Merge branch 'main' into feat/claude-code-refine
This commit is contained in:
Andy Lee
@@ -6,6 +6,7 @@ Preserves all optimization parameters to ensure performance
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import logging
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import os
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Any
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import numpy as np
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@@ -377,7 +378,7 @@ def compute_embeddings_ollama(
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Args:
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texts: List of texts to compute embeddings for
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model_name: Ollama model name (e.g., "nomic-embed-text-v2", "mxbai-embed-large")
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model_name: Ollama model name (e.g., "nomic-embed-text", "mxbai-embed-large")
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is_build: Whether this is a build operation (shows progress bar)
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host: Ollama host URL (default: http://localhost:11434)
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@@ -402,57 +403,106 @@ def compute_embeddings_ollama(
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try:
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response = requests.get(f"{host}/api/version", timeout=5)
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response.raise_for_status()
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except Exception as e:
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raise RuntimeError(
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f"Could not connect to Ollama at {host}. Please ensure Ollama is running. Error: {e}"
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except requests.exceptions.ConnectionError:
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error_msg = (
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f"❌ Could not connect to Ollama at {host}.\n\n"
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"Please ensure Ollama is running:\n"
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" • macOS/Linux: ollama serve\n"
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" • Windows: Make sure Ollama is running in the system tray\n\n"
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"Installation: https://ollama.com/download"
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)
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raise RuntimeError(error_msg)
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except Exception as e:
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raise RuntimeError(f"Unexpected error connecting to Ollama: {e}")
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# Check if model exists
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# Check if model exists and provide helpful suggestions
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try:
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response = requests.get(f"{host}/api/tags", timeout=5)
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response.raise_for_status()
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models = response.json()
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model_names = [model["name"] for model in models.get("models", [])]
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# Check if model exists (handle versioned names like nomic-embed-text-v2:latest)
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# Filter for embedding models (models that support embeddings)
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embedding_models = []
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suggested_embedding_models = [
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"nomic-embed-text",
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"mxbai-embed-large",
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"bge-m3",
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"all-minilm",
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"snowflake-arctic-embed",
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]
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for model in model_names:
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# Check if it's an embedding model (by name patterns or known models)
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base_name = model.split(":")[0]
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if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5"]):
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embedding_models.append(model)
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# Check if model exists (handle versioned names)
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model_found = any(
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model_name in name or name.startswith(f"{model_name}:") for name in model_names
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model_name == name.split(":")[0] or model_name == name for name in model_names
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)
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if not model_found:
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error_msg = f"Model '{model_name}' not found in Ollama. Available models: {', '.join(model_names)}"
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error_msg += f"\n\nTo install the model, run:\n ollama pull {model_name}"
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error_msg = f"❌ Model '{model_name}' not found in local Ollama.\n\n"
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# Suggest pulling the model
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error_msg += "📦 To install this embedding model:\n"
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error_msg += f" ollama pull {model_name}\n\n"
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# Show available embedding models
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if embedding_models:
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error_msg += "✅ Available embedding models:\n"
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for model in embedding_models[:5]:
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error_msg += f" • {model}\n"
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if len(embedding_models) > 5:
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error_msg += f" ... and {len(embedding_models) - 5} more\n"
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else:
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error_msg += "💡 Popular embedding models to install:\n"
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for model in suggested_embedding_models[:3]:
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error_msg += f" • ollama pull {model}\n"
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error_msg += "\n📚 Browse more: https://ollama.com/library"
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raise ValueError(error_msg)
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# Verify the model supports embeddings by testing it
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try:
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test_response = requests.post(
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f"{host}/api/embeddings", json={"model": model_name, "prompt": "test"}, timeout=10
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)
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if test_response.status_code != 200:
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error_msg = (
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f"⚠️ Model '{model_name}' exists but may not support embeddings.\n\n"
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f"Please use an embedding model like:\n"
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)
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for model in suggested_embedding_models[:3]:
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error_msg += f" • {model}\n"
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raise ValueError(error_msg)
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except requests.exceptions.RequestException:
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# If test fails, continue anyway - model might still work
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pass
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except requests.exceptions.RequestException as e:
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logger.warning(f"Could not verify model existence: {e}")
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# Process embeddings
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all_embeddings = []
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batch_size = 100 # Process in batches to avoid overwhelming the API
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# Process embeddings with optimized concurrent processing
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import requests
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# Add progress bar if in build mode
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try:
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if is_build:
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from tqdm import tqdm
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def get_single_embedding(text_idx_tuple):
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"""Helper function to get embedding for a single text."""
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text, idx = text_idx_tuple
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max_retries = 3
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retry_count = 0
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batch_iterator = tqdm(
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range(0, len(texts), batch_size),
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desc="Computing Ollama embeddings",
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unit="batch",
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total=(len(texts) + batch_size - 1) // batch_size,
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)
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else:
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batch_iterator = range(0, len(texts), batch_size)
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except ImportError:
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batch_iterator = range(0, len(texts), batch_size)
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# Truncate very long texts to avoid API issues
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truncated_text = text[:8000] if len(text) > 8000 else text
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for i in batch_iterator:
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batch_texts = texts[i : i + batch_size]
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for text in batch_texts:
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while retry_count < max_retries:
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try:
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response = requests.post(
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f"{host}/api/embeddings", json={"model": model_name, "prompt": text}, timeout=30
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f"{host}/api/embeddings",
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json={"model": model_name, "prompt": truncated_text},
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timeout=30,
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)
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response.raise_for_status()
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@@ -460,16 +510,112 @@ def compute_embeddings_ollama(
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embedding = result.get("embedding")
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if embedding is None:
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raise ValueError(f"No embedding returned for text: {text[:50]}...")
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raise ValueError(f"No embedding returned for text {idx}")
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all_embeddings.append(embedding)
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return idx, embedding
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except requests.exceptions.Timeout:
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retry_count += 1
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if retry_count >= max_retries:
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logger.warning(f"Timeout for text {idx} after {max_retries} retries")
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return idx, None
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except requests.exceptions.RequestException as e:
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logger.error(f"Failed to get embedding for text: {e}")
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raise RuntimeError(f"Error getting embedding from Ollama: {e}")
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except Exception as e:
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logger.error(f"Unexpected error: {e}")
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raise
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if retry_count >= max_retries - 1:
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logger.error(f"Failed to get embedding for text {idx}: {e}")
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return idx, None
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retry_count += 1
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return idx, None
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# Determine if we should use concurrent processing
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use_concurrent = (
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len(texts) > 5 and not is_build
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) # Don't use concurrent in build mode to avoid overwhelming
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max_workers = min(4, len(texts)) # Limit concurrent requests to avoid overwhelming Ollama
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all_embeddings = [None] * len(texts) # Pre-allocate list to maintain order
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failed_indices = []
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if use_concurrent:
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logger.info(
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f"Using concurrent processing with {max_workers} workers for {len(texts)} texts"
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)
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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# Submit all tasks
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future_to_idx = {
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executor.submit(get_single_embedding, (text, idx)): idx
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for idx, text in enumerate(texts)
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}
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# Add progress bar for concurrent processing
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try:
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if is_build or len(texts) > 10:
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from tqdm import tqdm
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futures_iterator = tqdm(
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as_completed(future_to_idx),
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total=len(texts),
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desc="Computing Ollama embeddings",
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)
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else:
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futures_iterator = as_completed(future_to_idx)
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except ImportError:
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futures_iterator = as_completed(future_to_idx)
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# Collect results as they complete
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for future in futures_iterator:
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try:
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idx, embedding = future.result()
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if embedding is not None:
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all_embeddings[idx] = embedding
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else:
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failed_indices.append(idx)
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except Exception as e:
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idx = future_to_idx[future]
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logger.error(f"Exception for text {idx}: {e}")
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failed_indices.append(idx)
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else:
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# Sequential processing with progress bar
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show_progress = is_build or len(texts) > 10
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try:
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if show_progress:
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from tqdm import tqdm
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iterator = tqdm(
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enumerate(texts), total=len(texts), desc="Computing Ollama embeddings"
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)
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else:
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iterator = enumerate(texts)
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except ImportError:
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iterator = enumerate(texts)
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for idx, text in iterator:
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result_idx, embedding = get_single_embedding((text, idx))
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if embedding is not None:
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all_embeddings[idx] = embedding
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else:
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failed_indices.append(idx)
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# Handle failed embeddings
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if failed_indices:
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if len(failed_indices) == len(texts):
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raise RuntimeError("Failed to compute any embeddings")
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logger.warning(f"Failed to compute embeddings for {len(failed_indices)}/{len(texts)} texts")
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# Use zero embeddings as fallback for failed ones
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valid_embedding = next((e for e in all_embeddings if e is not None), None)
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if valid_embedding:
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embedding_dim = len(valid_embedding)
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for idx in failed_indices:
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all_embeddings[idx] = [0.0] * embedding_dim
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# Remove None values and convert to numpy array
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all_embeddings = [e for e in all_embeddings if e is not None]
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# Convert to numpy array and normalize
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embeddings = np.array(all_embeddings, dtype=np.float32)
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