graphrag-microsoft/graphrag/query/structured_search/local_search/search.py

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License

"""LocalSearch implementation."""

import logging
import time
from collections.abc import AsyncGenerator
from typing import Any

import tiktoken

from graphrag.prompts.query.local_search_system_prompt import (
    LOCAL_SEARCH_SYSTEM_PROMPT,
)
from graphrag.query.context_builder.builders import LocalContextBuilder
from graphrag.query.context_builder.conversation_history import (
    ConversationHistory,
)
from graphrag.query.llm.base import BaseLLM, BaseLLMCallback
from graphrag.query.llm.text_utils import num_tokens
from graphrag.query.structured_search.base import BaseSearch, SearchResult

DEFAULT_LLM_PARAMS = {
    "max_tokens": 1500,
    "temperature": 0.0,
}

log = logging.getLogger(__name__)


class LocalSearch(BaseSearch[LocalContextBuilder]):
    """Search orchestration for local search mode."""

    def __init__(
        self,
        llm: BaseLLM,
        context_builder: LocalContextBuilder,
        token_encoder: tiktoken.Encoding | None = None,
        system_prompt: str | None = None,
        response_type: str = "multiple paragraphs",
        callbacks: list[BaseLLMCallback] | None = None,
        llm_params: dict[str, Any] = DEFAULT_LLM_PARAMS,
        context_builder_params: dict | None = None,
    ):
        super().__init__(
            llm=llm,
            context_builder=context_builder,
            token_encoder=token_encoder,
            llm_params=llm_params,
            context_builder_params=context_builder_params or {},
        )
        self.system_prompt = system_prompt or LOCAL_SEARCH_SYSTEM_PROMPT
        self.callbacks = callbacks
        self.response_type = response_type

    async def search(
        self,
        query: str,
        conversation_history: ConversationHistory | None = None,
        **kwargs,
    ) -> SearchResult:
        """Build local search context that fits a single context window and generate answer for the user query."""
        start_time = time.time()
        search_prompt = ""
        llm_calls, prompt_tokens, output_tokens = {}, {}, {}
        context_result = self.context_builder.build_context(
            query=query,
            conversation_history=conversation_history,
            **kwargs,
            **self.context_builder_params,
        )
        llm_calls["build_context"] = context_result.llm_calls
        prompt_tokens["build_context"] = context_result.prompt_tokens
        output_tokens["build_context"] = context_result.output_tokens

        log.info("GENERATE ANSWER: %s. QUERY: %s", start_time, query)
        try:
            if "drift_query" in kwargs:
                drift_query = kwargs["drift_query"]
                search_prompt = self.system_prompt.format(
                    context_data=context_result.context_chunks,
                    response_type=self.response_type,
                    global_query=drift_query,
                )
            else:
                search_prompt = self.system_prompt.format(
                    context_data=context_result.context_chunks,
                    response_type=self.response_type,
                )
            search_messages = [
                {"role": "system", "content": search_prompt},
                {"role": "user", "content": query},
            ]

            response = await self.llm.agenerate(
                messages=search_messages,
                streaming=True,
                callbacks=self.callbacks,
                **self.llm_params,
            )
            llm_calls["response"] = 1
            prompt_tokens["response"] = num_tokens(search_prompt, self.token_encoder)
            output_tokens["response"] = num_tokens(response, self.token_encoder)

            return SearchResult(
                response=response,
                context_data=context_result.context_records,
                context_text=context_result.context_chunks,
                completion_time=time.time() - start_time,
                llm_calls=sum(llm_calls.values()),
                prompt_tokens=sum(prompt_tokens.values()),
                output_tokens=sum(output_tokens.values()),
                llm_calls_categories=llm_calls,
                prompt_tokens_categories=prompt_tokens,
                output_tokens_categories=output_tokens,
            )

        except Exception:
            log.exception("Exception in _asearch")
            return SearchResult(
                response="",
                context_data=context_result.context_records,
                context_text=context_result.context_chunks,
                completion_time=time.time() - start_time,
                llm_calls=1,
                prompt_tokens=num_tokens(search_prompt, self.token_encoder),
                output_tokens=0,
            )

    async def stream_search(
        self,
        query: str,
        conversation_history: ConversationHistory | None = None,
    ) -> AsyncGenerator:
        """Build local search context that fits a single context window and generate answer for the user query."""
        start_time = time.time()

        context_result = self.context_builder.build_context(
            query=query,
            conversation_history=conversation_history,
            **self.context_builder_params,
        )
        log.info("GENERATE ANSWER: %s. QUERY: %s", start_time, query)
        search_prompt = self.system_prompt.format(
            context_data=context_result.context_chunks, response_type=self.response_type
        )
        search_messages = [
            {"role": "system", "content": search_prompt},
            {"role": "user", "content": query},
        ]

        # send context records first before sending the reduce response
        yield context_result.context_records
        async for response in self.llm.astream_generate(  # type: ignore
            messages=search_messages,
            callbacks=self.callbacks,
            **self.llm_params,
        ):
            yield response