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To feed new text content to Ollama using LlamaIndex, follow these steps:
This approach allows you to integrate and use Ollama with LlamaIndex effectively for generating text based on new content. For more details, refer to the source code.
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@dosu i need to put new content |
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To directly feed new text content into Ollama for processing, you can use the from llama_index.llms.ollama import Ollama
# Initialize Ollama with your chosen model
llm = Ollama(model="llama2", request_timeout=60.0)
# For processing a single piece of text content
response = llm.complete("Your new text content here")
print(response.text) # Prints the processed text
# For processing text content in a streaming manner
for completion_response in llm.stream_complete("Your new text content here"):
print(completion_response.text) # Prints processed text as it's receivedJust replace Both methods allow you to include additional parameters as needed, which are sent along with the request to Ollama's API. This flexibility lets you adjust the behavior of the text generation or processing according to your specific needs.
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@dosu i mean i have an txt file i want to feed some content |
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To feed content from a text file into Ollama, you can follow this approach: from llama_index.llms.ollama import Ollama
# Initialize Ollama with your chosen model
ollama = Ollama(model="llama2", request_timeout=60.0)
# Read text from your file
file_path = 'path/to/your/file.txt' # Replace with your file's path
with open(file_path, 'r') as file:
text_content = file.read()
# Process the text content
response = ollama.complete(text_content)
print(response.text) # This will print the processed textMake sure to replace
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To work with embeddings in Ollama using LlamaIndex for the content of your text file, ensuring it can "remember" the content for future queries, you'll need to follow a specific approach. This involves using the Ollama model to generate embeddings for your text, which can then be indexed or stored in a way that allows for efficient retrieval and querying based on those embeddings. Here's a simplified guide to get you started:
Unfortunately, the conversation doesn't provide specific code examples for steps 2 to 4, as these steps depend heavily on the specific capabilities of Ollama and LlamaIndex, as well as the infrastructure you have in place for indexing and querying embeddings. For detailed implementation, you would need to refer to the documentation of Ollama and LlamaIndex or any provided API references that detail how to generate embeddings and work with them for querying purposes.
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i cannot understand how can i feed new text content to ollama properly
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