Create custom single-hop queries from your documents
Load sample documents
I am using documents from sample of gitlab handbook. You can download it by running the below command.
from langchain_community.document_loaders import DirectoryLoader
path = "Sample_Docs_Markdown/"
loader = DirectoryLoader(path, glob="**/*.md")
docs = loader.load()
Create KG
Create a base knowledge graph with the documents
from ragas.testset.graph import KnowledgeGraph
from ragas.testset.graph import Node, NodeType
kg = KnowledgeGraph()
for doc in docs:
kg.nodes.append(
Node(
type=NodeType.DOCUMENT,
properties={
"page_content": doc.page_content,
"document_metadata": doc.metadata,
},
)
)
Set up the LLM and Embedding Model
You may use any of your choice, here I am using models from open-ai.
from ragas.llms.base import llm_factory
from ragas.embeddings.base import embedding_factory
llm = llm_factory()
embedding = embedding_factory()
Setup the transforms
Here we are using 2 extractors and 2 relationship builders. - Headline extrator: Extracts headlines from the documents - Keyphrase extractor: Extracts keyphrases from the documents - Headline splitter: Splits the document into nodes based on headlines
from ragas.testset.transforms import apply_transforms
from ragas.testset.transforms import (
HeadlinesExtractor,
HeadlineSplitter,
KeyphrasesExtractor,
)
headline_extractor = HeadlinesExtractor(llm=llm)
headline_splitter = HeadlineSplitter(min_tokens=300, max_tokens=1000)
keyphrase_extractor = KeyphrasesExtractor(
llm=llm, property_name="keyphrases", max_num=10
)
transforms = [
headline_extractor,
headline_splitter,
keyphrase_extractor,
]
apply_transforms(kg, transforms=transforms)
Output
Applying KeyphrasesExtractor: 6%| | 2/36 [00:01<00:20, 1Property 'keyphrases' already exists in node '514fdc'. Skipping!
Applying KeyphrasesExtractor: 11%| | 4/36 [00:01<00:10, 2Property 'keyphrases' already exists in node '84a0f6'. Skipping!
Applying KeyphrasesExtractor: 64%|▋| 23/36 [00:03<00:01, Property 'keyphrases' already exists in node '93f19d'. Skipping!
Applying KeyphrasesExtractor: 72%|▋| 26/36 [00:04<00:00, 1Property 'keyphrases' already exists in node 'a126bf'. Skipping!
Applying KeyphrasesExtractor: 81%|▊| 29/36 [00:04<00:00, Property 'keyphrases' already exists in node 'c230df'. Skipping!
Applying KeyphrasesExtractor: 89%|▉| 32/36 [00:04<00:00, 1Property 'keyphrases' already exists in node '4f2765'. Skipping!
Property 'keyphrases' already exists in node '4a4777'. Skipping!
Configure personas
You can also do this automatically by using the automatic persona generator
from ragas.testset.persona import Persona
person1 = Persona(
name="gitlab employee",
role_description="A junior gitlab employee curious on workings on gitlab",
)
persona2 = Persona(
name="Hiring manager at gitlab",
role_description="A hiring manager at gitlab trying to underestand hiring policies in gitlab",
)
persona_list = [person1, persona2]
SingleHop Query
Inherit from SingleHopQuerySynthesizer and modify the function that generates scenarios for query creation.
Steps: - find qualified set of nodes for the query creation. Here I am selecting all nodes with keyphrases extracted. - For each qualified set - Match the keyphrase with one or more persona. - Create all possible combinations of (Node, Persona, Query Style, Query Length) - Samples the required number of queries from the combinations
from ragas.testset.synthesizers.single_hop import (
SingleHopQuerySynthesizer,
SingleHopScenario,
)
from dataclasses import dataclass
from ragas.testset.synthesizers.prompts import (
ThemesPersonasInput,
ThemesPersonasMatchingPrompt,
)
@dataclass
class MySingleHopScenario(SingleHopQuerySynthesizer):
theme_persona_matching_prompt = ThemesPersonasMatchingPrompt()
async def _generate_scenarios(self, n, knowledge_graph, persona_list, callbacks):
property_name = "keyphrases"
nodes = []
for node in knowledge_graph.nodes:
if node.type.name == "CHUNK" and node.get_property(property_name):
nodes.append(node)
number_of_samples_per_node = max(1, n // len(nodes))
scenarios = []
for node in nodes:
if len(scenarios) >= n:
break
themes = node.properties.get(property_name, [""])
prompt_input = ThemesPersonasInput(themes=themes, personas=persona_list)
persona_concepts = await self.theme_persona_matching_prompt.generate(
data=prompt_input, llm=self.llm, callbacks=callbacks
)
base_scenarios = self.prepare_combinations(
node,
themes,
personas=persona_list,
persona_concepts=persona_concepts.mapping,
)
scenarios.extend(
self.sample_combinations(base_scenarios, number_of_samples_per_node)
)
return scenarios
query = MySingleHopScenario(llm=llm)
scenarios = await query.generate_scenarios(
n=5, knowledge_graph=kg, persona_list=persona_list
)
scenarios[0]
SingleHopScenario(
nodes=1
term=what is an ally
persona=name='Hiring manager at gitlab' role_description='A hiring manager at gitlab trying to underestand hiring policies in gitlab'
style=Web search like queries
length=long)
Modify prompt to customize the query style
Here I am replacing the default prompt with an instruction to generate only Yes/No questions. This is an optional step.
instruction = """Generate a Yes/No query and answer based on the specified conditions (persona, term, style, length)
and the provided context. Ensure the answer is entirely faithful to the context, using only the information
directly from the provided context.
### Instructions:
1. **Generate a Yes/No Query**: Based on the context, persona, term, style, and length, create a question
that aligns with the persona's perspective, incorporates the term, and can be answered with 'Yes' or 'No'.
2. **Generate an Answer**: Using only the content from the provided context, provide a 'Yes' or 'No' answer
to the query. Do not add any information not included in or inferable from the context."""
prompt = query.get_prompts()["generate_query_reference_prompt"]
prompt.instruction = instruction
query.set_prompts(**{"generate_query_reference_prompt": prompt})
result = await query.generate_sample(scenario=scenarios[-1])