I'm using openai assistant and I want to use same vector id and assistant id always. How can I do ?
#I'm using openai assistant and I want to use same vector id and assistant id always. How can I do ?
median ibex
pine charmBOT
Hi @median ibex. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
You can use the same Vector Store ID and Assistant ID for every user session by passing these IDs when you create or update your assistant. Here's how you can do it:
- Create or Update the Assistant: You can create a new assistant or update an existing one using the OpenAI API. When you do this, you can specify the Vector Store ID in the
tool_resourcesparameter. Here's an example of how to do this in Python:
assistant = client.beta.assistants.update(
assistant_id=assistant.id,
tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
)
In this code, assistant.id is the ID of the assistant you want to update, and vector_store.id is the ID of the Vector Store you want to use. If you're creating a new assistant, you would use the client.beta.assistants.create method instead of client.beta.assistants.update.
- Use the Assistant ID and Vector Store ID: Once you've created or updated the assistant with the Vector Store ID, you can use the same Assistant ID and Vector Store ID for every user session. You just need to pass these IDs when you create a new thread or run a new session.
Remember, if the files for which the session is starting are the same all the time, pass the Vector Store ID via the Assistant. If the file changes for each session, pass it via the thread.
For more information, you can refer to the OpenAI documentation (https://platform.openai.com/docs/assistants/tools/file-search) and the OpenAI Community Forum (https://community.openai.com/t/779350).
Please note that you need to replace assistant.id and vector_store.id with your actual Assistant ID and Vector Store ID.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
median ibex
I need extra code example @pine charm
pine charmBOT
Let me check the knowledge base for you. One moment! :books:
Sure, here are some additional code examples for creating an assistant and a thread with a vector store:
Creating an Assistant with a Vector Store
assistant = client.beta.assistants.create(
instructions="You are a helpful product support assistant and you answer questions based on the files provided to you.",
model="gpt-4o",
tools=[{"type": "file_search"}],
tool_resources={
"file_search": {
"vector_store_ids": ["vs_1"]
}
}
)
In this example, replace "vs_1" with your actual Vector Store ID.
Creating a Thread with a Vector Store
thread = client.beta.threads.create(
messages=[ { "role": "user", "content": "How do I cancel my subscription?"} ],
tool_resources={
"file_search": {
"vector_store_ids": ["vs_2"]
}
}
)
In this example, replace "vs_2" with your actual Vector Store ID.
These examples are based on the OpenAI documentation (https://platform.openai.com/docs/assistants/tools/file-search).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
median ibex
assistant = client.beta.assistants.create(
name="Q&A Assistant on user folders",
instructions=("""
You are an expert research assistant.
You need to find answers to the questions asked by the user with the highest accuracy rate and return the answers to the user.
"""),
tools=[{"type": "file_search"}],
tool_resources={"file_search": {"vector_store_ids": ["vs_eChsjONiC5UmqqJyFkW1GWKW"]}},
model="gpt-4o",
)
vector_store = client.beta.vector_stores.create(name="Product Statements")
directories = ["./company_search/", "./uploaded_files/"]
file_paths = []
for directory in directories:
all_files = glob.glob(os.path.join(directory, "*.*"))
file_paths.extend(all_files)
file_streams = [open(file_path, "rb") for file_path in file_paths]
file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
vector_store_id="vs_eChsjONiC5UmqqJyFkW1GWKW", files=file_streams
)
print('*'*10)
print("Vector ID: ",vector_store.id)
print('*'*10)
assistant = client.beta.assistants.update(
assistant_id=assistant.id,
tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
)
thread = client.beta.threads.create(
messages=[
{
"role": "user",
"content": question
}
]
)
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id, assistant_id=assistant.id
)```
Can you write on this code @pine charm assistant = client.beta.assistants.create(
name="Q&A Assistant on user folders",
instructions=("""
You are an expert research assistant.
You need to find answers to the questions asked by the user with the highest accuracy rate and return the answers to the user.
"""),
tools=[{"type": "file_search"}],
tool_resources={"file_search": {"vector_store_ids": ["vs_eChsjONiC5UmqqJyFkW1GWKW"]}},
model="gpt-4o",
)
vector_store = client.beta.vector_stores.create(name="Product Statements")
directories = ["./company_search/", "./uploaded_files/"]
file_paths = []
for directory in directories:
all_files = glob.glob(os.path.join(directory, "*.*"))
file_paths.extend(all_files)
file_streams = [open(file_path, "rb") for file_path in file_paths]
file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
vector_store_id="vs_eChsjONiC5UmqqJyFkW1GWKW", files=file_streams
)
print('*'*10)
print("Vector ID: ",vector_store.id)
print('*'*10)
assistant = client.beta.assistants.update(
assistant_id=assistant.id,
tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
)
thread = client.beta.threads.create(
messages=[
{
"role": "user",
"content": question
}
]
)
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id, assistant_id=assistant.id
)```
Can you write on this code @pine charmLet me check the knowledge base for you. One moment! :books:
The code you provided is a Python script that creates an OpenAI assistant, a vector store, and a thread. It also uploads files to the vector store and updates the assistant with the vector store ID. Here's a breakdown of what each part of the code does:
- Create an Assistant: The
client.beta.assistants.createmethod is used to create a new assistant. The assistant is given a name, instructions, tools (in this case, file search), a vector store ID, and a model.
assistant = client.beta.assistants.create(
name="Q&A Assistant on user folders",
instructions=("""
You are an expert research assistant.
You need to find answers to the questions asked by the user with the highest accuracy rate and return the answers to the user.
"""),
tools=[{"type": "file_search"}],
tool_resources={"file_search": {"vector_store_ids": ["vs_eChsjONiC5UmqqJyFkW1GWKW"]}},
model="gpt-4o",
)
- Create a Vector Store: The
client.beta.vector_stores.createmethod is used to create a new vector store.
vector_store = client.beta.vector_stores.create(name="Product Statements")
- Upload Files to Vector Store: The script then finds all files in the specified directories and uploads them to the vector store.
directories = ["./company_search/", "./uploaded_files/"]
file_paths = []
for directory in directories:
all_files = glob.glob(os.path.join(directory, "*.*"))
file_paths.extend(all_files)
file_streams = [open(file_path, "rb") for file_path in file_paths]
file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
vector_store_id="vs_eChsjONiC5UmqqJyFkW1GWKW", files=file_streams
)
- Update the Assistant: The
client.beta.assistants.updatemethod is used to update the assistant with the vector store ID.
assistant = client.beta.assistants.update(
assistant_id=assistant.id,
tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
)
- Create a Thread: The
client.beta.threads.createmethod is used to create a new thread with a user message.
thread = client.beta.threads.create(
messages=[
{
"role": "user",
"content": question
}
]
)
- Run the Thread: The
client.beta.threads.runs.create_and_pollmethod is used to run the thread and wait for the result.
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id, assistant_id=assistant.id
)