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add embed model command and fix question invoke (#4766)

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* add embed model command and fix question invoke

* Update docs/tutorials/langchainpy.md

Co-authored-by: Kim Hallberg <hallberg.kim@gmail.com>

* Update docs/tutorials/langchainpy.md

---------

Co-authored-by: Kim Hallberg <hallberg.kim@gmail.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
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Shubham 2024-06-04 10:50:48 +05:30 committed by GitHub
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@ -45,7 +45,7 @@ all_splits = text_splitter.split_documents(data)
``` ```
It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb` It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
We also need to pull embedding model: `ollama pull nomic-embed-text`
```python ```python
from langchain.embeddings import OllamaEmbeddings from langchain.embeddings import OllamaEmbeddings
from langchain.vectorstores import Chroma from langchain.vectorstores import Chroma
@ -68,7 +68,8 @@ The next thing is to send the question and the relevant parts of the docs to the
```python ```python
from langchain.chains import RetrievalQA from langchain.chains import RetrievalQA
qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever()) qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
qachain.invoke({"query": question}) res = qachain.invoke({"query": question})
print(res['result'])
``` ```
The answer received from this chain was: The answer received from this chain was: