Python+Ollama开发报错：nomic-embed-text模型未找到解决指引

在M2 Mac上使用Python 3.11.7（通过venv管理依赖）开发加载HTML并基于上下文查询的RAG系统，测试Ollama时出现模型未找到错误。

核心代码 #

from langchain_community.llms import Ollama
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import Chroma
from langchain_community import embeddings
from langchain_community.chat_models import ChatOllama
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain.text_splitter import CharacterTextSplitter

model_local = Ollama(base_url="http://192.168.0.200:11434", model="mistral")

# 1. Split data into chunks
urls = [
    "https://es.wikipedia.org/wiki/The_A-Team",
]
docs = [WebBaseLoader(url).load() for url in urls]
docs_list = [item for sublist in docs for item in sublist]
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
doc_splits = text_splitter.split_documents(docs_list)

# 2. Convert documents to Embeddings and store them
vectorstore = Chroma.from_documents(
    documents=doc_splits,
    collection_name="rag-chroma",
    embedding=embeddings.ollama.OllamaEmbeddings(model='nomic-embed-text'),
)
retriever = vectorstore.as_retriever()

# 3. Before RAG
print("Before RAG\n")
before_rag_template = "What is {topic}"
before_rag_prompt = ChatPromptTemplate.from_template(before_rag_template)
before_rag_chain = before_rag_prompt | model_local | StrOutputParser()
print(before_rag_chain.invoke({"topic": "Ollama"}))

# 4. After RAG
print("\n########\nAfter RAG\n")
after_rag_template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
after_rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | after_rag_prompt
    | model_local
    | StrOutputParser()
)
print(after_rag_chain.invoke("Quien integra Brigada A?"))

报错信息 #

cd /Users/santiago/Proyects/OllamaURL ; /usr/bin/env /Users/santiago/Proyects/OllamaURL/env/bin/python /Users/santiago/.vscode/extensions/ms-python.debugpy-2024.2.0-darwin-arm64/bundled/libs/debugpy/adapter/../../debugpy/launcher 54637 -- /Users/santiago/Proyects/OllamaURL/rag.py
Traceback (most recent call last):
  File "/Users/santiago/Proyects/OllamaURL/rag.py", line 25, in <module>
    vectorstore = Chroma.from_documents(
                  ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 778, in from_documents
    return cls.from_texts(
           ^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 736, in from_texts
    chroma_collection.add_texts(
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 275, in add_texts
    embeddings = self._embedding_function.embed_documents(texts)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/embeddings/ollama.py", line 204, in embed_documents
    embeddings = self._embed(instruction_pairs)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/embeddings/ollama.py", line 192, in _embed
    return [self._process_emb_response(prompt) for prompt in iter_]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/embeddings/ollama.py", line 192, in <listcomp>
    return [self._process_emb_response(prompt) for prompt in iter_]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/santiago/Proyects/OllamaURL/env/lib/python3.11/site-packages/langchain_community/embeddings/ollama.py", line 166, in _process_emb_response
    raise ValueError(
ValueError: Error raised by inference API HTTP code: 404, {"error":"model 'nomic-embed-text' not found, try pulling it first"}

• 拉取缺失的嵌入模型：代码中使用的nomic-embed-text模型未在Ollama服务中加载。如果Ollama运行在远程机器（192.168.0.200），需在该机器执行命令：

  ollama pull nomic-embed-text
  

  若Ollama在本地Mac运行，直接在本地终端执行上述命令。

• 为嵌入模型指定远程地址：代码中LLM模型指定了base_url，但嵌入模型默认访问本地localhost:11434，需同步配置远程地址：

  embedding=embeddings.ollama.OllamaEmbeddings(
      model='nomic-embed-text',
      base_url="http://192.168.0.200:11434"
  )
  

• 验证模型可用性：执行curl命令测试模型是否可正常调用：

  curl http://192.168.0.200:11434/api/embeddings -d '{
    "model": "nomic-embed-text",
    "prompt": "test"
  }'
  

  返回正常嵌入向量则说明配置正确。

• 确认模型已加载：在Ollama服务所在机器执行ollama list，确认nomic-embed-text出现在模型列表中。

内容的提问来源于stack exchange，提问作者safernandez666