how do i specify my choice of llm in the followingvecto...

bbhrdwj

@kapa.ai how do i specify my choice of llm in the following
vector_store = PineconeVectorStore(pinecone_index=pinecone_index, add_sparse_vector=True)
pinecone_vectorstore_index = VectorStoreIndex.from_vector_store(vector_store = vector_store)

print('case id', request.case_id)

retriever = pinecone_vectorstore_index.as_retriever(
# similarity_top_k = 5,
# metadata_filters={"case_id": request.case_id}
vector_store_kwargs={"filter": {"case_id": request.case_id}}
)
response_synthesizer = get_response_synthesizer(response_mode="refine")

query_engine = RetrieverQueryEngine(
retriever=retriever,
response_synthesizer=response_synthesizer
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": custom_prompt}
)
response = query_engine.query(request.query)

return {"response": response.response}

13 comments

kkapa.ai

Please stand by @bhrdwj as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

----------------------
To specify your choice of Language Learning Model (LLM) in LlamaIndex, you can set it in the Settings object. Here's an example of how to do it using the OpenAI LLM:

Plain Text

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

Settings.llm = OpenAI(temperature=0.2, model="gpt-4")

In this example, the OpenAI LLM is being used, and it's being configured with a temperature of 0.2 and the model "gpt-4". You can replace OpenAI with your choice of LLM and adjust the parameters as needed.

After setting the LLM in Settings, it will be used in the subsequent stages of your pipeline, including the RetrieverQueryEngine and response_synthesizer.

Here's how you can incorporate it into your existing code:

Plain Text

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

# Specify your choice of LLM
Settings.llm = OpenAI(temperature=0.2, model="gpt-4")

vector_store = PineconeVectorStore(pinecone_index=pinecone_index, add_sparse_vector=True)
pinecone_vectorstore_index = VectorStoreIndex.from_vector_store(vector_store = vector_store)

print('case id', request.case_id)

retriever = pinecone_vectorstore_index.as_retriever(
    vector_store_kwargs={"filter": {"case_id": request.case_id}}
)
response_synthesizer = get_response_synthesizer(response_mode="refine")

query_engine = RetrieverQueryEngine(
        retriever=retriever,
        response_synthesizer=response_synthesizer
)
query_engine.update_prompts(
    {"response_synthesizer:text_qa_template": custom_prompt}
)
response = query_engine.query(request.query)

return {"response": response.response}

Please note that the choice of LLM and its parameters should be chosen based on your specific use case and requirements.

kkapa.ai

For more information, you can refer to the LlamaIndex documentation on using LLMs ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbhrdwj

@kapa.ai What is the best way to load data present in S3 bucket and read text from them?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
To load data from an S3 bucket and read text from them, you can use the SimpleDirectoryReader or S3Reader provided by LlamaIndex. Here's a step-by-step guide on how to do it:

Using SimpleDirectoryReader:

First, you need to create an instance of S3FileSystem and then use it with SimpleDirectoryReader to load documents from an S3 bucket.

Plain Text

from s3fs import S3FileSystem
from llama_index.readers import SimpleDirectoryReader

s3_fs = S3FileSystem(anon=False, endpoint_url=endpoint_url)

reader = SimpleDirectoryReader(
    input_dir=bucket_name,
    fs=s3_fs,
    recursive=True,  # recursively searches all subdirectories
)

docs = reader.load_data()
print(f"Loaded {len(docs)} docs")

In this example, bucket_name is the name of your S3 bucket and endpoint_url is the URL of your S3 endpoint. The load_data() method loads the documents from the S3 bucket (source ()).

Using S3Reader:

Alternatively, you can use the S3Reader class in LlamaIndex, which is specifically designed for reading data from S3. Here's an example:

Plain Text

from llama_index.readers.s3 import S3Reader

reader = S3Reader(
    aws_access_id=aws_access_id,
    aws_access_secret=aws_access_secret,
    s3_endpoint_url=s3_endpoint_url,
    bucket=bucket_name,
)

docs = reader.load_data()

In this example, aws_access_id and aws_access_secret are your AWS credentials, s3_endpoint_url is the URL of your S3 endpoint, and bucket_name is the name of your S3 bucket. The load_data() method loads the documents from the S3 bucket (source ()).

kkapa.ai

Please replace the placeholders with your actual values. Also, ensure that you have the necessary permissions to access the S3 bucket.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbhrdwj

@kapa.ai got this error when i used S3Reader
pydantic.v1.error_wrappers.ValidationError: 1 validation error for S3Reader
bucket
field required (type=value_error.missing)

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
The error message you're seeing is indicating that the bucket field is required when creating an instance of S3Reader. This field should contain the name of your S3 bucket.

Here's how you can specify it:

Plain Text

from llama_index.readers.s3 import S3Reader

reader = S3Reader(
    aws_access_id=aws_access_id,
    aws_access_secret=aws_access_secret,
    s3_endpoint_url=s3_endpoint_url,
    bucket='your_bucket_name',  # replace with your actual bucket name
)

In this example, replace 'your_bucket_name' with the name of your S3 bucket. Also, replace aws_access_id, aws_access_secret, and s3_endpoint_url with your actual AWS credentials and S3 endpoint URL.

For more details, you can refer to the LlamaIndex documentation on S3Reader ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbhrdwj

@kapa.ai @Logan M what is best way to read documents from S3 bucket as llamaindex documents?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
The best way to read documents from an S3 bucket as LlamaIndex documents is to use the S3Reader class provided by LlamaIndex. Here's how you can do it:

Plain Text

from llama_index.readers.s3 import S3Reader

reader = S3Reader(
    aws_access_id='your_aws_access_id',  # replace with your actual AWS access id
    aws_access_secret='your_aws_access_secret',  # replace with your actual AWS access secret
    s3_endpoint_url='your_s3_endpoint_url',  # replace with your actual S3 endpoint URL
    bucket='your_bucket_name',  # replace with your actual bucket name
)

docs = reader.load_s3_files_as_docs()

In this example, replace 'your_aws_access_id', 'your_aws_access_secret', 'your_s3_endpoint_url', and 'your_bucket_name' with your actual AWS credentials, S3 endpoint URL, and bucket name. The load_s3_files_as_docs() method loads the documents from the S3 bucket.

Please ensure that you have the necessary permissions to access the S3 bucket.

For more details, you can refer to the LlamaIndex documentation on S3Reader ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

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how do i specify my choice of llm in the followingvector_store = PineconeVectorStore(pine