embedding distance comparsion between models

poetry.lock

@@ -638,6 +638,98 @@ humanfriendly = ">=9.1"
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 cron = ["capturer (>=2.4)"]
 
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+
 [[package]]
 name = "kubernetes"
 version = "32.0.0"
@@ -1803,6 +2061,65 @@ files = [
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 ]
 
+[[package]]
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+version = "3.10.0"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.10"
+groups = ["evaluations"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
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+    {file = "matplotlib-3.10.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ae80dc3a4add4665cf2faa90138384a7ffe2a4e37c58d83e115b54287c4f06ef"},
+    {file = "matplotlib-3.10.0.tar.gz", hash = "sha256:b886d02a581b96704c9d1ffe55709e49b4d2d52709ccebc4be42db856e511278"},
+]
+
+[package.dependencies]
+contourpy = ">=1.0.1"
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.3.1"
+numpy = ">=1.23"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
+[package.extras]
+dev = ["meson-python (>=0.13.1,<0.17.0)", "pybind11 (>=2.13.2,!=2.13.3)", "setuptools (>=64)", "setuptools_scm (>=7)"]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -2118,7 +2435,7 @@ version = "2.2.2"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.10"
-groups = ["main", "huggingface"]
+groups = ["main", "evaluations", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e"},
@@ -2787,7 +3104,7 @@ version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev", "huggingface"]
+groups = ["main", "dev", "evaluations", "huggingface"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
@@ -2800,7 +3117,7 @@ version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"},
@@ -2888,7 +3205,7 @@ version = "11.1.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.9"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "pillow-11.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:e1abe69aca89514737465752b4bcaf8016de61b3be1397a8fc260ba33321b3a8"},
@@ -3423,6 +3740,22 @@ files = [
 [package.extras]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "pyparsing"
+version = "3.2.1"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.9"
+groups = ["evaluations"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
+    {file = "pyparsing-3.2.1-py3-none-any.whl", hash = "sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1"},
+    {file = "pyparsing-3.2.1.tar.gz", hash = "sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pypika"
 version = "0.48.9"
@@ -3514,7 +3847,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
@@ -3559,7 +3892,7 @@ version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
@@ -3902,6 +4235,29 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+description = "Statistical data visualization"
+optional = false
+python-versions = ">=3.8"
+groups = ["evaluations"]
+markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
+files = [
+    {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
+    {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
+
 [[package]]
 name = "semantic-version"
 version = "2.10.0"
@@ -3960,7 +4316,7 @@ version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
@@ -4448,7 +4804,7 @@ version = "2025.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
-groups = ["main"]
+groups = ["main", "evaluations"]
 markers = "python_version <= \"3.11\" or python_version >= \"3.12\""
 files = [
     {file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
@@ -4973,4 +5329,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10, <3.13"
-content-hash = "1fadad8dff111d7324b75ce2fe892dd2321c743d771a1d8754e6d8f1bb1b181a"
+content-hash = "4877a761765907ab7a7f19e8933a36d76289b82b9670fc2feeb9691db00bee39"

pyproject.toml

@@ -40,6 +40,13 @@ optional=true
 torch = "^2.6.0"
 transformers = "^4.48.2"
 
+[tool.poetry.group.evaluations]
+optional=true
+
+[tool.poetry.group.evaluations.dependencies]
+matplotlib = "^3.10.0"
+seaborn = "^0.13.2"
+
 [build-system]
 requires = ["poetry-core>=2.0.0,<3.0.0"]
 build-backend = "poetry.core.masonry.api"

src/know_lang_bot/evaluation/embedding_evaluation.py

@@ -0,0 +1,195 @@
+from pathlib import Path
+import matplotlib.pyplot as plt
+import seaborn as sns
+from typing import List, Dict, Tuple, Optional
+import chromadb
+import numpy as np
+from pydantic import BaseModel
+from dataclasses import dataclass
+from know_lang_bot.chat_bot.chat_graph import ChatResult
+from know_lang_bot.config import AppConfig, EmbeddingConfig
+import json
+from know_lang_bot.evaluation.chatbot_evaluation import EvalCase, TRANSFORMER_TEST_CASES
+from know_lang_bot.models.embeddings import generate_embedding, EmbeddingVector
+
+@dataclass
+class ConfigEvalResult:
+    """Results for a single configuration"""
+    config_name: str
+    distances: List[float]
+    stats: Dict[str, float]
+
+class RetrievalMetrics(BaseModel):
+    """Metrics for retrieval analysis"""
+    distances: List[float]
+    similarity_scores: List[float]
+    chunk_count: int
+
+class EnhancedChatResult(ChatResult):
+    """Extended ChatResult with retrieval metrics"""
+    retrieval_metrics: Optional[RetrievalMetrics] = None
+
+def _embedding_cache_path(embdding_config: EmbeddingConfig) -> Path:
+    """Get the cache path for a specific embedding configuration"""
+    return Path(f"embeddings_{embdding_config.model_name}_{embdding_config.model_provider.value}.json")
+
+def load_cached_embeddings(cache_path: Path, embdding_config: EmbeddingConfig) -> Optional[Dict[str, EmbeddingVector]]:
+    """Load cached embeddings for a specific configuration"""
+    cache_file = cache_path / _embedding_cache_path(embdding_config)
+    if cache_file.exists():
+        with open(cache_file, 'r') as f:
+            return json.load(f)
+    return None
+
+def save_cached_embeddings(cache_path: Path, config: EmbeddingConfig, embeddings: Dict[str, EmbeddingVector]):
+    """Save embeddings to cache"""
+    cache_path.mkdir(parents=True, exist_ok=True)
+    cache_file = cache_path / _embedding_cache_path(config)
+    with open(cache_file, 'w') as f:
+        json.dump(embeddings, f)
+
+async def analyze_embedding_distributions(
+    test_cases: List[EvalCase],
+    configs: List[Tuple[str, AppConfig]],
+    cache_path : Path
+) -> List[ConfigEvalResult]:
+    """Analyze embedding distance distributions for multiple configurations"""
+    results = []
+    
+    for config_name, config in configs:
+        # Try to load cached embeddings first
+        cached_embeddings = load_cached_embeddings(cache_path, config.embedding)
+        
+        if cached_embeddings is None:
+            print(f"Generating new embeddings for {config_name}...")
+            # Generate embeddings for all test cases
+            questions = [case.question for case in test_cases]
+            try:
+                embeddings = generate_embedding(questions, config.embedding)
+                
+                # Cache the embeddings
+                cached_embeddings = {
+                    question: embedding 
+                    for question, embedding in zip(questions, embeddings)
+                }
+                save_cached_embeddings(cache_path, config.embedding, cached_embeddings)
+            except Exception as e:
+                print(f"Error generating embeddings for {config_name}: {str(e)}")
+                continue
+        else:
+            print(f"Using cached embeddings for {config_name}")
+
+        # Get collection for this config
+        collection = chromadb.PersistentClient(
+            path=str(config.db.persist_directory)
+        ).get_collection(f"{config.db.collection_name}")
+        
+        distances = []
+        
+        # Query each test case
+        for case in test_cases:
+            query_results = collection.query(
+                query_embeddings=[cached_embeddings[case.question]],
+                n_results=10,
+                include=['distances']
+            )
+            distances.extend(query_results['distances'][0])
+        
+        # Calculate statistics
+        stats = {
+            'mean': float(np.mean(distances)),
+            'std': float(np.std(distances)),
+            'median': float(np.median(distances)),
+            'min': float(np.min(distances)),
+            'max': float(np.max(distances))
+        }
+        
+        results.append(ConfigEvalResult(
+            config_name=config_name,
+            distances=distances,
+            stats=stats
+        ))
+    
+    return results
+
+def plot_distance_distributions(results: List[ConfigEvalResult]):
+    """Plot and compare distance distributions for all configurations"""
+    plt.figure(figsize=(15, 8))
+    
+    # Convert data to pandas DataFrame
+    import pandas as pd
+    all_data = pd.DataFrame([
+        {'method': result.config_name, 'distance': d}
+        for result in results
+        for d in result.distances
+    ])
+
+    # Distribution plot
+    plt.subplot(2, 2, 1)
+    sns.histplot(
+        data=all_data,
+        x='distance',
+        hue='method',
+        stat='density',
+        common_norm=True,
+        alpha=0.6
+    )
+    plt.title('Distance Distribution Comparison')
+    plt.xlabel('Cosine Distance')
+    plt.ylabel('Density')
+    
+    # Box plot with pandas DataFrame
+    plt.subplot(2, 2, 2)
+    sns.boxplot(
+        data=all_data,
+        x='method',
+        y='distance'
+    )
+    plt.title('Distance Distribution Statistics')
+    plt.ylabel('Cosine Distance')
+    plt.xticks(rotation=45)
+    
+    # Statistics summary
+    plt.subplot(2, 2, (3, 4))
+    stats_text = "Distance Statistics:\n\n"
+    for result in results:
+        stats_text += f"{result.config_name}:\n"
+        stats_text += f"  Mean: {result.stats['mean']:.3f} ± {result.stats['std']:.3f}\n"
+        stats_text += f"  Median: {result.stats['median']:.3f}\n"
+        stats_text += f"  Range: [{result.stats['min']:.3f}, {result.stats['max']:.3f}]\n\n"
+    
+    plt.text(0.1, 0.9, stats_text, 
+             transform=plt.gca().transAxes,
+             verticalalignment='top',
+             fontfamily='monospace')
+    plt.axis('off')
+
+    print(stats_text)
+    
+    plt.tight_layout()
+    plt.savefig('embedding_comparison.png')
+    plt.close()
+
+async def main():
+    # Define different configurations to compare
+    configs = [
+        ("Ollama Embedding", AppConfig(_env_file=Path('.env.evaluation.ollama'))),
+        ("OpenAI Embedding", AppConfig(_env_file=Path('.env.evaluation.openai'))),
+        # Add more configurations as needed
+    ]
+    
+    # Analyze distributions for all configs
+    results = await analyze_embedding_distributions(
+        TRANSFORMER_TEST_CASES,
+        configs,
+        cache_path=Path("evaluations", "embedding_cache")
+    )
+    
+    # Plot distributions
+    plot_distance_distributions(results)
+    
+    return results
+
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main())