haylanandClaude-Bot
7247674a9f
The server's Docker/BuildKit couldn't do a git-context build of a public github.com repo (fails with "could not read Username ... terminal prompts disabled" — an auth-shaped error for what should be an anonymous clone). Rather than debug that, vendor litellm-pgvector's small source tree directly (vendor/litellm-pgvector/, see VENDORED.md for provenance/update steps) and build from the local path. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
86 lines
2.0 KiB
Python
86 lines
2.0 KiB
Python
from typing import Optional, Dict, Any, List
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from pydantic import BaseModel
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from datetime import datetime
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class VectorStoreCreateRequest(BaseModel):
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name: str
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file_ids: Optional[List[str]] = None
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expires_after: Optional[Dict[str, Any]] = None
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chunking_strategy: Optional[Dict[str, Any]] = None
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metadata: Optional[Dict[str, Any]] = None
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class VectorStoreResponse(BaseModel):
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id: str
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object: str = "vector_store"
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created_at: int
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name: str
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usage_bytes: int
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file_counts: Dict[str, int]
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status: str
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expires_after: Optional[Dict[str, Any]] = None
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expires_at: Optional[int] = None
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last_active_at: Optional[int] = None
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metadata: Optional[Dict[str, Any]] = None
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class VectorStoreSearchRequest(BaseModel):
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query: str
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limit: Optional[int] = 20
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filters: Optional[Dict[str, Any]] = None
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return_metadata: Optional[bool] = True
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class ContentChunk(BaseModel):
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type: str = "text"
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text: str
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class SearchResult(BaseModel):
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file_id: str
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filename: str
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score: float
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attributes: Optional[Dict[str, Any]] = None
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content: List[ContentChunk]
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class VectorStoreSearchResponse(BaseModel):
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object: str = "vector_store.search_results.page"
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search_query: str
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data: List[SearchResult]
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has_more: bool = False
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next_page: Optional[str] = None
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class EmbeddingCreateRequest(BaseModel):
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content: str
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embedding: List[float]
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metadata: Optional[Dict[str, Any]] = None
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class EmbeddingResponse(BaseModel):
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id: str
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object: str = "embedding"
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vector_store_id: str
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content: str
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metadata: Optional[Dict[str, Any]] = None
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created_at: int
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class EmbeddingBatchCreateRequest(BaseModel):
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embeddings: List[EmbeddingCreateRequest]
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class EmbeddingBatchCreateResponse(BaseModel):
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object: str = "embedding.batch"
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data: List[EmbeddingResponse]
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created: int
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class VectorStoreListResponse(BaseModel):
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object: str = "list"
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data: List[VectorStoreResponse]
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first_id: Optional[str] = None
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last_id: Optional[str] = None
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has_more: bool = False |