chatglm = ChatZhiPuAI(model_name="glm-4")query_engine= index.as_query_engine(llm=chatglm) response = query_engine.query("你的问题") print(response) (2)在test的同等目录下创建一个data文件夹用于存放加载的数据,作者这里在data文件夹中放入的是.txt文件用于导入。 (3)运行即可以下为展示结果 数据...
使用LlamaIndex,就像将as_query_engine与as_chat_engine交换一样简单: engine = index.as_chat_engine() output = engine.chat("What do I like to drink?") print(output) # "You enjoy drinking coffee." output = engine.chat("How do I brew it?") print(output) # "You brew coffee with a Ae...
使用LlamaIndex,就像将as_query_engine与as_chat_engine交换一样简单: engine = index.as_chat_engine() output = engine.chat("What do I like to drink?") print(output) # "You enjoy drinking coffee." output = engine.chat("How do I brew it?") print(output) # "You brew coffee with a Ae...
query_engine=index.as_query_engine() response= query_engine.query("<query_text>") print(response) 使用这种方法,您可以使用任何LLM。也许您有在本地运行的,或者在您自己的服务器上运行的LLM。只要类被实现并且返回了生成的token,它就应该可以正常工作。 请注意,我们需要使用prompt helper来定制提示的大小,因...
response = qa_engine.query("Llama2 有多少参数?") print(response) 1. 2. 3. 4. 5. 6. 7. 8. Llama 2有7B, 13B, 和70B参数。 可以看到首先对单轮式的回答。 可以看到,其实就是用索引获取到查询引擎,然后直接去对查询引擎进行提问就可以了。
num_queries=3, # 生成 query 数 use_async=False, # query_gen_prompt="...", # 可以自定义 query 生成的 prompt 模板 ) # 构建单轮 query engine query_engine = RetrieverQueryEngine.from_args( fusion_retriever, node_postprocessors=[reranker] ...
engine = index.as_query_engine( service_context=service_context, ) output = engine.query("What do I like to drink?") print(output) 使用LangChain,代码会变得很长: from langchain_community.document_loaders import DirectoryLoader # pip install "unstructured[md]" ...
response =llm.complete(prompt)print(str(response))#OutputThe author went to the Rhode IslandSchoolof Design (RISD) for artschool. 设置LLMLingua fromllama_index.query_engine import RetrieverQueryEnginefromllama_index.response_synthesizers import CompactAndRefinefromllama_index.indices.postprocessor import...
prompt = "\n\n".join(context_list + [question]) response = llm.complete(prompt) print(str(response)) #Output The author went to the Rhode Island School of Design (RISD) for art school. 设置LLMLingua from llama_index.query_engine import RetrieverQueryEngine ...
QueryEngine: Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recom...