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Updated 4 months ago

openaiagent with vector store?

openaiagent with vector store?

At a glance

The post asks about using the OpenAIAssistantAgent with a vector store in LlamaIndex. The comments provide a step-by-step guide on how to do this, including importing the necessary modules, loading data, setting up the vector store and index, and creating the agent. The guide includes sample code and instructions to replace placeholders with the user's database details. Community members also mention that there are additional examples available in the LlamaIndex documentation.

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openaiagent with vector store?

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4 comments

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

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Yes, you can use the OpenAIAssistantAgent with a vector store in LlamaIndex. Here's a step-by-step guide on how to do it:

Import the necessary modules:

Plain Text

from llama_index.agent.openai import OpenAIAssistantAgent
from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    StorageContext,
)
from llama_index.vector_stores.supabase import SupabaseVectorStore
from llama_index.core.tools import QueryEngineTool, ToolMetadata

Load your data:

Plain Text

!mkdir -p 'data/10k/'
!wget '<https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf'> -O 'data/10k/uber_2021.pdf'
!wget '<https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf'> -O 'data/10k/lyft_2021.pdf'

# load data
reader = SimpleDirectoryReader(input_files=["./data/10k/lyft_2021.pdf"])
docs = reader.load_data()
for doc in docs:
    doc.id_ = "lyft_docs"

Set up your vector store and index:

Plain Text

vector_store = SupabaseVectorStore(
    postgres_connection_string=(
        "postgresql://<user>:<password>@<host>:<port>/<db_name>"
    ),
    collection_name="base_demo",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(docs, storage_context=storage_context)

Create your agent:

```python
lyft_tool = QueryEngineTool(
query_engine=index.as_query_engine(similarity_top_k=3),
metadata=ToolMetadata(
name="lyft_10k",
description=(
"Provides information about Lyft financials for year 2021. "
"Use a detailed plain text question as input to the tool."
),
),
)

agent = OpenAIAssistantAgent.from_new(
name="SEC Analyst",
instructions="You are a QA assistant designed to analyze SEC filings.",
tools=[lyft_tool],
verbose=True,
run_retrieve_sleep_time=1.0,
)

Plain Text

5. Use your agent to chat:

python
response = agent.chat(
"Tell me about Lyft's risk factors, as well as response to COVID-19"
)
print(str(response))

``
Please replace the placeholders in the

postgres_connection_string` with your actual database details.

For more details, you can refer to the source ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@WhiteFang_Jr @Logan M any examples(not notebook) to use vector db with openaiagent for chat https://docs.llamaindex.ai/en/stable/examples/agent/openai_agent_query_cookbook/...

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