盘古大模型 PANGULARGEMODELS-文档摘要

时间：2024-09-05 14:59:05

文档摘要

基于已有的知识库，进行摘要总结。有stuff、refine、map-reduce策略。

Stuff：将所有文档直接填充到prompt中，提给模型处理，适合文档较少的场景。

from pangukitsappdev.api.embeddings.factory import Embeddings
from pangukitsappdev.api.llms.factory import LLMs
from pangukitsappdev.api.memory.vector.factory import Vectors
from pangukitsappdev.api.memory.vector.vector_config import VectorStoreConfig, ServerInfoCss
from pangukitsappdev.skill.doc.summary import DocSummaryStuffSkill
vector_store_config = VectorStoreConfig(store_name="css",
                                        index_name="your_index_name",
                                        embedding=Embeddings.of("css"),
                                        text_key="name",
                                        vector_fields=["description"],
                                        distance_strategy="inner_product",
                                        server_info=ServerInfoCss(env_prefix="sdk.memory.css"))
vector_api = Vectors.of("css", vector_store_config)

# 检索
query = "杜甫"
docs = vector_api.similarity_search(query, 4)

# 摘要
doc_skill = DocSummaryStuffSkill(LLMs.of("pangu"))

print(doc_skill.execute({"documents": docs}))

Refine：基于首个文档，并循环后续文档来迭代更新答案。

from pangukitsappdev.api.embeddings.factory import Embeddings
from pangukitsappdev.api.llms.factory import LLMs
from pangukitsappdev.api.memory.vector.factory import Vectors
from pangukitsappdev.api.memory.vector.vector_config import VectorStoreConfig, ServerInfoCss
from pangukitsappdev.skill.doc.summary import DocSummaryRefineSkill
vector_store_config = VectorStoreConfig(store_name="css",
                                        index_name="your_index_name",
                                        embedding=Embeddings.of("css"),
                                        text_key="name",
                                        vector_fields=["description"],
                                        distance_strategy="inner_product",
                                        server_info=ServerInfoCss(env_prefix="sdk.memory.css"))

vector_api = Vectors.of("css", vector_store_config)

# 检索
query = "杜甫"
docs = vector_api.similarity_search(query, 4)

# 摘要
doc_skill = DocSummaryRefineSkill(LLMs.of("pangu"))

print(doc_skill.execute({"documents": docs}))

Map-Reduce：先将文档单独进行摘要，将摘要后的文档再提交给模型。必要时，会循环迭代摘要。

from pangukitsappdev.api.embeddings.factory import Embeddings
from pangukitsappdev.api.llms.factory import LLMs
from pangukitsappdev.api.memory.vector.factory import Vectors
from pangukitsappdev.api.memory.vector.vector_config import VectorStoreConfig, ServerInfoCss
from pangukitsappdev.skill.doc.summary import DocSummaryMapReduceSkill
vector_store_config = VectorStoreConfig(store_name="css",
                                        index_name="your_index_name",
                                        embedding=Embeddings.of("css"),
                                        text_key="name",
                                        vector_fields=["description"],
                                        distance_strategy="inner_product",
                                        server_info=ServerInfoCss(env_prefix="sdk.memory.css"))

vector_api = Vectors.of("css", vector_store_config)

# 检索
query = "杜甫"
docs = vector_api.similarity_search(query, 4)

# 摘要
doc_skill = DocSummaryMapReduceSkill(LLMs.of("pangu"))

print(doc_skill.execute({"documents": docs}))

父主题： Skill（技能）

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