README.md

metadata

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
          type: mteb/coreb
        metrics:
          - type: main_score
            value: 0.4362
            name: Main Score (Rank
          - 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
      - task:
          type: retrieval
          name: CoIR
        dataset:
          name: CoIR Macro-Average (Rank
          type: mteb/coir
        metrics:
          - type: ndcg_at_10
            value: 0.5343
            name: NDCG@10 (Rank
      - task:
          type: retrieval
          name: MTEB(Code, v1)
        dataset:
          name: MTEB(Code, v1) Macro-Average (Rank
          type: mteb/code
        metrics:
          - type: ndcg_at_10
            value: 0.5475
            name: NDCG@10 (Rank
      - 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
      - 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
      - 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: 0.668
            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: 0.3
            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.

---

🌟 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

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 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.

🥇 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)
🏅 #8	CoIR	$\le$ 141M (2M – 141.3M)	0.5343	👉 CoIR Leaderboard (Rank #8)
🏅 #9	MTEB(Code, v1)	$\le$ 141M (2M – 141.3M)	0.5475	👉 MTEB Code Leaderboard (Rank #9)

---

🌟 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
CoIR (Compact Tier)	Full Benchmark Suite	MeanTask	#8 (≤141M)	17	0.5343	Top 8 Worldwide in $\le$141M class; View Filter
MTEB(Code, v1) (Compact Tier)	Full Benchmark Suite	MeanTask	#9 (≤141M)	17	0.5475	Top 9 Worldwide in $\le$141M class; View Filter
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

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:

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:

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:

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

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)

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:

  @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