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multiple: langchain 0.2 in master (#21191)
0.2rc migrations - [x] Move memory - [x] Move remaining retrievers - [x] graph_qa chains - [x] some dependency from evaluation code potentially on math utils - [x] Move openapi chain from `langchain.chains.api.openapi` to `langchain_community.chains.openapi` - [x] Migrate `langchain.chains.ernie_functions` to `langchain_community.chains.ernie_functions` - [x] migrate `langchain/chains/llm_requests.py` to `langchain_community.chains.llm_requests` - [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder` -> `langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder` (namespace not ideal, but it needs to be moved to `langchain` to avoid circular deps) - [x] unit tests langchain -- add pytest.mark.community to some unit tests that will stay in langchain - [x] unit tests community -- move unit tests that depend on community to community - [x] mv integration tests that depend on community to community - [x] mypy checks Other todo - [x] Make deprecation warnings not noisy (need to use warn deprecated and check that things are implemented properly) - [x] Update deprecation messages with timeline for code removal (likely we actually won't be removing things until 0.4 release) -- will give people more time to transition their code. - [ ] Add information to deprecation warning to show users how to migrate their code base using langchain-cli - [ ] Remove any unnecessary requirements in langchain (e.g., is SQLALchemy required?) --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Eugene Yurtsev
GitHub
221
libs/community/langchain_community/chains/graph_qa/gremlin.py
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221
libs/community/langchain_community/chains/graph_qa/gremlin.py
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"""Question answering over a graph."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.prompts.prompt import PromptTemplate
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from langchain_core.pydantic_v1 import Field
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from langchain_community.chains.graph_qa.prompts import (
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CYPHER_QA_PROMPT,
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GRAPHDB_SPARQL_FIX_TEMPLATE,
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GREMLIN_GENERATION_PROMPT,
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)
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from langchain_community.graphs import GremlinGraph
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INTERMEDIATE_STEPS_KEY = "intermediate_steps"
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def extract_gremlin(text: str) -> str:
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"""Extract Gremlin code from a text.
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Args:
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text: Text to extract Gremlin code from.
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Returns:
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Gremlin code extracted from the text.
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"""
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text = text.replace("`", "")
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if text.startswith("gremlin"):
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text = text[len("gremlin") :]
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return text.replace("\n", "")
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class GremlinQAChain(Chain):
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"""Chain for question-answering against a graph by generating gremlin statements.
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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graph: GremlinGraph = Field(exclude=True)
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gremlin_generation_chain: LLMChain
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qa_chain: LLMChain
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gremlin_fix_chain: LLMChain
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max_fix_retries: int = 3
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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top_k: int = 100
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return_direct: bool = False
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return_intermediate_steps: bool = False
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@property
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def input_keys(self) -> List[str]:
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"""Input keys.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Output keys.
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:meta private:
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"""
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_output_keys = [self.output_key]
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return _output_keys
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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*,
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gremlin_fix_prompt: BasePromptTemplate = PromptTemplate(
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input_variables=["error_message", "generated_sparql", "schema"],
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template=GRAPHDB_SPARQL_FIX_TEMPLATE.replace("SPARQL", "Gremlin").replace(
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"in Turtle format", ""
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),
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),
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qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
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gremlin_prompt: BasePromptTemplate = GREMLIN_GENERATION_PROMPT,
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**kwargs: Any,
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) -> GremlinQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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gremlin_generation_chain = LLMChain(llm=llm, prompt=gremlin_prompt)
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gremlinl_fix_chain = LLMChain(llm=llm, prompt=gremlin_fix_prompt)
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return cls(
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qa_chain=qa_chain,
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gremlin_generation_chain=gremlin_generation_chain,
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gremlin_fix_chain=gremlinl_fix_chain,
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**kwargs,
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)
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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"""Generate gremlin statement, use it to look up in db and answer question."""
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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callbacks = _run_manager.get_child()
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question = inputs[self.input_key]
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intermediate_steps: List = []
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chain_response = self.gremlin_generation_chain.invoke(
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{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
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)
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generated_gremlin = extract_gremlin(
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chain_response[self.gremlin_generation_chain.output_key]
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)
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_run_manager.on_text("Generated gremlin:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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generated_gremlin, color="green", end="\n", verbose=self.verbose
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)
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intermediate_steps.append({"query": generated_gremlin})
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if generated_gremlin:
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context = self.execute_with_retry(
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_run_manager, callbacks, generated_gremlin
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)[: self.top_k]
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else:
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context = []
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if self.return_direct:
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final_result = context
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else:
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_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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str(context), color="green", end="\n", verbose=self.verbose
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)
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intermediate_steps.append({"context": context})
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result = self.qa_chain.invoke(
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{"question": question, "context": context},
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callbacks=callbacks,
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)
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final_result = result[self.qa_chain.output_key]
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chain_result: Dict[str, Any] = {self.output_key: final_result}
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if self.return_intermediate_steps:
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chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
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return chain_result
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def execute_query(self, query: str) -> List[Any]:
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try:
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return self.graph.query(query)
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except Exception as e:
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if hasattr(e, "status_message"):
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raise ValueError(e.status_message)
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else:
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raise ValueError(str(e))
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def execute_with_retry(
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self,
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_run_manager: CallbackManagerForChainRun,
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callbacks: CallbackManager,
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generated_gremlin: str,
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) -> List[Any]:
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try:
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return self.execute_query(generated_gremlin)
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except Exception as e:
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retries = 0
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error_message = str(e)
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self.log_invalid_query(_run_manager, generated_gremlin, error_message)
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while retries < self.max_fix_retries:
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try:
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fix_chain_result = self.gremlin_fix_chain.invoke(
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{
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"error_message": error_message,
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# we are borrowing template from sparql
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"generated_sparql": generated_gremlin,
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"schema": self.schema,
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},
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callbacks=callbacks,
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)
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fixed_gremlin = fix_chain_result[self.gremlin_fix_chain.output_key]
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return self.execute_query(fixed_gremlin)
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except Exception as e:
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retries += 1
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parse_exception = str(e)
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self.log_invalid_query(_run_manager, fixed_gremlin, parse_exception)
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raise ValueError("The generated Gremlin query is invalid.")
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def log_invalid_query(
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self,
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_run_manager: CallbackManagerForChainRun,
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generated_query: str,
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error_message: str,
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) -> None:
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_run_manager.on_text("Invalid Gremlin query: ", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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generated_query, color="red", end="\n", verbose=self.verbose
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)
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_run_manager.on_text(
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"Gremlin Query Parse Error: ", end="\n", verbose=self.verbose
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)
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_run_manager.on_text(
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error_message, color="red", end="\n\n", verbose=self.verbose
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)
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