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Updated 2 months ago

GreetingsI have built a pipeline that utilize llama index to read a pdf and ask question

Greetings
I have built a pipeline that utilize llama index to read a pdf and ask question and retrieve most relevent chunk of data using vector store index
I aim to edit the text in the retrieved node(which is the most revlevent one) and then delete the original node and insert the updated node
I managed to delete using _delete method
but when I use the _insert method or add_nodes_to_index method , I get the following error
ValueError Traceback (most recent call last)
<ipython-input-77-c1a98b89981b> in <cell line: 1>()
----> 1 index.insert(nodes=response)

3 frames
/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in setattr(self, name, value)
355
356 if self.config.extra is not Extra.allow and name not in self.fields:
--> 357 raise ValueError(f'"{self.class.name}" object has no field "{name}"')
358 elif not self.config.allowmutation or self.config.frozen:
359 raise TypeError(f'"{self.class.__name}" is immutable and does not support item assignment')

ValueError: "NodeWithScore" object has no field "embedding"

althogh I just replaced the text in the node and didn't make any changes to it
Is there a way to insert new node to an existing vector store index object ?
Thanks in advance
k
n
69 comments
Please stand by @nagwa as I search the knowledge base. I normally take about 20 seconds or so. :wave:
----------------------
Hello,

Yes, you can insert a new node into an existing VectorStoreIndex object. However, the error you're encountering suggests that the node you're trying to insert does not have an 'embedding' field. In LlamaIndex, to insert a node into a vector index, it should have an embedding. This is mentioned in the LlamaIndex documentation ().

Here's an example of how you can add data to an existing index:

Plain Text
from llama_index.core import VectorStoreIndex, Document

# Assuming you have an existing index
index = VectorStoreIndex.from_documents(
    [Document(text="The sky is purple in Portland, Maine")],
    uri="/tmp/new_dataset",
)

# Insert new nodes
nodes = [node.node for node in response]
index.insert_nodes(nodes)


In your case, you might need to ensure that the node you're trying to insert has an embedding. If it doesn't, you might need to generate an embedding for it before trying to insert it.

If you're still encountering issues, it might be helpful to review the LlamaIndex API reference () or the document management guide () for more details.

I hope this helps! Let me know if you have any other questions.
----------------------
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----------------------
That's worked fine for me , but how to make sure that it's inserted correctly?
@kapa.ai That's worked fine for me , but how to make sure that it's inserted correctly?
Let me check the knowledge base for you. One moment! :books:
----------------------
You can verify that a node has been correctly inserted by querying the index and checking if the new node appears in the results. Here's an example of how you can do this:

Plain Text
# Query the index
response = index.query("Your query here")

# Print the response
print(response)

# Print the source nodes
for node in response.source_nodes:
    print(node.get_text())


In the code above, replace "Your query here" with a query that should match the text of the node you've inserted. If the node has been correctly inserted, it should appear in the results.

You can also check the total number of nodes in the index using the len() function:

Plain Text
print(len(index))


This will return the total number of nodes in the index. If a node has been correctly inserted, this number should increase.

For more information, you can refer to the LlamaIndex documentation () and the example notebooks () on GitHub.
----------------------
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----------------------
@kapa.ai how can i delete a node , i use ._delete and pass the _id of the node but when i make a query , it retrireved this node although i deleted it ?
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----------------------
In LlamaIndex, you can delete a document from most index data structures by specifying a document_id. However, please note that the tree index currently does not support deletion. Here's an example of how you can delete a document:

Plain Text
index.delete_ref_doc("doc_id_0", delete_from_docstore=True)


The delete_from_docstore parameter will default to False in case you are sharing nodes between indexes using the same docstore. However, these nodes will not be used when querying when this is set to False as they will be deleted from the index_struct of the index, which keeps track of which nodes can be used for querying.

If you're still seeing the node after deleting it, it's possible that the deletion hasn't been fully propagated through the index. You might need to refresh the index to ensure the deletion is fully processed.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai i need to search on the document and find specific numbers and then replace them maintaitng the whole structure of the document the same
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----------------------
To search for specific numbers in a document and replace them while maintaining the structure of the document, you can use the LlamaIndex's query engine to find the relevant nodes, then modify the text of those nodes. Here's a basic example:

Plain Text
# Assuming you have an existing index
index = VectorStoreIndex.from_documents(
    [Document(text="The number that is being searched for is ten.")],
    uri="/tmp/new_dataset",
)

# Query the index
response = index.query("ten")

# Replace the number in the text of the nodes
for node in response.source_nodes:
    node.text = node.text.replace("ten", "your replacement here")

# Delete the old nodes
index.delete_ref_doc("doc_id_0", delete_from_docstore=True)

# Insert the new nodes
index.insert_nodes([node.node for node in response.source_nodes])


In this example, replace "ten" with the number you're searching for, and "your replacement here" with the text you want to replace it with.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the number is part of a larger number or word (e.g., replacing "10" would also replace "100" or "tennis"). You might need to use more advanced text processing techniques to handle these cases.

For more information, you can refer to the LlamaIndex documentation () and the example notebooks () on GitHub.
----------------------
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----------------------
@kapa.ai this code doesn't delete the node from the vector store index
index.delete_nodes("b70f106d-e416-42b2-a9e2-8e7e28f6f9d3")
Let me check the knowledge base for you. One moment! :books:
----------------------
The delete_nodes method in LlamaIndex is used to delete nodes from the index. However, it requires a list of node IDs, not a single node ID. If you want to delete a single node, you should still pass its ID as a list. Here's how you can do it:

Plain Text
index.delete_nodes(["b70f106d-e416-42b2-a9e2-8e7e28f6f9d3"])


Also, please note that the delete_nodes method deletes nodes from the index, but not from the document store. If you want to delete the document associated with the node from the document store as well, you should use the delete_ref_doc method and pass the ref_doc_id of the document:

Plain Text
index.delete_ref_doc("ref_doc_id_here", delete_from_docstore=True)


For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai after creating the knowledge base , how to used all the text data again to create a new pdf file
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----------------------
To create a new PDF file from the text data in your knowledge base, you can use a PDF library such as fpdf in Python. Here's a basic example:

Plain Text
from fpdf import FPDF

# Create instance of FPDF class
pdf = FPDF()

# Add a page
pdf.add_page()

# Set font
pdf.set_font("Arial", size = 15)

# Assuming `text_data` is a list of strings from your knowledge base
text_data = ["Text from first document", "Text from second document", "..."]

for line in text_data:
    # Add a cell
    pdf.cell(200, 10, txt = line, ln = True)

# Save the pdf with name .pdf
pdf.output("Your_new_file.pdf")


In this example, replace text_data with the text data from your knowledge base.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the text data includes special characters that are not supported by the PDF format. You might need to use more advanced text processing techniques to handle these cases.

For more information, you can refer to the fpdf documentation ().
----------------------
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----------------------
@kapa.ai how to loop over all nodes in the index and extract text
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----------------------
You can loop over all nodes in the index and extract text by using the get_all_nodes method of the index. Here's an example:

Plain Text
# Get all nodes
all_nodes = index.get_all_nodes()

# Loop over all nodes and print text
for node in all_nodes:
    print(node.text)


In this example, index is your existing LlamaIndex instance.

Please note that this will retrieve all nodes in the index, which might be a large number depending on the size of your index. If you only need a subset of the nodes, you might want to use the retrieve method with a specific query instead.

For more information, you can refer to the LlamaIndex documentation ().
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@kapa.ai there is no method called get_all_nodes for the index
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion. You're correct, there isn't a get_all_nodes method in LlamaIndex.

To retrieve all nodes, you can use the retrieve_all method of the RecursiveRetriever class. Here's an example:

Plain Text
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.schema import QueryBundle

# Assuming you have an existing retriever
retriever = RecursiveRetriever(...)

# Create a query bundle
query_bundle = QueryBundle(query="Your query here")

# Retrieve all nodes
all_nodes = retriever.retrieve_all(query_bundle)

# Loop over all nodes and print text
for node in all_nodes:
    print(node.text)


In this example, replace "Your query here" with a query that matches all nodes in your index.

Please note that this will retrieve all nodes that match the query, which might be a large number depending on the size of your index and the specificity of your query. If you only need a subset of the nodes, you might want to use a more specific query.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai how to get the number of nodes in the index? using len doesn't work
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----------------------
To get the number of nodes in the index, you can use the count method of the VectorStoreIndex class. Here's an example:

Plain Text
# Assuming you have an existing index
index = VectorStoreIndex(...)

# Get the number of nodes
num_nodes = index.count()

print(f"Number of nodes: {num_nodes}")


In this example, index is your existing LlamaIndex instance.

Please note that this will return the total number of nodes in the index, which might be a large number depending on the size of your index.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@this doesn't work also
@kapa.ai this doesn't work also
Let me check the knowledge base for you. One moment! :books:
----------------------
I apologize for the confusion. The LlamaIndex does not provide a direct method to count the total number of nodes in the index.

However, you can retrieve all nodes using a retriever and then count the number of nodes. Here's an example:

Plain Text
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.schema import QueryBundle

# Assuming you have an existing retriever
retriever = RecursiveRetriever(...)

# Create a query bundle
query_bundle = QueryBundle(query="Your query here")

# Retrieve all nodes
all_nodes = retriever.retrieve_all(query_bundle)

# Get the number of nodes
num_nodes = len(all_nodes)

print(f"Number of nodes: {num_nodes}")


In this example, replace "Your query here" with a query that matches all nodes in your index.

Please note that this will retrieve all nodes that match the query, which might be a large number depending on the size of your index and the specificity of your query. If you only need a subset of the nodes, you might want to use a more specific query.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai how to find the order of a node in the pdf file from the index
Let me check the knowledge base for you. One moment! :books:
----------------------
Each node in LlamaIndex has metadata associated with it, which can include information about its position in the original document. When you load a PDF file into LlamaIndex, the PDFReader or PyMuPDFReader class can include page number information in the node metadata.

Here's an example of how you can get the page number of a node:

Plain Text
# Assuming `node` is a node from your index
page_number = node.metadata.get('page_number')
print(f"Page number: {page_number}")


In this example, node is a node from your index. The metadata attribute of a node is a dictionary that can include various metadata fields, depending on how the node was created. The page_number field is included when you load a PDF file using the PDFReader or PyMuPDFReader class.

Please note that this will only work if the PDFReader or PyMuPDFReader class was used to load the PDF file and the include_page_numbers parameter was set to True (which is the default).

For more information, you can refer to the LlamaIndex documentation () and the example notebooks () on GitHub.
----------------------
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----------------------
@kapa.ai how to add header in the metadata of the node when using markdownheader splitter to split text and ingest it to the vector store index
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----------------------
To add headers in the metadata of the node when using MarkdownHeaderSplitter to split text and ingest it to the VectorStoreIndex, you can follow the steps below:

  1. First, you need to split your text into nodes using MarkdownHeaderSplitter. This splitter will automatically add header information to the metadata of each node.
Plain Text
from llama_index.core.node_parser import MarkdownHeaderSplitter

# Assuming `text` is your markdown text
splitter = MarkdownHeaderSplitter()
nodes = splitter.get_nodes_from_documents([text])


  1. Then, you can insert these nodes into your VectorStoreIndex. The insert_nodes method of the VectorStoreIndex will automatically include the metadata of each node.
Plain Text
# Assuming `index` is your VectorStoreIndex
index.insert_nodes(nodes)


In this example, text is your markdown text and index is your VectorStoreIndex. The MarkdownHeaderSplitter class splits the text into nodes based on markdown headers and includes the header information in the metadata of each node.

For more information, you can refer to the LlamaIndex documentation () and the example notebooks () on the LlamaIndex website.
----------------------
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@kapa.ai what is the default splitter in vector store index
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----------------------
The default splitter in VectorStoreIndex is the SentenceSplitter. This is used when no other splitter is specified in the transformations parameter of the from_documents method. Here's an example:

Plain Text
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter

# Assuming `documents` is your list of documents
splitter = SentenceSplitter(chunk_size=1024)
index = VectorStoreIndex.from_documents(documents, transformations=[splitter])


In this example, if you don't provide a transformations parameter, the SentenceSplitter will be used by default.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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@kapa.ai can i use llama index metadata filters in langchain?
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can use LlamaIndex's metadata filters in Langchain. LlamaIndex's metadata filters are designed to work with any project that uses LlamaIndex for managing and querying data.

Here's an example of how you can use metadata filters in LlamaIndex:

Plain Text
from llama_index.core.vector_stores import MetadataFilters, MetadataFilter

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="author", value="John Doe"),
    ]
)

retriever = index.as_retriever(filters=filters)


In this example, replace "author" and "John Doe" with the key and value you want to filter on, and index with your LlamaIndex instance.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai how to loop through all nodes in the vector store index by their order in the document
Let me check the knowledge base for you. One moment! :books:
----------------------
To loop through all nodes in the VectorStoreIndex by their order in the document, you would need to have some sort of ordering metadata associated with each node. This could be a page number, a paragraph number, or any other kind of sequential identifier.

Assuming you have such an identifier stored in the metadata of each node under the key 'order', you could retrieve all nodes and then sort them by this identifier. Here's an example:

Plain Text
# Assuming you have an existing retriever
retriever = RecursiveRetriever(...)

# Create a query bundle
query_bundle = QueryBundle(query="Your query here")

# Retrieve all nodes
all_nodes = retriever.retrieve_all(query_bundle)

# Sort nodes by order
sorted_nodes = sorted(all_nodes, key=lambda node: node.metadata.get('order'))

# Loop over sorted nodes and print text
for node in sorted_nodes:
    print(node.text)


In this example, replace "Your query here" with a query that matches all nodes in your index.

Please note that this will retrieve all nodes that match the query, which might be a large number depending on the size of your index and the specificity of your query. If you only need a subset of the nodes, you might want to use a more specific query.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai I don't have an identifier
Let me check the knowledge base for you. One moment! :books:
----------------------
If you don't have an identifier to order the nodes, it might be challenging to retrieve them in their original order from the document. The order of nodes in the VectorStoreIndex might not match the order in the original document, especially if you're using a splitter that splits the document into non-sequential chunks (like the SentenceSplitter).

One possible solution is to add an identifier when you're creating the nodes. For example, you could add a sequential identifier to each node when you're splitting the document into nodes. Here's an example:

Plain Text
from llama_index.core.node_parser import SentenceSplitter

# Assuming `text` is your text
splitter = SentenceSplitter()
nodes = splitter.get_nodes_from_documents([text])

# Add sequential identifier to each node
for i, node in enumerate(nodes):
    node.metadata['order'] = i


In this example, replace text with your text. The SentenceSplitter class splits the text into nodes, and then a sequential identifier is added to the metadata of each node.

After adding the identifier, you can insert the nodes into the VectorStoreIndex and then retrieve and sort them as shown in the previous example.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai what is the default vector database used in vector store index?
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----------------------
The default vector database used in VectorStoreIndex is the SimpleVectorStore, which is an in-memory vector store. This is used when no other vector store is specified during the construction of the VectorStoreIndex. Here's an example:

Plain Text
from llama_index import VectorStoreIndex, SimpleDirectoryReader

# Load documents and build index
documents = SimpleDirectoryReader(
    "../../examples/data/paul_graham"
).load_data()
index = VectorStoreIndex.from_documents(documents)


In this example, if you don't provide a storage_context parameter, the SimpleVectorStore will be used by default.

For more information, you can refer to the LlamaIndex documentation ().
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@kapa.ai
any help with this error
with open("/content/markdown_files/new_pdf_file/new_pdf_file.md", 'r', encoding='utf-8') as file:
# Read the content of the file
content = file.read()
nodes = parser.split_text(content)
llama_documents = [
Document(text=node.page_content, doc_id=nodes.index(node)) for node in nodes
]nodes = parser.get_nodes_from_documents(llama_documents)
TypeError: object of type 'Document' has no len()
Let me check the knowledge base for you. One moment! :books:
----------------------
The error message "TypeError: object of type 'Document' has no len()" suggests that you're trying to get the length of a Document object, which is not supported because Document objects don't have a length.

Looking at your code, it seems like you're trying to pass a list of Document objects to the get_nodes_from_documents method of your parser. However, this method expects a list of text strings, not Document objects.

Here's how you can fix this:

Plain Text
with open("/content/markdown_files/new_pdf_file/new_pdf_file.md", 'r', encoding='utf-8') as file:
    # Read the content of the file
    content = file.read()

# Split the content into nodes
nodes = parser.split_text(content)

# Convert nodes to text strings
texts = [node.page_content for node in nodes]

# Get nodes from texts
nodes = parser.get_nodes_from_documents(texts)


In this example, I've replaced llama_documents with texts, which is a list of text strings extracted from the nodes. Then, I've passed texts to the get_nodes_from_documents method.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai can we use chat store in llama index with langchain
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----------------------
Yes, you can use the ChatStore in LlamaIndex with LangChain. The ChatStore is a data structure in LlamaIndex that is designed to store and manage chat data. You can use it to store the output of a LangChain LLM and then use LlamaIndex's query engines to query this data.

Here's an example of how you can use a LangChain LLM with a ChatStore:

Plain Text
from llama_index.llms.langchain import LangChainLLM
from llama_index.core import ChatStore
from langchain_openai import ChatOpenAI

# Create a LangChain LLM
llm = LangChainLLM(llm=ChatOpenAI(...))

# Create a ChatStore
chat_store = ChatStore(llm=llm)

# Add a message to the ChatStore
chat_store.add_user_message("Hello, world!")

# Query the ChatStore
response = chat_store.query("What is the meaning of life?")


In this example, replace ... with the parameters for your LangChain LLM.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the chat data includes special characters that are not supported by the ChatStore. You might need to use more advanced text processing techniques to handle these cases.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
can we use chat store in llama index with langchain with a rag app
@kapa.ai can we use chat store in llama index with langchain with a rag app
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can use the ChatStore in LlamaIndex with LangChain in a Retrieval-Augmented Generation (RAG) application. The ChatStore is a data structure in LlamaIndex that is designed to store and manage chat data. You can use it to store the output of a LangChain LLM and then use LlamaIndex's query engines to query this data.

Here's an example of how you can use a LangChain LLM with a ChatStore in a RAG application:

Plain Text
from llama_index.llms.langchain import LangChainLLM
from llama_index.core import ChatStore
from langchain_openai import ChatOpenAI
from llama_index.core import RAG

# Create a LangChain LLM
llm = LangChainLLM(llm=ChatOpenAI(...))

# Create a ChatStore
chat_store = ChatStore(llm=llm)

# Add a message to the ChatStore
chat_store.add_user_message("Hello, world!")

# Create a RAG
rag = RAG(chat_store=chat_store)

# Query the RAG
response = rag.query("What is the meaning of life?")


In this example, replace ... with the parameters for your LangChain LLM.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the chat data includes special characters that are not supported by the ChatStore. You might need to use more advanced text processing techniques to handle these cases.

For more information, you can refer to the LlamaIndex documentation ().
----------------------
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----------------------
can we use chat store in llama index with langchain with a rag app totally utilizing langchain
@kapa.ai can we use chat store in llama index with langchain with a rag app totally utilizing langchain
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, you can use the ChatStore in LlamaIndex with LangChain in a Retrieval-Augmented Generation (RAG) application. However, please note that LlamaIndex and LangChain are separate tools and they need to be integrated properly.

LangChain is used as a language model, while LlamaIndex is used for managing and querying large external knowledge bases. You can use LangChain to generate responses in a chat application, and LlamaIndex to store and retrieve these responses.

Here's a high-level example of how you can use LangChain with LlamaIndex's ChatStore:

Plain Text
from llama_index.llms.langchain import LangChainLLM
from llama_index.core import ChatStore
from langchain_openai import ChatOpenAI

# Create a LangChain LLM
llm = LangChainLLM(llm=ChatOpenAI(...))

# Create a ChatStore
chat_store = ChatStore(llm=llm)

# Add a message to the ChatStore
chat_store.add_user_message("Hello, world!")

# Use LangChain to generate a response
response = llm.complete("What is the meaning of life?")

# Add the response to the ChatStore
chat_store.add_system_message(response)


In this example, replace ... with the parameters for your LangChain LLM.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the chat data includes special characters that are not supported by the ChatStore. You might need to use more advanced text processing techniques to handle these cases.

For more information, you can refer to the LlamaIndex documentation () and the LangChain documentation ().
----------------------
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----------------------
@kapa.ai can you build a rag model utilizing langchain and ollama
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----------------------
Yes, you can build a Retrieval-Augmented Generation (RAG) model utilizing LangChain and Ollama. LangChain can be used as the language model for generating responses, while Ollama can be used for multi-modal tasks.

Here's a high-level example of how you can use LangChain and Ollama with LlamaIndex:

Plain Text
from llama_index.llms.langchain import LangChainLLM
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
from llama_index.core import RAG

# Create a LangChain LLM
llm = LangChainLLM(llm=OllamaMultiModal(model="llava:13b"))

# Create a RAG
rag = RAG(llm=llm)

# Query the RAG
response = rag.query("What is the meaning of life?")


In this example, the LangChainLLM is initialized with an OllamaMultiModal model, which is then used to create a RAG.

Please note that this is a basic example and might not cover all cases. For example, it won't handle cases where the query requires multi-modal input. You might need to use more advanced techniques to handle these cases.

For more information, you can refer to the LlamaIndex documentation () and the LangChain documentation ().
----------------------
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