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Cache the result from llm for graphrag and raptor (#4051)
### What problem does this PR solve? #4045 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
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@ -12,6 +12,8 @@ import traceback
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from typing import Any, Callable, Mapping
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from dataclasses import dataclass
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import tiktoken
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from graphrag.extractor import Extractor
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from graphrag.graph_prompt import GRAPH_EXTRACTION_PROMPT, CONTINUE_PROMPT, LOOP_PROMPT
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from graphrag.utils import ErrorHandlerFn, perform_variable_replacements, clean_str
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from rag.llm.chat_model import Base as CompletionLLM
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@ -34,10 +36,9 @@ class GraphExtractionResult:
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source_docs: dict[Any, Any]
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class GraphExtractor:
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class GraphExtractor(Extractor):
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"""Unipartite graph extractor class definition."""
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_llm: CompletionLLM
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_join_descriptions: bool
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_tuple_delimiter_key: str
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_record_delimiter_key: str
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@ -165,9 +166,7 @@ class GraphExtractor:
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token_count = 0
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text = perform_variable_replacements(self._extraction_prompt, variables=variables)
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gen_conf = {"temperature": 0.3}
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response = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
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if response.find("**ERROR**") >= 0:
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raise Exception(response)
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response = self._chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
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token_count = num_tokens_from_string(text + response)
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results = response or ""
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@ -177,9 +176,7 @@ class GraphExtractor:
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for i in range(self._max_gleanings):
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text = perform_variable_replacements(CONTINUE_PROMPT, history=history, variables=variables)
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history.append({"role": "user", "content": text})
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response = self._llm.chat("", history, gen_conf)
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if response.find("**ERROR**") >=0:
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raise Exception(response)
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response = self._chat("", history, gen_conf)
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results += response or ""
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# if this is the final glean, don't bother updating the continuation flag
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@ -187,7 +184,7 @@ class GraphExtractor:
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break
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history.append({"role": "assistant", "content": response})
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history.append({"role": "user", "content": LOOP_PROMPT})
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continuation = self._llm.chat("", history, self._loop_args)
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continuation = self._chat("", history, self._loop_args)
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if continuation != "YES":
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break
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