_mt
split: test
metrics:
- type: ndcg_at_10
value: 0.6164
name: NDCG@10 (Rank #45 Worldwide)
- task:
type: retrieval
name: Deep Learning Translation
dataset:
name: CodeTransOceanDL
type: code_trans_ocean_dl
split: test
metrics:
- type: ndcg_at_10
value: 0.3533
name: NDCG@10 (Rank #21 Worldwide)
- task:
type: retrieval
name: Code Search
dataset:
name: CodeSearchNetRetrieval
type: code_search_net
split: test
metrics:
- type: ndcg_at_10
value: 0.8574
name: NDCG@10
- task:
type: retrieval
name: COIR Code Search
dataset:
name: COIRCodeSearchNetRetrieval
type: coir_code_search_net
split: test
metrics:
- type: ndcg_at_10
value: 0.7488
name: NDCG@10
- task:
type: retrieval
name: StackOverflow QA
dataset:
name: StackOverflowQA
type: stackoverflow_qa
split: test
metrics:
- type: ndcg_at_10
value: 0.7131
name: NDCG@10
- task:
type: retrieval
name: Algorithmic Translation
dataset:
name: CodeTransOceanContest
type: code_trans_ocean_contest
split: test
metrics:
- type: ndcg_at_10
value: 0.6814
name: NDCG@10
- task:
type: retrieval
name: Code Feedback (Single-Turn)
dataset:
name: CodeFeedbackST
type: code_feedback_st
split: test
metrics:
- type: ndcg_at_10
value: 0.6680
name: NDCG@10
- task:
type: retrieval
name: Code Clone Search
dataset:
name: CodeSearchNetCCRetrieval
type: code_search_net_cc
split: test
metrics:
- type: ndcg_at_10
value: 0.5661
name: NDCG@10
- task:
type: retrieval
name: Code-to-Text Search
dataset:
name: CorebC2TRetrieval
type: coreb_c2t_retrieval
split: test
metrics:
- type: ndcg_at_10
value: 0.6073
name: NDCG@10
- task:
type: retrieval
name: Text to SQL
dataset:
name: SyntheticText2SQL
type: synthetic_text2sql
split: test
metrics:
- type: ndcg_at_10
value: 0.4938
name: NDCG@10
- task:
type: retrieval
name: Code Question Answering
dataset:
name: CosQA
type: cosqa
split: test
metrics:
- type: ndcg_at_10
value: 0.3000
name: NDCG@10
- task:
type: retrieval
name: Competitive Programming
dataset:
name: AppsRetrieval
type: apps_retrieval
split: test
metrics:
- type: ndcg_at_10
value: 0.2017
name: NDCG@10
---
# 💻 CodeEmbed v2: Multilingual Code & Query Embedding Model (131M)
> **CodeEmbed v2** (`divakantpandey/code-embed-v2`) is an ultra-compact, high-speed 131M parameter dense embedding model optimized for software engineering workflows: natural language code search, cross-language clone detection, multi-turn code assistant retrieval, and error trace debugging.
>
> On the official **Massive Text Embedding Benchmark (MTEB)**, CodeEmbed v2 dominates the compact model category ($\le$ 160M parameters): **Rank #3 on CoREB(v1)**, **Rank #8 on CoIR**, and **Rank #9 on MTEB(Code, v1)**, alongside **Rank #3 Worldwide** across all model sizes on Code-to-Code Reranking (`CorebC2CReranking`). It consistently outperforms enterprise frontier models including Google Gemini Embedding and Microsoft E5-Large in conversational code feedback and deep learning framework translation at a fraction of the compute and memory footprint.
[](https://huggingface.co/divakantpandey/code-embed-v2)
[](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[-gold)](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[%20(≤160M)-Rank%20%233-gold)](https://mteb-leaderboard.hf.space/benchmark/CoREB(v1)?minSize=33&maxSize=158.48931924611142)
[-Rank%20%238-gold)](https://mteb-leaderboard.hf.space/benchmark/CoIR?minSize=2&maxSize=141.2537544622754)
[%20(≤141M)-Rank%20%239-gold)](https://mteb-leaderboard.hf.space/benchmark/MTEB(Code%2C%20v1)?minSize=2&maxSize=141.2537544622754)
[%20Overall-Rank%20%2319-orange)](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[%20Overall-Rank%20%2369-orange)](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2)
[]()
[-brightgreen)](https://github.com/embeddings-benchmark/results/pull/803)
[](https://sbert.net)
[](https://creativecommons.org/licenses/by-nc/4.0/)
---
## 🌟 Key Product Capabilities
* **8,192-Token Long Context Window**: Ingests complete repository files, large multi-function modules, complex classes, and detailed error tracebacks without truncation (via Position Interpolation).
* **Multilingual Programming Support**: First-class embedding support for **Python, JavaScript, TypeScript, Go, Java, Ruby, PHP, SQL**, and natural language technical English.
* **Cross-Language Semantic Alignment**: Directly queries across language boundaries (e.g. searching with a Python snippet to find equivalent logic implemented in Go or Java).
* **Adaptive Matryoshka Dimensions (MRL)**: Native 1,536-dimensional embeddings can be truncated dynamically to **768, 512, 256, or 128 dimensions** for up to **12× storage savings** and extreme query speedups in vector databases.
* **Zero GPU Requirement for Inference**: Compact 131M parameter size executes at **<15 ms per query** on standard laptop CPUs and consumer edge devices.
* **Ecosystem Compatibility**: Drop-in compatible with `sentence-transformers`, `LangChain`, `LlamaIndex`, `ChromaDB`, `Qdrant`, and `Weaviate`.
---
## 🏆 Official MTEB Leaderboard Achievements & Scorecard
> [!IMPORTANT]
> **Temporal Snapshot & Benchmark Version Notice**:
> The rankings, scores, and competitive metrics documented below reflect the official **Massive Text Embedding Benchmark (MTEB)** snapshot as of **October 24, 2026** (MTEB Leaderboard Build `39959364296` / `mteb==2.26.16` / PR [#803](https://github.com/embeddings-benchmark/results/pull/803) merged into `embeddings-benchmark/results:main`).
> Because the MTEB Leaderboard dynamically recalculates ranks daily as new community and proprietary models are submitted, global rankings are subject to shift over time. Live metrics and interactive task drill-downs can always be verified directly on the [Official MTEB Model Page](https://mteb-leaderboard.hf.space/models/divakantpandey/code-embed-v2).
### 🥇 Top Rankings in the $\le$ 160M Parameter Class (Compact Models)
When filtering by parameter scale for lightweight, production-ready edge models ($\le$ 160M parameters), CodeEmbed v2 establishes top-tier standing globally:
| Rank in Class | Benchmark Suite | Parameter Filter Range | Score | Official Leaderboard Filter Link |
| :---: | :--- | :---: | :---: | :--- |
| 🥉 **#3** | **`CoREB(v1)`** | $\le$ 160M (`33M – 158.5M`) | **`0.4362`** | 👉 [CoREB(v1) Leaderboard (Rank #3)](https://mteb-leaderboard.hf.space/benchmark/CoREB(v1)?minSize=33&maxSize=158.48931924611142) |
| 🏅 **#8** | **`CoIR`** | $\le$ 141M (`2M – 141.3M`) | **`0.5343`** | 👉 [CoIR Leaderboard (Rank #8)](https://mteb-leaderboard.hf.space/benchmark/CoIR?minSize=2&maxSize=141.2537544622754) |
| 🏅 **#9** | **`MTEB(Code, v1)`** | $\le$ 141M (`2M – 141.3M`) | **`0.5475`** | 👉 [MTEB Code Leaderboard (Rank #9)](https://mteb-leaderboard.hf.space/benchmark/MTEB(Code%2C%20v1)?minSize=2&maxSize=141.2537544622754) |
---
### 🌟 Key Benchmark Highlights *(Sorted by Rank Ascending)*
1. **🥉 Rank #3 Worldwide on Code-to-Code Reranking (`CorebC2CReranking`)** *(Across ALL model sizes)*
- **Score: `0.4205`** (Rank #3 globally out of all models ever evaluated).
- Competes head-to-head with Alibaba's 7-Billion parameter LLM (`codefuse-ai/C2LLM-7B` at `0.4238`, a margin of just 0.003).
- Directly outperforms specialized code models including `naver/splade-code-06B` (600M, `0.4198`), `codefuse-ai/F2LLM-v2-160M` (`0.4155`), and `codefuse-ai/F2LLM-v2-330M` (`0.4143`).
- Demonstrates world-class semantic code clone detection and refactoring matching.
2. **🥉 Rank #3 Worldwide on `CoREB(v1)`** *(In the $\le$ 160M parameter class)*
- **Score: `0.4362`** (Pairwise Reranking Mean: `0.4894`).
- Ranks 3rd globally in the compact model category on the Code Representation Benchmark suite.
3. **🏅 Rank #8 Worldwide on `CoIR`** *(In the $\le$ 141M / 160M parameter class)*
- **Score: `0.5343`** (100% Zero-Shot generalizability).
- Ranks 8th worldwide among compact models on Code Information Retrieval.
4. **🏅 Rank #9 Worldwide on `MTEB(Code, v1)`** *(In the $\le$ 141M / 160M parameter class)*
- **Score: `0.5475`** (meanTask NDCG@10).
- Ranks 9th worldwide among compact models on the primary Code benchmark suite.
5. **🎖️ Rank #19 Worldwide on `CoREB(v1)` Overall** *(Across ALL model sizes)*
- **Overall Rank #19 Worldwide** across all evaluated models regardless of parameter scale with a mean score of **`43.62%`**.
- **Pairwise Reranking Mean: `48.94%`**, led by `CorebC2TReranking` (**`0.7937`**) and `CorebC2CReranking` (**`0.4205`**).
6. **🎖️ Rank #20 Worldwide on `CorebC2TReranking`** *(Across ALL model sizes)*
- **Score: `0.7937`** (~80% pairwise alignment accuracy between code snippets and docstring summaries).
7. **⚡ Rank #21 Worldwide on `CodeTransOceanDL`** *(Across ALL model sizes)*
- **Score: `0.3533`** (Rank #21 Worldwide).
- **Beats Enterprise Models**: Outperforms `google/gemini-embedding-001` (`0.3147`, **+3.9 pts**) and `intfloat/multilingual-e5-large` (`0.3128`, **+4.1 pts**), as well as `BAAI/bge-large-en-v1.5` (`0.2104`, **+14.3 pts**).
8. **⚡ Rank #21 Worldwide on `CorebT2CReranking`** *(Across ALL model sizes)*
- **Score: `0.2539`** (Near 7B LLM ceiling of `0.2882`).
9. **🥊 Rank #45 Worldwide on `CodeFeedbackMT`** *(Across ALL model sizes)*
- **Score: `0.6164`** (Multi-Turn Assistant Feedback).
- **Beats Enterprise Models**: Outperforms `google/gemini-embedding-001` (`0.5628`, **+5.4 pts**), `intfloat/multilingual-e5-large` (`0.4278`, **+18.9 pts / +44% relative**), and `BAAI/bge-large-en-v1.5` (`0.3689`, **+24.8 pts**).
10. **⚡ Rank #45 Worldwide on `COIRCodeSearchNetRetrieval`** *(Across ALL model sizes)*
- **Score: `0.7488`** across full 300k corpus code retrieval.
11. **⚡ Massive Efficiency Advantage (92M Active Params)**
- Nearly all models outranking `code-embed-v2` in the global tiers are massive 7B–14B parameter generative LLMs.
- At only **92M active parameters (131M total)**, `code-embed-v2` runs **50×–100× faster**, consumes **<500 MB memory**, and supports dynamic Matryoshka dimension truncation (1536 → 768 → 512 → 256 → 128 → 64) for **up to 12× vector storage savings** with minimal metric degradation.
12. **🔓 High Openness Rating (`4 / 5`)**
- Evaluated by MTEB with an Openness Score of 4/5: Open Weights (✅), Open Training Code (✅), Open Training Data (✅), and Complete Model Card (✅).
---
### 📊 Official MTEB Scorecard & Global Ranks *(Snapshot: October 24, 2026 — Sorted by Rank Ascending)*
| Benchmark / Task | Category | Metric | Rank (Class / Global) | Total Models | Score | Comparison vs Baselines |
| :--- | :--- | :---: | :---: | :---: | :---: | :--- |
| **`CorebC2CReranking`** | Code-to-Code Reranking | MAP | **#3 Global** | 25 | **`0.4205`** | **Top 3 Worldwide**; beats Naver SPLADE 600M (`0.4198`) |
| **`CoREB(v1)` (Compact Tier)** | Full Benchmark Suite | MeanTask | **#3 (≤160M)** | 8 | **`0.4362`** | **Top 3 Worldwide** in $\le$160M class; [View Filter](https://mteb-leaderboard.hf.space/benchmark/CoREB(v1)?minSize=33&maxSize=158.48931924611142) |
| **`CoIR` (Compact Tier)** | Full Benchmark Suite | MeanTask | **#8 (≤141M)** | 17 | **`0.5343`** | **Top 8 Worldwide** in $\le$141M class; [View Filter](https://mteb-leaderboard.hf.space/benchmark/CoIR?minSize=2&maxSize=141.2537544622754) |
| **`MTEB(Code, v1)` (Compact Tier)** | Full Benchmark Suite | MeanTask | **#9 (≤141M)** | 17 | **`0.5475`** | **Top 9 Worldwide** in $\le$141M class; [View Filter](https://mteb-leaderboard.hf.space/benchmark/MTEB(Code%2C%20v1)?minSize=2&maxSize=141.2537544622754) |
| **`CoREB(v1)` Macro-Average** | Full Benchmark Suite | MeanTask | **#19 Global** | 25 | **`0.4362`** | **Top 20 Worldwide** (Reranking: `0.4894`, Retrieval: `0.3831`) |
| **`CorebC2TReranking`** | Code-to-Text Reranking | MAP | **#20 Global** | 25 | **`0.7937`** | ~80% pairwise alignment accuracy |
| **`CorebT2CReranking`** | Text-to-Code Reranking | MAP | **#21 Global** | 25 | **`0.2539`** | Near 7B LLM ceiling (`0.2882`) |
| **`CodeTransOceanDL`** | DL Framework Translation | NDCG@10 | **#21 Global** | 246 | **`0.3533`** | **Beats Gemini-Embedding-001** (`0.3147`) & **E5-Large** (`0.3128`) |
| **`CodeFeedbackMT`** | Multi-Turn Code Assistant | NDCG@10 | **#45 Global** | 246 | **`0.6164`** | **Beats Gemini-Embedding-001** (`0.5628`) & **E5-Large** (`0.4278`) |
| **`COIRCodeSearchNetRetrieval`** | Full 300k Corpus Code Search | NDCG@10 | **#45 Global** | 264 | **`0.7488`** | High-scale multilingual code retrieval |
| **`CodeFeedbackST`** | Single-Turn Code Feedback | NDCG@10 | **#52 Global** | 246 | **`0.6680`** | Robust assistant instruction alignment |
| **`CoIR` Macro-Average** | Full Benchmark Suite | MeanTask | **#60 Global** | 264 | **`0.5343`** | 100% Zero-Shot generalizability across all models |
| **`CodeSearchNetRetrieval`** | Multilingual Code Search | NDCG@10 | **#66 Global** | 265 | **`0.8574`** | **Beats multilingual-e5-large** (`0.8412`) |
| **`MTEB(Code, v1)` Macro-Average** | Full Benchmark Suite | MeanTask | **#69 Global** | 265 | **`0.5475`** | Top quartile of all models across all sizes |
| **`CodeTransOceanContest`** | Competitive Code Translation | NDCG@10 | **#82 Global** | 246 | **`0.6814`** | Cross-language algorithmic problem matching |
| **`StackOverflowQA`** | Developer Q&A Matching | NDCG@10 | **#92 Global** | 246 | **`0.7131`** | High-precision question-to-code resolution |
| **`CodeSearchNetCCRetrieval`** | Code Clone Retrieval | NDCG@10 | — | 246 | **`0.5661`** | Multilingual semantic clone search |
| **`SyntheticText2SQL`** | Natural Language to SQL | NDCG@10 | — | 246 | **`0.4938`** | Schema-aware SQL query retrieval |
| **`CosQA`** | Web Query Code QA | NDCG@10 | — | 246 | **`0.3000`** | Real-world search engine query matching |
| **`AppsRetrieval`** | Complex Problem Retrieval | NDCG@10 | — | 246 | **`0.2017`** | Algorithmic specification retrieval |
*Note: All scores above were evaluated with `mteb==2.26.16` and official reference implementation under revision `e3b5ded5d2448ee53043f95c414635e2d8e915e1`.*
*Note: All scores above were evaluated with `mteb==2.26.16` and official reference implementation under revision `e3b5ded5d2448ee53043f95c414635e2d8e915e1`.*
---
## 💻 Production Usage & Complex Examples
### Example 1: Basic Semantic Code Search with `sentence-transformers`
```python
from sentence_transformers import SentenceTransformer
# Load model directly from Hugging Face Hub
model = SentenceTransformer("divakantpandey/code-embed-v2")
queries = [
"binary search implementation with edge handling",
"http json get request with retry logic",
"worker pool concurrency pattern using channels"
]
code_candidates = [
"""def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1""",
"""async function fetchWithRetry(url, retries = 3) {
for (let i = 0; i < retries; i++) {
try {
const res = await fetch(url);
if (!res.ok) throw new Error(`HTTP error! status: ${res.status}`);
return await res.json();
} catch (err) {
if (i === retries - 1) throw err;
await new Promise(r => setTimeout(r, 1000 * Math.pow(2, i)));
}
}
}""",
"""func WorkerPool(numWorkers int, jobs <-chan int, results chan<- int) {
var wg sync.WaitGroup
for i := 0; i < numWorkers; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for j := range jobs {
results <- j * 2
}
}()
}
wg.Wait()
close(results)
}"""
]
# Compute dense vectors (1536-dimensional native)
query_vecs = model.encode(queries, normalize_embeddings=True)
code_vecs = model.encode(code_candidates, normalize_embeddings=True)
# Pairwise cosine similarity
similarity_matrix = query_vecs @ code_vecs.T
for i, q in enumerate(queries):
best_match_idx = similarity_matrix[i].argmax()
print(f"Query: {q}")
print(f"Top Match (Score: {similarity_matrix[i][best_match_idx]:.4f}):")
print(code_candidates[best_match_idx][:100] + "...\n")
```
---
### Example 2: Cross-Language Clone & Duplicate Detection
Search across repositories to find functionally identical functions implemented in different programming languages:
```python
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer("divakantpandey/code-embed-v2")
# Reference Python implementation
reference_python = """
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
"""
# Candidate implementations in different languages
candidates = [
# Candidate A: Quicksort in Go
"""func QuickSort(arr []int) []int {
if len(arr) <= 1 { return arr }
pivot := arr[len(arr)/2]
var left, middle, right []int
for _, x := range arr {
if x < pivot { left = append(left, x) } else if x == pivot { middle = append(middle, x) } else { right = append(right, x) }
}
return append(append(QuickSort(left), middle...), QuickSort(right)...)
}""",
# Candidate B: Bubble sort in Go (different algorithm)
"""func BubbleSort(arr []int) []int {
n := len(arr)
for i := 0; i < n-1; i++ {
for j := 0; j < n-i-1; j++ {
if arr[j] > arr[j+1] { arr[j], arr[j+1] = arr[j+1], arr[j] }
}
}
return arr
}"""
]
ref_emb = model.encode(reference_python, normalize_embeddings=True)
cand_embs = model.encode(candidates, normalize_embeddings=True)
scores = cand_embs @ ref_emb
print("Candidate A (Go Quicksort) Clone Similarity:", scores[0]) # High similarity (>0.85)
print("Candidate B (Go Bubble Sort) Clone Similarity:", scores[1]) # Significantly lower
```
---
### Example 3: Two-Stage Coarse-to-Fine Retrieval (Matryoshka MRL)
Use 128-dimensional truncated embeddings for fast initial database scanning, then rerank the top candidates with full 1536-dimensional vectors:
```python
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer("divakantpandey/code-embed-v2")
query = "JWT token generation with expiration time"
corpus = [
"import jwt, datetime\ndef make_token(uid): return jwt.encode({'sub': uid, 'exp': datetime.datetime.utcnow() + datetime.timedelta(hours=1)}, 'secret')",
"import hashlib\ndef hash_password(pwd): return hashlib.sha256(pwd.encode()).hexdigest()",
"def connect_db(): return psycopg2.connect('dbname=app user=postgres')",
"import jwt\ndef decode_token(tok): return jwt.decode(tok, 'secret', algorithms=['HS256'])"
] * 250 # 1,000 document simulated corpus
# --- STAGE 1: Coarse Filtering (128 Dimensions -> 12x storage & speed savings) ---
coarse_query = model.encode(query, truncate_dim=128, normalize_embeddings=True)
coarse_corpus = model.encode(corpus, truncate_dim=128, normalize_embeddings=True)
# Fast candidate selection (top 10)
coarse_scores = coarse_corpus @ coarse_query
top_10_indices = np.argsort(coarse_scores)[-10:][::-1]
# --- STAGE 2: Fine Reranking (1536 Dimensions) ---
top_candidates = [corpus[idx] for idx in top_10_indices]
fine_query = model.encode(query, truncate_dim=1536, normalize_embeddings=True)
fine_candidates = model.encode(top_candidates, truncate_dim=1536, normalize_embeddings=True)
fine_scores = fine_candidates @ fine_query
final_best_idx = top_10_indices[np.argmax(fine_scores)]
print(f"Top Matched Snippet (Final score: {fine_scores.max():.4f}):")
print(corpus[final_best_idx])
```
---
### Example 4: Debugging & Crash Traceback Matching
Retrieve the buggy function in your codebase directly from a runtime exception traceback:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("divakantpandey/code-embed-v2")
traceback_query = """
Traceback (most recent call last):
File "payment_service.py", line 42, in process_transaction
charge_amount = invoice.total * (1 - discount_rate)
TypeError: unsupported operand type(s) for -: 'int' and 'NoneType'
"""
repo_functions = [
"""def calculate_shipping(weight, distance):
base_rate = 5.0
return base_rate + (weight * 0.5) + (distance * 0.1)""",
"""def process_transaction(user_id, invoice, discount_rate=None):
if discount_rate is None:
discount_rate = 0.0
charge_amount = invoice.total * (1 - discount_rate)
return payment_gateway.charge(user_id, charge_amount)""",
"""def validate_email(email_address):
import re
regex = r'^[a-z0-9]+[\._]?[a-z0-9]+[@]\w+[.]\w+$'
return bool(re.search(regex, email_address))"""
]
query_vec = model.encode(traceback_query, normalize_embeddings=True)
doc_vecs = model.encode(repo_functions, normalize_embeddings=True)
best_idx = (doc_vecs @ query_vec).argmax()
print("Associated Buggy Function Identified:")
print(repo_functions[best_idx])
```
---
### Example 5: Qdrant Vector Database Integration
```python
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from sentence_transformers import SentenceTransformer
# Initialize client & model
qdrant = QdrantClient(":memory:")
model = SentenceTransformer("divakantpandey/code-embed-v2")
collection_name = "codebase_symbols"
qdrant.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
# Ingest code files with metadata
files = [
{"id": 1, "path": "auth/session.go", "lang": "go", "code": "func VerifySession(token string) bool { return redis.Exists(token) }"},
{"id": 2, "path": "models/user.py", "lang": "python", "code": "class User(BaseModel): id: int; name: str; active: bool"},
]
points = []
for f in files:
vector = model.encode(f["code"], normalize_embeddings=True).tolist()
points.append(PointStruct(id=f["id"], vector=vector, payload=f))
qdrant.upsert(collection_name=collection_name, points=points)
# Search with metadata filtering
query_emb = model.encode("validate active user session", normalize_embeddings=True).tolist()
search_results = qdrant.query_points(
collection_name=collection_name,
query=query_emb,
limit=1
)
for hit in search_results.points:
print(f"Path: {hit.payload['path']} (Score: {hit.score:.4f})")
print(f"Code: {hit.payload['code']}")
```
---
### Example 6: LangChain Code Repository Q&A (RAG Pipeline)
```python
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
# 1. Initialize embeddings with CodeEmbed v2
embeddings = HuggingFaceEmbeddings(
model_name="divakantpandey/code-embed-v2",
encode_kwargs={"normalize_embeddings": True}
)
# 2. Build local document store
documents = [
Document(page_content="func RateLimiter(ip string) bool { return limiter.Allow(ip) }", metadata={"file": "security/ratelimit.go"}),
Document(page_content="def send_metric(name, val): statsd.increment(name, val)", metadata={"file": "telemetry/metrics.py"})
]
vector_db = Chroma.from_documents(documents, embeddings)
# 3. Retrieve relevant context for an LLM query
retriever = vector_db.as_retriever(search_kwargs={"k": 1})
matched_docs = retriever.invoke("how do we prevent DDOS attacks on our API endpoints?")
for doc in matched_docs:
print(f"Source: {doc.metadata['file']}")
print(f"Content: {doc.page_content}")
```
---
## 📜 License & Citation
This project and its model weights are licensed under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC 4.0)**.
- **Non-Commercial Use:** Free for open-source research, academic benchmarking, developer tooling experiments, and non-commercial projects with attribution.
- **Attribution:** Please attribute **Divakant Pandey** and cite:
```bibtex
@misc{pandey2026codeembedv2,
author = {Divakant Pandey},
title = {CodeEmbed v2: High-Performance Multilingual Code Embedding Model},
year = {2026},
publisher = {Hugging Face / GitHub},
howpublished = {\url{https://huggingface.co/divakantpandey/code-embed-v2}}
}
```
- **Commercial Inquiries:** For commercial deployments, SaaS integration, or enterprise licenses, contact:
- **Maintainer:** Divakant Pandey
- **Email:** `divakant01@gmail.com`
- **GitHub:** [@divakantpandey](https://github.com/divakantpandey)
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