路由式查询引擎 | Router Query Engine 这是Llama-index 中最简单的 Agentic RAG 实现方案。在这种方案中,我们只需要创建一个路由式查询引擎。它能够在 LLM 帮助下,(从提供的工具和查询引擎列表中)确定具体使用什么工具或查询引擎来解决用户查询的。 下图是本文要实现的路由式查询引擎的基本结构: ...
from llama_index.core.query_engine import NLSQLTableQueryEngine from llama_index.core.tools import QueryEngineTool sql_query_engine = NLSQLTableQueryEngine( sql_database=sql_database, tables=["albums", "tracks", "artists"], verbose=True, ) sql_tool = QueryEngineTool.from_defaults( query_en...
File ~\AppData\Local\anaconda3\envs\test\Lib\site-packages\llama_index\core\query_engine\router_query_engine.py:361, in ToolRetrieverRouterQueryEngine._query(self, query_bundle) 358 responses.append(query_engine.query(query_bundle)) 360 if len(responses) > 1: --> 361 final_response = com...
To ensure that the correct PDF is always selected for processing the query in the SubQuestionQueryEngine using the microsoft/Phi-3-mini-4k-instruct model in a Retrieval-Augmented Generation (RAG) setup with llama-index version 0.10.57, you need to properly configure the QueryEngineTool instances...
from llama_index.indices.query.query_transform import HyDEQueryTransform from llama_index.query_engine.transform_query_engine import ( TransformQueryEngine, ) index = VectorStoreIndex.from_documents(documents, service_context=service_context_gpt3) query_engine = index.as_query_engine(similarity_to...
engine = create_engine("sqlite:///chinook.db") sql_database =SQLDatabase(engine) 2.安装可观测性工具,官方推荐使用Arize Phoenix。 · # setupArizePhoenixforlogging/observability importphoenix as px importllama_index px.launch_app() llama_index.set_global_handler("arize_phoenix") ...
from llama_index.experimental.query_engine import PandasQueryEngin Initialize the PandasQueryEngine query_engine = PandasQueryEngine(df=df, llm=llm, verbose=True, synthesize_response=True) class Query(BaseModel): query: str app = FastAPI() Load the CSV file df = pd.read_csv("sample.csv") ...
Bug Description I tried to use the function of AutoMergingRetriever, when I ran the code auto_merging_engine = RetrieverQueryEngine.from_args( automerging_retriever, node_postprocessors=[rerank]) it showed the following bugs. however, wh...
vector-search-integrate-with-llamaindex.md vector-search-integrate-with-peewee.md vector-search-integrate-with-sqlalchemy.md vector-search-integration-overview.md vector-search-limitations.md vector-search-overview.md views.md wrong-index-solution.md Repository files navigation README License...
LlamaIndex and is fully compatible with Langchain, it will be pretty easy to use other LLMs.At the moment, however, your text WILL be processed with OpenAI, even if you're self-hosting this tool. If OpenAI's terms of service present a problem for you, we leave that to you to ...