#arp 30

hi gemi, this time we are very close, 
the prompt in the py file need a prepare,
as i just commented and shown by the running of f.py
Please give me a whole new g.py to replace f.py

And i just found that huggingface need us to count token/cost, right?

$ uv run  e.py
Traceback (most recent call last):
  File "/home/za/dev/orchestration/e.py", line 8, in <module>
    from langchain_community.chains import RetrievalQA
ImportError: cannot import name 'RetrievalQA' from 'langchain_community.chains' (/home/za/dev/orchestration/.venv/lib/python3.12/site-packages/langchain_community/chains/__init__.py)
za@zaasus:~/dev/orchestration
$

$ uv run  e.py
Traceback (most recent call last):
  File "/home/za/dev/orchestration/e.py", line 8, in <module>
    from langchain_community.chains import RetrievalQA
ImportError: cannot import name 'RetrievalQA' from 'langchain_community.chains' (/home/za/dev/orchestration/.venv/lib/python3.12/site-packages/langchain_community/chains/__init__.py)
za@zaasus:~/dev/orchestration
$ uv run  f.py  #replace RetrievalQA with LCEL syntax
Traceback (most recent call last):
  File "/home/za/dev/orchestration/f.py", line 7, in <module>
    from langchain.chains import RetrievalQA
ModuleNotFoundError: No module named 'langchain.chains'
za@zaasus:~/dev/orchestration
$ cat f.py

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_ollama import OllamaLLM
from langchain.chains import RetrievalQA
#from langchain_community.chains import RetrievalQA

# 1. Load your local 'claws' (The Checklist)
loader = TextLoader("./checklist.md")
documents = loader.load()

# 2. Split text into bite-sized 'LEGO' pieces
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(documents)

# 3. Create a local Vector Store (Reading Notes)
# This uses a tiny local model to 'embed' text into math vectors
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(documents=texts, embedding=embeddings)

# 4. Connect the Brain
llm = OllamaLLM(model="gemma3:4b", base_url="http://127.0.0.1:11434")

## 5. The RAG Chain (The Claw)
#rag_chain = RetrievalQA.from_chain_type(
#    llm=llm,
#    chain_type="stuff",
#    retriever=vectorstore.as_retriever()
#)


from langchain_core.runnables import RunnablePassthrough

# The modern way to build your SOS Helper
retriever = vectorstore.as_retriever()

# Create the LEGO pipe
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Run it!
print(rag_chain.invoke("What are the current stages in the SOS project?"))
za@zaasus:~/dev/orchestration
$
za@zaasus:~/dev/orchestration
$
za@zaasus:~/dev/orchestration
$ uv run  f.py  #replace RetrievalQA with LCEL syntax  AND use _community
Traceback (most recent call last):
  File "/home/za/dev/orchestration/f.py", line 8, in <module>
    from langchain_community.chains import RetrievalQA
ImportError: cannot import name 'RetrievalQA' from 'langchain_community.chains' (/home/za/dev/orchestration/.venv/lib/python3.12/site-packages/langchain_community/chains/__init__.py)
za@zaasus:~/dev/orchestration
$ uv run  f.py  #replace RetrievalQA with LCEL syntax  AND rm  _community
Created a chunk of size 652, which is longer than the specified 500
modules.json: 100%|██████████████████████████████████████████████████| 349/349 [00:00<00:00, 2.79MB/s]
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
config_sentence_transformers.json: 100%|█████████████████████████████| 116/116 [00:00<00:00, 1.06MB/s]
README.md: 10.5kB [00:00, 33.5MB/s]
sentence_bert_config.json: 100%|████████████████████████████████████| 53.0/53.0 [00:00<00:00, 403kB/s]

config.json: 100%|███████████████████████████████████████████████████| 612/612 [00:00<00:00, 4.65MB/s]
model.safetensors: 100%|█████████████████████████████████████████| 90.9M/90.9M [00:35<00:00, 2.55MB/s]
Loading weights: 100%|███████████████████████████████████████████| 103/103 [00:00<00:00, 14293.72it/s]
tokenizer_config.json: 100%|█████████████████████████████████████████| 350/350 [00:00<00:00, 3.59MB/s]
vocab.txt: 232kB [00:00, 325kB/s]
tokenizer.json: 466kB [00:01, 344kB/s]
special_tokens_map.json: 100%|███████████████████████████████████████| 112/112 [00:00<00:00, 1.07MB/s]
config.json: 100%|███████████████████████████████████████████████████| 190/190 [00:00<00:00, 1.65MB/s]
Traceback (most recent call last):
  File "/home/za/dev/orchestration/f.py", line 42, in <module>
    | prompt
      ^^^^^^
NameError: name 'prompt' is not defined
za@zaasus:~/dev/orchestration
$


# apr 29


What wrong with the following code?  it got 

TypeError: BaseModel.__init__() takes 1 positional argument but 2 were given

def bb():
    print("\n--- Template Call ---")
    template = """You are a senior Rust and Python developer.
    Explain the following concept to a beginner using a simple analogy: {concept}"""

    prompt = PromptTemplate.from_template(template)
    chain = prompt | llm | StrOutputParser

    chain.invoke({"concept": "the pipeline in LangChain expression language"})




--- Template Call ---
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[29], line 1
----> 1 bb()

File /tmp/ipython_edit_1hy9vq7a/ipython_edit_ofhq7_9a.py:11, in bb()
      8 prompt = PromptTemplate.from_template(template)
      9 chain = prompt | llm | StrOutputParser
---> 11 chain.invoke({"concept": "the pipeline in LangChain expression language"})

File ~/dev/orchestration/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3201, in RunnableSequence.invoke(self, input, config, **kwargs)
   3199                 input_ = context.run(step.invoke, input_, config, **kwargs)
   3200             else:
-> 3201                 input_ = context.run(step.invoke, input_, config)
   3202 # finish the root run
   3203 except BaseException as e:

File ~/dev/orchestration/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:5062, in RunnableLambda.invoke(self, input, config, **kwargs)
   5047 """Invoke this `Runnable` synchronously.
   5048
   5049 Args:
   (...)   5059
   5060 """
   5061 if hasattr(self, "func"):
-> 5062     return self._call_with_config(
   5063         self._invoke,
   5064         input,
   5065         ensure_config(config),
   5066         **kwargs,
   5067     )
   5068 msg = "Cannot invoke a coroutine function synchronously.Use `ainvoke` instead."
   5069 raise TypeError(msg)

......

# apr 28

## 15:55

Hi, the ENV. all set, 
ollama run gemma3:4b ## ok
But the curl did fail.

za@zaasus:~/tmp
$ echo $OLLAMA_
$OLLAMA_API_BASE  $OLLAMA_HOST      $OLLAMA_MODELS
za@zaasus:~/tmp
$ echo $OLLAMA_API_BASE
http://127.0.0.1:11434
za@zaasus:~/tmp
$ echo $OLLAMA_API_BASE  $OLLAMA_HOST   $OLLAMA_MODELS
http://127.0.0.1:11434 127.0.0.1:11434 /home/za/.ollama/models
za@zaasus:~/tmp
$ ll ~/.ollama/models/
total 24
drwxr-xr-x 4 za za  4096 Apr 19 19:17 ./
drwxr-xr-x 3 za za  4096 Apr 24 18:33 ../
drwxr-xr-x 2 za za 12288 Apr 23 00:38 blobs/
drwxr-xr-x 3 za za  4096 Mar 21 08:32 manifests/
za@zaasus:~/tmp
$ ollama list
NAME                        ID              SIZE      MODIFIED
phi4-mini:3.8b              78fad5d182a7    2.5 GB    5 days ago
chat-apr-22-gemma:latest    3229272cf4b4    3.3 GB    5 days ago
qwen2.5:14b                 7cdf5a0187d5    9.0 GB    8 days ago
qwen2.5:latest              845dbda0ea48    4.7 GB    2 weeks ago
codellama:latest            8fdf8f752f6e    3.8 GB    2 weeks ago
qwen2.5-coder:7b            dae161e27b0e    4.7 GB    2 weeks ago
deepseek-coder:6.7b         ce298d984115    3.8 GB    2 weeks ago
llama3.2:latest             2130127e04b5    3.3 GB    2 weeks ago
gemma3:1b                   8648f39daa8f    815 MB    5 weeks ago
gemma3:4b                   a2af6cc3eb7f    3.3 GB    5 weeks ago
za@zaasus:~/tmp
$ ollama run gemma3:4b """which model loaded?"""
I am Gemma, a large language model created by the Gemma team at Google
DeepMind. I’m an open-weights model, which means I’m widely available for
public use.

za@zaasus:~/tmp
$

za@zaasus:~/tmp
$ curl http://localhost:11434/api/show -d '{"name": "gemma3:4b"}'
Relay failed to localhost:11434za@zaasus:~/tmp
### relay failed?


## 1605

Looks it's due to 'localhost' DNS, 

za@zaasus:~/tmp
$ curl http://127.0.0.1:11434/api/show -d '{"name": "gemma3:4b"}'
{"license":"Gemma Terms of Use \n\nLast modified: February 21, 2024\n\nBy using, reproducing, modifying, ...


za@zaasus:~/tmp
$ echo $no_proxy
localhost,127.0.0.1,0.0.0.0,.local
za@zaasus:~/tmp
$ aider --model  ollama/gemma3:4b
──────────────────────────────────────────────────────────────────────────────────────────────────────
Aider v0.86.2
Model: ollama/gemma3:4b with whole edit format
Git repo: .git with 1 files
Repo-map: using 4096 tokens, auto refresh
──────────────────────────────────────────────────────────────────────────────────────────────────────
> /ask "which model running"

Repo-map can't include /home/za/tmp/path/to/gemini_cli.py
Has it been deleted from the file system but not from git?
Could you please add the contents of gemini_cli.py to the chat? I need to see the code to understand
which model is being used and how to answer your question.


Tokens: 166 sent, 37 received.

path/to/gemini_cli.py
Add file to the chat? (Y)es/(N)o/(D)on't ask again [Yes]:


## why 'aider' as for path/to/gemini_cli.py ?