forked from Archives/langchain
fd866d1801
# Improve Cypher QA prompt The current QA prompt is optimized for networkX answer generation, which returns all the possible triples. However, Cypher search is a bit more focused and doesn't necessary return all the context information. Due to that reason, the model sometimes refuses to generate an answer even though the information is provided: ![Screenshot from 2023-05-24 08-36-23](https://github.com/hwchase17/langchain/assets/19948365/351cf9c1-2567-447c-91fd-284ae3fa1ccf) To fix this issue, I have updated the prompt. Interestingly, I tried many variations with less instructions and they didn't work properly. However, the current fix works nicely. ![Screenshot from 2023-05-24 08-37-25](https://github.com/hwchase17/langchain/assets/19948365/fc830603-e6ec-4a23-8a86-eaf572996014)
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
"""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 pydantic import Field
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.graphs.neo4j_graph import Neo4jGraph
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from langchain.prompts.base import BasePromptTemplate
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class GraphCypherQAChain(Chain):
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"""Chain for question-answering against a graph by generating Cypher statements."""
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graph: Neo4jGraph = Field(exclude=True)
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cypher_generation_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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@property
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def input_keys(self) -> List[str]:
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"""Return the 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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"""Return the 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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qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
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cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
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**kwargs: Any,
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) -> GraphCypherQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
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return cls(
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qa_chain=qa_chain,
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cypher_generation_chain=cypher_generation_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 Cypher 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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generated_cypher = self.cypher_generation_chain.run(
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{"question": question, "schema": self.graph.get_schema}, callbacks=callbacks
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)
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_run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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generated_cypher, color="green", end="\n", verbose=self.verbose
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)
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context = self.graph.query(generated_cypher)
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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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result = self.qa_chain(
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{"question": question, "context": context},
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callbacks=callbacks,
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)
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return {self.output_key: result[self.qa_chain.output_key]}
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