hugegraph-llm/src/hugegraph_llm/api/models/rag_requests.py

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from typing import Optional, Literal

from fastapi import Query
from pydantic import BaseModel

from hugegraph_llm.config import prompt


class GraphConfigRequest(BaseModel):
    ip: str = Query('127.0.0.1', description="hugegraph client ip.")
    port: str = Query('8080', description="hugegraph client port.")
    name: str = Query('hugegraph', description="hugegraph client name.")
    user: str = Query('', description="hugegraph client user.")
    pwd: str = Query('', description="hugegraph client pwd.")
    gs: str = None


class RAGRequest(BaseModel):
    query: str = Query(..., description="Query you want to ask")
    raw_answer: bool = Query(False, description="Use LLM to generate answer directly")
    vector_only: bool = Query(False, description="Use LLM to generate answer with vector")
    graph_only: bool = Query(True, description="Use LLM to generate answer with graph RAG only")
    graph_vector_answer: bool = Query(False, description="Use LLM to generate answer with vector & GraphRAG")
    graph_ratio: float = Query(0.5, description="The ratio of GraphRAG ans & vector ans")
    rerank_method: Literal["bleu", "reranker"] = Query("bleu", description="Method to rerank the results.")
    near_neighbor_first: bool = Query(False, description="Prioritize near neighbors in the search results.")
    custom_priority_info: str = Query("", description="Custom information to prioritize certain results.")
    # Graph Configs
    max_graph_items: int = Query(30, description="Maximum number of items for GQL queries in graph.")
    topk_return_results: int = Query(20, description="Number of sorted results to return finally.")
    vector_dis_threshold: float = Query(0.9, description="Threshold for vector similarity\
                                         (results greater than this will be ignored).")
    topk_per_keyword : int = Query(1, description="TopK results returned for each keyword \
                                   extracted from the query, by default only the most similar one is returned.")
    client_config: Optional[GraphConfigRequest] = Query(None, description="hugegraph server config.")

    # Keep prompt params in the end
    answer_prompt: Optional[str] = Query(prompt.answer_prompt, description="Prompt to guide the answer generation.")
    keywords_extract_prompt: Optional[str] = Query(
        prompt.keywords_extract_prompt,
        description="Prompt for extracting keywords from query.",
    )
    gremlin_tmpl_num: int = Query(1, description="Number of Gremlin templates to use.")
    gremlin_prompt: Optional[str] = Query(
        prompt.gremlin_generate_prompt,
        description="Prompt for the Text2Gremlin query.",
    )


# TODO: import the default value of prompt.* dynamically
class GraphRAGRequest(BaseModel):
    query: str = Query(..., description="Query you want to ask")
    # Graph Configs
    max_graph_items: int = Query(30, description="Maximum number of items for GQL queries in graph.")
    topk_return_results: int = Query(20, description="Number of sorted results to return finally.")
    vector_dis_threshold: float = Query(0.9, description="Threshold for vector similarity \
                                        (results greater than this will be ignored).")
    topk_per_keyword : int = Query(1, description="TopK results returned for each keyword extracted\
                                    from the query, by default only the most similar one is returned.")

    client_config : Optional[GraphConfigRequest] = Query(None, description="hugegraph server config.")
    get_vertex_only: bool = Query(False, description="return only keywords & vertex (early stop).")

    gremlin_tmpl_num: int = Query(
        1, description="Number of Gremlin templates to use. If num <=0 means template is not provided"
    )
    rerank_method: Literal["bleu", "reranker"] = Query("bleu", description="Method to rerank the results.")
    near_neighbor_first: bool = Query(False, description="Prioritize near neighbors in the search results.")
    custom_priority_info: str = Query("", description="Custom information to prioritize certain results.")
    gremlin_prompt: Optional[str] = Query(
        prompt.gremlin_generate_prompt,
        description="Prompt for the Text2Gremlin query.",
    )


class LLMConfigRequest(BaseModel):
    llm_type: str
    # The common parameters shared by OpenAI, Qianfan Wenxin,
    # and OLLAMA platforms.
    api_key: str
    api_base: str
    language_model: str
    # Openai-only properties
    max_tokens: str = None
    # qianfan-wenxin-only properties
    secret_key: str = None
    # ollama-only properties
    host: str = None
    port: str = None


class RerankerConfigRequest(BaseModel):
    reranker_model: str
    reranker_type: str
    api_key: str
    cohere_base_url: Optional[str] = None


class LogStreamRequest(BaseModel):
    admin_token: Optional[str] = None
    log_file: Optional[str] = "llm-server.log"