Ollama

Are you done with the first point mentioned in the setup process here: https://docs.llamaindex.ai/en/stable/examples/llm/ollama.html#setup ?

Ollama

Hi I havent done it. will try update it thanks.

Thanks much llm problem is solved now

ValueError: Metadata filters not implemented for Faiss yet. having only issue with FAISS indexer could you please help with that?

Seems like you are trying metadata filtering which is not present with this index.

ah ok got it. just looking for open source indexer. Do you have any suggestion?

for metadata indexing

You can try LlamaIndex VectorStoreIndex, You can do metadata filtering with that.

https://docs.llamaindex.ai/en/stable/module_guides/indexing/metadata_extraction.html

North remembers 🥶
Lol 😆

😀

Hi using ollama LLM .

llm = Ollama(model="mistral", request_timeout=30.0)

service_context = ServiceContext.from_defaults(
llm=llm, callback_manager=callback_manager, chunk_size=256,
embed_model="local")

*

retriever = VectorIndexAutoRetriever(
index,
vector_store_info=vector_store_info,
service_context=service_context,
max_top_k=10000,
)

query_engine = RetrieverQueryEngine.from_args(retriever,llm=llm)

the problem is still the LLM resolver is looking for open ai keys. when i checked the resolver function it is checking for lang chain and open ai and not directly the ollama.


File ~/anaconda3/lib/python3.11/site-packages/llama_index/llms/utils.py:31 in resolve_llm
raise ValueError(

ValueError:
**
Could not load OpenAI model. If you intended to use OpenAI, please check your OPENAI_API_KEY.
Original error:
No API key found for OpenAI.
Please set either the OPENAI_API_KEY environment variable or openai.api_key prior to initialization.
API keys can be found or created at https://platform.openai.com/account/api-keys

To disable the LLM entirely, set llm=None.
**

Could you please help wether the issue is from my code ? is there documents available for using ollama. Thanks .

Try setting the service_context global:

perfect , works . Thank you

one more question please

dataset_path ="data/deeplake"

vector_store = DeepLakeVectorStore(dataset_path=dataset_path, overwrite=True)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(
[], storage_context=storage_context, service_context=service_context
)

add documents to index

for wiki_title in wiki_titles:
index.insert(docs_dict[wiki_title])

is it possible to index one time and save it and call it whenerver needed? is there any document available please?

I think, once you ingest the docs then you dont have to ingest it again in case of third party vector store like Deeplake vector store

program would b closed several times, each time if the number of documents is in millions it will take time to ingest, is there any possible way to save the ingested index adn load it wheneber necessary?