Model: Rsr2425/simplify-ft-arctic-embed-l
Author: Rsr2425
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:64, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-l, base_model:finetune:Snowflake/snowflake-arctic-embed-l, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-l
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:64, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget: source_sentence: 1. What is the first step to take when implementing architecture as code according to the provided context?
2. How should the content of each file be formatted when outputting code?
sentences: architecture is, in the end, implemented as code.\\n\\nThink step by step and reason yourself to the right decisions to make sure we get it right.\\nYou will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.\\n\\nThen you will output the content of each file including ALL code.\\nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such that\\nFILENAME is the lowercase file name including the file extension,\\nLANG is the markup code block language for the code\'s language, and CODE is the code:\\n\\nFILENAME\\n\`\`\`LANG\\nCODE\\n\`\`\`\\n\\nYou will start with the \\"entrypoint\\" file, then go to the, Stream tokens:
for message, metadata in graph.stream(    {"question": "What is Task Decomposition?"}, stream_mode="messages"):    print(message.content, end="|")
|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| It| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|)| prompting|,| which| encourages| the| model| to| think| step| by| step|,| or| through| more| structured| methods| like| the| Tree| of| Thoughts|.| This| approach| not| only| simplifies| task| execution| but| also| provides| insights| into| the| model|'s| reasoning| process|.||
tipFor async invocations, use:result = await graph.ainvoke(...)andasync for step in graph.astream(...):, return {"answer": response.content}graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])graph_builder.add_edge(START, "analyze_query")graph = graph_builder.compile(), source_sentence: 1. What is the purpose of the DocumentTransformer object in the context provided?  
2. Where can one find detailed documentation on how to use DocumentTransformers?
sentences: Learn more about splitting text using different methods by reading the how-to docs
Code (py or js)
Scientific papers
Interface: API reference for the base interface.

DocumentTransformer: Object that performs a transformation on a list
of Document objects.

Docs: Detailed documentation on how to use DocumentTransformers
Integrations
Interface: API reference for the base interface., {'retrieve': {'context': [Document(id='a42dc78b-8f76-472a-9e25-180508af74f3', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 1585}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into, Do I need to use LangGraph?LangGraph is not required to build a RAG application. Indeed, we can implement the same application logic through invocations of the individual components:question = "..."retrieved_docs = vector_store.similarity_search(question)docs_content = "\n\n".join(doc.page_content for doc in retrieved_docs)prompt = prompt.invoke({"question": question, "context": docs_content})answer = llm.invoke(prompt)The benefits of LangGraph include:
Support for multiple invocation modes: this logic would need to be rewritten if we wanted to stream output tokens, or stream the results of individual steps;
Automatic support for tracing via LangSmith and deployments via LangGraph Platform;, source_sentence: 1. What mode did the agent move into after the clarifications were made?
2. What instructions were given to the agent regarding the code writing process?
sentences: = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)all_splits = text_splitter.split_documents(docs)# Update metadata (illustration purposes)total_documents = len(all_splits)third = total_documents // 3for i, document in enumerate(all_splits):    if i < third:        document.metadata["section"] = "beginning"    elif i < 2 * third:        document.metadata["section"] = "middle"    else:        document.metadata["section"] = "end"# Index chunksvector_store = InMemoryVectorStore(embeddings)_ = vector_store.add_documents(all_splits)# Define schema for searchclass Search(TypedDict):    """Search query."""    query: Annotated[str, ..., "Search query to run."]    section: Annotated[        Literal["beginning", "middle", "end"],, limitations:'), Document(id='ca7f06e4-2c2e-4788-9a81-2418d82213d9', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 32942, 'section': 'end'}, page_content='}\n]\nThen after these clarification, the agent moved into the code writing mode with a different system message.\nSystem message:'), Document(id='1fcc2736-30f4-4ef6-90f2-c64af92118cb', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 35127, 'section': 'end'}, page_content='"content": "You will get instructions for code to write.\\nYou will write a very long answer. Make sure that every detail of the architecture is, in the end, implemented as code.\\nMake sure that every detail of the architecture is,, Build a Retrieval Augmented Generation (RAG) App: Part 1 | 🦜️🔗 LangChain, source_sentence: 1. What is the purpose of the `getpass` module in the provided context?
2. How is the chat model initialized in the given code snippet?
sentences: Select chat model:Groq▾GroqOpenAIAnthropicAzureGoogle VertexAWSCohereNVIDIAFireworks AIMistral AITogether AIIBM watsonxDatabrickspip install -qU "langchain[groq]"import getpassimport osif not os.environ.get("GROQ_API_KEY"):  os.environ["GROQ_API_KEY"] = getpass.getpass("Enter API key for Groq: ")from langchain.chat_models import init_chat_modelllm = init_chat_model("llama3-8b-8192", model_provider="groq"), One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or RAG.
This is a multi-part tutorial:, user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.")]}}----------------{'generate': {'answer': 'Task decomposition is the process of breaking down a complex task into smaller, more manageable steps. This technique, often enhanced by methods like Chain of Thought (CoT) or Tree of Thoughts, allows models to reason through tasks systematically and improves performance by clarifying the thought process. It can be achieved through simple prompts, task-specific instructions, or human inputs.'}}----------------, source_sentence: 1. How do chat models utilize the state of the graph to recover sources for generated answers?  
2. What is the significance of the "context" field in the state when returning sources?
sentences: Docs: Detailed documentation on how to use embeddings.
Integrations: 30+ integrations to choose from.
Interface: API reference for the base interface.

VectorStore: Wrapper around a vector database, used for storing and
querying embeddings.

Docs: Detailed documentation on how to use vector stores.
Integrations: 40+ integrations to choose from.
Interface: API reference for the base interface., Returning sources​
Note that by storing the retrieved context in the state of the graph, we recover sources for the model's generated answer in the "context" field of the state. See this guide on returning sources for more detail.
Go deeper​
Chat models take in a sequence of messages and return a message., display(Image(graph.get_graph().draw_mermaid_png()))
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 1
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 1
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 1
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 1
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 1
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 1
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 1
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 1
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 8
Created: 2025-02-25 19:57:41