from langchain_openaiimportChatOpenAI from typingimportList from pydanticimportBaseModel,Field from langchainimportPromptTemplateclassRatingScore(BaseModel):relevance_score:float=Field(...,description="The rele
from langchain.docstore.document import Document from langchain_openai import ChatOpenAI from typing import List from pydantic import BaseModel, Field from langchain import PromptTemplate class RatingScore(BaseModel): relevance_score: float = Field(..., description="The relevance score of a document...
基于LLM 的Reranking实现示例如下: from langchain.docstore.document import Document from langchain_openai import ChatOpenAI from typing import List from pydantic import BaseModel, Field from langchain import PromptTemplate class RatingScore(BaseModel): relevance_score: float = Field(..., description="...
LLM Wrappers:封装 OpenAI GPT、Anthropic Claude、开源模型(LLaMA、ChatGLM)等,支持模型切换和本地部署。 Chains:通过 LLMChain、RetrievalQAChain 等组合模块,支持多步推理(如先检索→后过滤→再生成)。 Agents:允许模型调用外部工具(如搜索引擎、Python 解释器),实现动态决策。 核心优化技术 Hybrid Search:同时使用 ...
mcp chatbot chroma hyde reranking rag streamlit large-language-models llm splade openai-chatgpt langchain-python retrieval-augmented-generation Updated May 5, 2025 Jupyter Notebook Anush008 / fastembed-rs Sponsor Star 517 Code Issues Pull requests Rust library for generating vector embeddings, ...
vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.text_splitter import CharacterTextSplitter from langchain.schema import Document from langchain.chains.qa_with_sources import load_qa_...
LangChain Batched, modification of the Batching algoritm in Infinity Documentation View the docs at https:///michaelfeil.github.io/infinity on how to get started. After startup, the Swagger Ui will be available under {url}:{port}/docs, in this case http://localhost:7997/docs. You can al...
Relevance Score:""")llm=ChatOpenAI(temperature=0,model_name="gpt-4",max_tokens=4000)llm_chain=prompt_template|llm.with_structured_output(RatingScore)scored_docs=[]fordocindocs: input_data={"query": query,"doc": doc.page_content}
from langchain.docstore.document import Document from langchain_openai import ChatOpenAI from typing import List from pydantic import BaseModel, Field from langchain import PromptTemplate class RatingScore(BaseModel): relevance_score: float = Field(..., description="The relevance score of a document...
32 32 EMBEDDING_PROVIDER: str = "openai" 33 33 COMPLETION_PROVIDER: str = "ollama" 34 34 PARSER_PROVIDER: str = "combined" 35 + RERANKER_PROVIDER: str = "bge" 35 36 36 37 # Storage settings 37 38 STORAGE_PATH: str = "./storage" @@ -53,13 +54,19 @@ class Settin...