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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
241
libs/community/langchain_community/chains/graph_qa/arangodb.py
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241
libs/community/langchain_community/chains/graph_qa/arangodb.py
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"""Question answering over a graph."""
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from __future__ import annotations
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import re
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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 import 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.pydantic_v1 import Field
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from langchain_community.chains.graph_qa.prompts import (
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AQL_FIX_PROMPT,
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AQL_GENERATION_PROMPT,
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AQL_QA_PROMPT,
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)
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from langchain_community.graphs.arangodb_graph import ArangoGraph
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class ArangoGraphQAChain(Chain):
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"""Chain for question-answering against a graph by generating AQL 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: ArangoGraph = Field(exclude=True)
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aql_generation_chain: LLMChain
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aql_fix_chain: LLMChain
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qa_chain: LLMChain
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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# Specifies the maximum number of AQL Query Results to return
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top_k: int = 10
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# Specifies the set of AQL Query Examples that promote few-shot-learning
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aql_examples: str = ""
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# Specify whether to return the AQL Query in the output dictionary
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return_aql_query: bool = False
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# Specify whether to return the AQL JSON Result in the output dictionary
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return_aql_result: bool = False
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# Specify the maximum amount of AQL Generation attempts that should be made
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max_aql_generation_attempts: int = 3
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@property
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def input_keys(self) -> List[str]:
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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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return [self.output_key]
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@property
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def _chain_type(self) -> str:
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return "graph_aql_chain"
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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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qa_prompt: BasePromptTemplate = AQL_QA_PROMPT,
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aql_generation_prompt: BasePromptTemplate = AQL_GENERATION_PROMPT,
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aql_fix_prompt: BasePromptTemplate = AQL_FIX_PROMPT,
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**kwargs: Any,
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) -> ArangoGraphQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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aql_generation_chain = LLMChain(llm=llm, prompt=aql_generation_prompt)
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aql_fix_chain = LLMChain(llm=llm, prompt=aql_fix_prompt)
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return cls(
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qa_chain=qa_chain,
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aql_generation_chain=aql_generation_chain,
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aql_fix_chain=aql_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, Any]:
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"""
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Generate an AQL statement from user input, use it retrieve a response
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from an ArangoDB Database instance, and respond to the user input
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in natural language.
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Users can modify the following ArangoGraphQAChain Class Variables:
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:var top_k: The maximum number of AQL Query Results to return
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:type top_k: int
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:var aql_examples: A set of AQL Query Examples that are passed to
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the AQL Generation Prompt Template to promote few-shot-learning.
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Defaults to an empty string.
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:type aql_examples: str
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:var return_aql_query: Whether to return the AQL Query in the
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output dictionary. Defaults to False.
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:type return_aql_query: bool
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:var return_aql_result: Whether to return the AQL Query in the
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output dictionary. Defaults to False
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:type return_aql_result: bool
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:var max_aql_generation_attempts: The maximum amount of AQL
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Generation attempts to be made prior to raising the last
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AQL Query Execution Error. Defaults to 3.
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:type max_aql_generation_attempts: int
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"""
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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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user_input = inputs[self.input_key]
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#########################
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# Generate AQL Query #
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aql_generation_output = self.aql_generation_chain.run(
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{
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"adb_schema": self.graph.schema,
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"aql_examples": self.aql_examples,
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"user_input": user_input,
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},
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callbacks=callbacks,
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)
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#########################
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aql_query = ""
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aql_error = ""
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aql_result = None
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aql_generation_attempt = 1
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while (
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aql_result is None
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and aql_generation_attempt < self.max_aql_generation_attempts + 1
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):
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#####################
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# Extract AQL Query #
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pattern = r"```(?i:aql)?(.*?)```"
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matches = re.findall(pattern, aql_generation_output, re.DOTALL)
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if not matches:
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_run_manager.on_text(
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"Invalid Response: ", end="\n", verbose=self.verbose
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)
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_run_manager.on_text(
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aql_generation_output, color="red", end="\n", verbose=self.verbose
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)
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raise ValueError(f"Response is Invalid: {aql_generation_output}")
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aql_query = matches[0]
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#####################
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_run_manager.on_text(
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f"AQL Query ({aql_generation_attempt}):", verbose=self.verbose
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)
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_run_manager.on_text(
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aql_query, color="green", end="\n", verbose=self.verbose
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)
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#####################
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# Execute AQL Query #
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from arango import AQLQueryExecuteError
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try:
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aql_result = self.graph.query(aql_query, self.top_k)
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except AQLQueryExecuteError as e:
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aql_error = e.error_message
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_run_manager.on_text(
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"AQL Query Execution Error: ", end="\n", verbose=self.verbose
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)
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_run_manager.on_text(
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aql_error, color="yellow", end="\n\n", verbose=self.verbose
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)
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########################
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# Retry AQL Generation #
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aql_generation_output = self.aql_fix_chain.run(
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{
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"adb_schema": self.graph.schema,
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"aql_query": aql_query,
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"aql_error": aql_error,
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},
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callbacks=callbacks,
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)
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########################
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#####################
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aql_generation_attempt += 1
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if aql_result is None:
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m = f"""
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Maximum amount of AQL Query Generation attempts reached.
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Unable to execute the AQL Query due to the following error:
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{aql_error}
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"""
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raise ValueError(m)
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_run_manager.on_text("AQL Result:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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str(aql_result), color="green", end="\n", verbose=self.verbose
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)
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########################
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# Interpret AQL Result #
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result = self.qa_chain(
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{
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"adb_schema": self.graph.schema,
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"user_input": user_input,
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"aql_query": aql_query,
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"aql_result": aql_result,
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},
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callbacks=callbacks,
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)
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########################
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# Return results #
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result = {self.output_key: result[self.qa_chain.output_key]}
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if self.return_aql_query:
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result["aql_query"] = aql_query
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if self.return_aql_result:
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result["aql_result"] = aql_result
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return result
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