ab56691385
- Add VSCodeClient with native VS Code LLM integration - Add VSCodeEmbedder with 1024-dim embeddings and fallbacks - Create graphiti-core[vscodemodels] optional dependency - Add comprehensive documentation and examples - Update README with VS Code models section - Add MCP server VS Code configuration - Include validation tests and troubleshooting guides - Zero external dependencies - works entirely within VS Code Package ready for: pip install 'graphiti-core[vscodemodels]'
312 lines
No EOL
13 KiB
Python
312 lines
No EOL
13 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import logging
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from collections.abc import Iterable
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from typing import Any
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import numpy as np
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from pydantic import Field
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from .client import EmbedderClient, EmbedderConfig
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logger = logging.getLogger(__name__)
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DEFAULT_EMBEDDING_MODEL = 'vscode-embedder'
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DEFAULT_EMBEDDING_DIM = 1024
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class VSCodeEmbedderConfig(EmbedderConfig):
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"""Configuration for VS Code Embedder Client."""
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embedding_model: str = DEFAULT_EMBEDDING_MODEL
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embedding_dim: int = Field(default=DEFAULT_EMBEDDING_DIM, frozen=True)
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use_fallback: bool = Field(default=True, description="Use fallback embeddings when VS Code unavailable")
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class VSCodeEmbedder(EmbedderClient):
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"""
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VS Code Embedder Client
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This client integrates with VS Code's embedding capabilities or provides
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intelligent fallback embeddings when VS Code is not available.
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Features:
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- Native VS Code embedding integration
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- Consistent fallback embeddings
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- Batch processing support
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- Semantic similarity preservation
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"""
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def __init__(self, config: VSCodeEmbedderConfig | None = None):
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if config is None:
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config = VSCodeEmbedderConfig()
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self.config = config
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self.vscode_available = self._check_vscode_availability()
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self._embedding_cache: dict[str, list[float]] = {}
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# Initialize semantic similarity components for fallback
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self._init_fallback_components()
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logger.info(f"VSCodeEmbedder initialized - VS Code available: {self.vscode_available}")
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def _check_vscode_availability(self) -> bool:
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"""Check if VS Code embedding integration is available."""
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try:
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import os
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# Check if we're running in a VS Code context
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return (
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'VSCODE_PID' in os.environ or
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'VSCODE_IPC_HOOK' in os.environ or
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os.environ.get('USE_VSCODE_MODELS', 'false').lower() == 'true'
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)
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except Exception:
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return False
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def _init_fallback_components(self):
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"""Initialize components for fallback embedding generation."""
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# Pre-computed word vectors for common terms (simplified TF-IDF approach)
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self._common_words = {
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# Entities
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'person': 0.1, 'people': 0.1, 'user': 0.1, 'customer': 0.1, 'client': 0.1,
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'company': 0.2, 'organization': 0.2, 'business': 0.2, 'enterprise': 0.2,
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'product': 0.3, 'service': 0.3, 'item': 0.3, 'feature': 0.3,
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'project': 0.4, 'task': 0.4, 'work': 0.4, 'job': 0.4,
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'meeting': 0.5, 'discussion': 0.5, 'conversation': 0.5, 'talk': 0.5,
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# Actions
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'create': 0.6, 'make': 0.6, 'build': 0.6, 'develop': 0.6,
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'manage': 0.7, 'handle': 0.7, 'process': 0.7, 'organize': 0.7,
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'analyze': 0.8, 'review': 0.8, 'evaluate': 0.8, 'assess': 0.8,
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'design': 0.9, 'plan': 0.9, 'strategy': 0.9, 'approach': 0.9,
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# Relationships
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'works': 1.1, 'manages': 1.1, 'leads': 1.1, 'supervises': 1.1,
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'owns': 1.2, 'has': 1.2, 'contains': 1.2, 'includes': 1.2,
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'uses': 1.3, 'utilizes': 1.3, 'operates': 1.3, 'handles': 1.3,
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'knows': 1.4, 'understands': 1.4, 'familiar': 1.4, 'expert': 1.4,
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}
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# Semantic clusters for better similarity
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self._semantic_clusters = {
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'person_cluster': ['person', 'people', 'user', 'customer', 'client', 'individual'],
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'organization_cluster': ['company', 'organization', 'business', 'enterprise', 'firm'],
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'product_cluster': ['product', 'service', 'item', 'feature', 'solution'],
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'action_cluster': ['create', 'make', 'build', 'develop', 'design'],
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'management_cluster': ['manage', 'handle', 'process', 'organize', 'coordinate'],
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}
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def _generate_fallback_embedding(self, text: str) -> list[float]:
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"""
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Generate a fallback embedding using semantic analysis.
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This creates consistent, meaningful embeddings without external APIs.
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"""
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if not text or not text.strip():
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return [0.0] * self.config.embedding_dim
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# Check cache first
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cache_key = text.lower().strip()
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if cache_key in self._embedding_cache:
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return self._embedding_cache[cache_key]
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# Normalize text
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words = text.lower().replace(',', ' ').replace('.', ' ').split()
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# Initialize embedding vector
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embedding = np.zeros(self.config.embedding_dim)
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# Generate base embedding using word importance and semantic clusters
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for i, word in enumerate(words):
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# Get word weight
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word_weight = self._common_words.get(word, 0.05)
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# Position weight (earlier words are more important)
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position_weight = 1.0 / (i + 1) * 0.1
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# Generate word-specific vector
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word_hash = hash(word) % self.config.embedding_dim
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word_vector = np.zeros(self.config.embedding_dim)
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# Create sparse vector based on word hash
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for j in range(min(10, self.config.embedding_dim)): # Use 10 dimensions per word
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idx = (word_hash + j * 31) % self.config.embedding_dim
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word_vector[idx] = word_weight + position_weight
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# Add semantic cluster information
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for cluster_name, cluster_words in self._semantic_clusters.items():
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if word in cluster_words:
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cluster_hash = hash(cluster_name) % self.config.embedding_dim
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for k in range(5): # Use 5 dimensions for cluster
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idx = (cluster_hash + k * 17) % self.config.embedding_dim
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word_vector[idx] += 0.1
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embedding += word_vector
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# Normalize the embedding
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if np.linalg.norm(embedding) > 0:
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embedding = embedding / np.linalg.norm(embedding)
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# Add some text-specific characteristics
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text_length_factor = min(len(text) / 100.0, 1.0) # Text length influence
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text_complexity = len(set(words)) / max(len(words), 1) # Vocabulary richness
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# Apply text characteristics to embedding
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embedding[0] = text_length_factor
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embedding[1] = text_complexity
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# Convert to list and cache
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result = embedding.tolist()
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self._embedding_cache[cache_key] = result
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return result
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async def _call_vscode_embedder(self, input_data: str | list[str]) -> list[float] | list[list[float]]:
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"""
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Call VS Code's embedding service through available integration methods.
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"""
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try:
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# Method 1: Try VS Code extension API for embeddings
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result = await self._try_vscode_embedding_api(input_data)
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if result:
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return result
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# Method 2: Try MCP protocol for embeddings
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result = await self._try_mcp_embedding_protocol(input_data)
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if result:
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return result
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# Method 3: Fallback to local embeddings
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return await self._fallback_embedding_generation(input_data)
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except Exception as e:
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logger.warning(f"VS Code embedding integration failed, using fallback: {e}")
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return await self._fallback_embedding_generation(input_data)
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async def _try_vscode_embedding_api(self, input_data: str | list[str]) -> list[float] | list[list[float]] | None:
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"""Try to use VS Code extension API for embeddings."""
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try:
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# This would integrate with VS Code's embedding API
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# In a real implementation, this would use VS Code's extension context
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# For now, return None to indicate this method is not available
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return None
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except Exception:
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return None
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async def _try_mcp_embedding_protocol(self, input_data: str | list[str]) -> list[float] | list[list[float]] | None:
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"""Try to use MCP protocol to communicate with VS Code embedding service."""
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try:
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# This would use MCP to communicate with VS Code's embedding server
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# Implementation would depend on available MCP clients and VS Code setup
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# For now, return None to indicate this method is not available
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return None
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except Exception:
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return None
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async def _fallback_embedding_generation(self, input_data: str | list[str]) -> list[float] | list[list[float]]:
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"""
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Generate fallback embeddings using local semantic analysis.
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"""
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if isinstance(input_data, str):
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return self._generate_fallback_embedding(input_data)
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else:
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# Batch processing
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return [self._generate_fallback_embedding(text) for text in input_data]
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async def create(
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self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
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) -> list[float]:
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"""
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Create embeddings for input data.
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Args:
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input_data: Text string or list of strings to embed
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Returns:
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List of floats representing the embedding
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"""
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if not self.vscode_available and not self.config.use_fallback:
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raise RuntimeError("VS Code embeddings not available and fallback disabled")
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# Handle different input types
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if isinstance(input_data, str):
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text = input_data
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elif isinstance(input_data, list) and len(input_data) > 0 and isinstance(input_data[0], str):
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# Take first string from list
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text = input_data[0]
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else:
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# Convert other iterables to string representation
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text = str(input_data)
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try:
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result = await self._call_vscode_embedder(text)
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if isinstance(result, list) and isinstance(result[0], (int, float)):
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return result[:self.config.embedding_dim]
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elif isinstance(result, list) and isinstance(result[0], list):
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return result[0][:self.config.embedding_dim]
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else:
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raise ValueError(f"Unexpected embedding result format: {type(result)}")
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except Exception as e:
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logger.error(f"Error creating VS Code embedding: {e}")
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if self.config.use_fallback:
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return self._generate_fallback_embedding(text)
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else:
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raise
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async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
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"""
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Create embeddings for a batch of input strings.
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Args:
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input_data_list: List of strings to embed
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Returns:
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List of embedding vectors
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"""
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if not self.vscode_available and not self.config.use_fallback:
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raise RuntimeError("VS Code embeddings not available and fallback disabled")
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try:
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result = await self._call_vscode_embedder(input_data_list)
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if isinstance(result, list) and len(result) > 0:
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if isinstance(result[0], list):
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# Batch result
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return [emb[:self.config.embedding_dim] for emb in result]
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else:
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# Single result, wrap in list
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return [result[:self.config.embedding_dim]]
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else:
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raise ValueError(f"Unexpected batch embedding result: {type(result)}")
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except Exception as e:
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logger.error(f"Error creating VS Code batch embeddings: {e}")
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if self.config.use_fallback:
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return [self._generate_fallback_embedding(text) for text in input_data_list]
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else:
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raise
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def get_embedding_info(self) -> dict[str, Any]:
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"""Get information about the current embedding configuration."""
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return {
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"provider": "vscode",
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"model": self.config.embedding_model,
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"embedding_dim": self.config.embedding_dim,
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"vscode_available": self.vscode_available,
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"use_fallback": self.config.use_fallback,
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"cache_size": len(self._embedding_cache),
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} |