Hello @hallow gulch
If i create a new Assistant API to be used in my app.
And let's say for each user, when they need to engage in this app, depending on the type of the engagement, i want this Assistant API to use a specific vector store. We have around 300 vector stores, and one of them should be used.
When we prepare the input prompt (based on what the user entered), can we specify which vector store for the assistant api to use (based on the user engagement type) ? If yes, how can we do this code-wise?
#File search
jade quarry
hallow gulchBOT
Hi @jade quarry. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
Yes, you can specify which vector store to use for each user session. You can pass your Vector Store IDs either one time in the Assistant or while creating a Thread. If the files for which the session is starting are the same all the time, pass it via Assistant. If the file changes for each session, pass it via thread. This is mentioned in the OpenAI community forum post (https://community.openai.com/t/779350).
However, the exact code to do this is not provided in the knowledge sources. You would typically do this by setting the vector_store_id parameter when creating an Assistant or a Thread, but the exact method would depend on the programming language and the specific OpenAI SDK you are using.
For more detailed instructions, you should refer to the OpenAI API documentation (https://platform.openai.com/docs/).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
Let me check the knowledge base for you. One moment! :books:
The efficiency and cost-effectiveness of the two implementations would depend on several factors, including the specific use case, the complexity of the questions and answers, and the number of tokens used in each approach.
Implementation #1 (Text Input): This approach might be more straightforward and easier to implement, as you're directly providing the questions and answers as text input. However, it could potentially use more tokens, especially if the text input is long (around 1,600 words as you mentioned). The cost of using the Assistant API is based on the number of tokens processed, so a longer text input could result in higher costs.
Implementation #2 (Vector Store): This approach might be more efficient in terms of token usage, especially if the questions and answers are stored in a compact form in the vector store. One user in the OpenAI community forum (https://community.openai.com/t/783317) reported that their token usage was minimized from an average of 10k (on playground with vector store) to just 1k-2k with embedding. However, this approach could be more complex to implement, as it involves creating and managing vector stores and using the embedding API.
In terms of power and context, both approaches should be able to provide the Assistant API with the necessary context for the interview process. However, the vector store approach might provide more flexibility, as it allows the assistant to dynamically fetch and process questions and answers from the vector store.
Ultimately, the best approach would depend on your specific requirements and constraints. You might want to experiment with both approaches and see which one works best for your use case.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
jade quarry
@hallow gulch How can i specify at thread creation level, which vector store to use as part of the assistant's answers?
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
When creating a thread, you can specify the vector store to use by setting the vector_store_id parameter. However, the exact code to do this is not provided in the knowledge sources. Typically, you would do this when creating the thread, and the method would depend on the programming language and the specific OpenAI SDK you are using.
For example, in Python, you might do something like this:
thread = openai.Thread.create(vector_store_id="your_vector_store_id")
Please note that this is a hypothetical example and may not work as-is. For more detailed instructions, you should refer to the OpenAI API documentation (https://platform.openai.com/docs/).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
In the knowledge sources provided, there isn't a specific example of how to set the vector_store_id when creating a thread in TypeScript. However, typically in TypeScript, you would use the OpenAI SDK's method for creating a thread and pass in the vector_store_id as a parameter.
Here's a hypothetical example:
let thread = openai.Thread.create({
vector_store_id: "your_vector_store_id"
});
Please note that this is a hypothetical example and may not work as-is. For more detailed instructions, you should refer to the OpenAI API documentation (https://platform.openai.com/docs/) or the specific documentation for the OpenAI SDK you are using.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
jade quarry
@hallow gulch
If i put some files in the Vector Store, Can i make the Assistant give these files to a user?
For example if a user asks for a file, is it possible for the assistant to give them this file?
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
Based on the knowledge sources provided, the Assistant API can retrieve and use information from files in a vector store to generate responses. However, it doesn't directly send or provide these files to the user.
When a user asks a question, the Assistant can use the information in the vector store to generate a relevant response. For example, if a user asks a question about the content of a document in the vector store, the Assistant can retrieve the relevant information from the document and use it to answer the user's question.
If a user asks for a file, the Assistant could potentially provide information from the file in its response. However, it wouldn't directly send the file to the user. The actual file delivery would need to be handled separately, outside of the Assistant API.
For more detailed information on how the Assistant API uses vector stores, you can refer to the OpenAI API documentation (https://platform.openai.com/docs/).
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
jade quarry
@hallow gulch How can i imlpement this actual file delivery?
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
The actual file delivery to the user would typically be handled by your application's backend server and would depend on the specific requirements of your application. Here's a general approach:
-
Store the file: When a file is uploaded to a vector store, you could also store it in a secure location that your application's server can access. This could be a database, a cloud storage service like AWS S3 or Azure Blob Storage, or your server's local file system.
-
Generate a secure link: When a user requests a file, your server can generate a secure, temporary link to the file. This could be a pre-signed URL if you're using a cloud storage service, or a URL that points to a route on your server that can serve the file.
-
Serve the file: When the user accesses the link, your server should authenticate the request, ensure the user has permission to access the file, and then serve the file to the user. This could involve setting the appropriate HTTP headers and then streaming the file data.
Here's a hypothetical example of how you might generate a pre-signed URL with Azure Blob Storage, based on the knowledge source (https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_action_snowflake_middleware#storing-the-file-in-blob-storage):
from azure.storage.blob import BlobServiceClient, BlobClient, BlobSasPermissions, generate_blob_sasdef generate_secure_link(blob_service_client: BlobServiceClient, container_name: str, blob_name: str):
blob_client = blob_service_client.get_blob_client(container_name, blob_name)
sas_token = generate_blob_sas(
blob_service_client.account_name,
container_name,
blob_name,
account_key=blob_service_client.credential.account_key,
permission=BlobSasPermissions(read=True),
expiry=datetime.utcnow() + timedelta(hours=1)
)
return f"<https://{blob_service_client.account_name}.blob.core.windows.net/{container_name}/{blob_name}?{sas_token}">
Please note that this is a hypothetical example and may not work as-is. You should refer to the documentation for your chosen storage service for more detailed instructions.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------@hallow gulch I see, but how can i make the process as follows:
The user asks for info, the assistant finds the file in the vector store, and the file or a link to this file is now sent back to the user. How can we implement this?
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
Based on the knowledge sources provided, the Assistant API can retrieve and use information from files in a vector store to generate responses. However, it doesn't directly send or provide these files to the user.
If you want to provide a file or a link to a file to the user, you would need to implement this functionality in your application. Here's a general approach:
-
Store the file: When a file is uploaded to a vector store, you could also store it in a secure location that your application's server can access. This could be a database, a cloud storage service like AWS S3 or Azure Blob Storage, or your server's local file system.
-
Associate the file with a vector store: You would need to keep track of which files are associated with which vector stores. This could be done by storing this information in a database.
-
Generate a secure link: When the Assistant identifies a file in the vector store that should be provided to the user, your server can generate a secure, temporary link to the file. This could be a pre-signed URL if you're using a cloud storage service, or a URL that points to a route on your server that can serve the file.
-
Provide the link to the user: The Assistant can include the secure link in its response to the user. The user can then use this link to download the file.
Please note that this is a general approach and the exact implementation would depend on the specific requirements of your application and the technologies you are using. For more detailed instructions, you should refer to the documentation for your chosen storage service and the OpenAI API.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
jade quarry
@hallow gulch How will this assistant get the link from my server?
hallow gulchBOT
Let me check the knowledge base for you. One moment! :books:
The Assistant API itself doesn't directly interact with your server to get the link. Instead, your application would need to handle this process. Here's a general approach:
-
User makes a request: The user asks the Assistant for a file.
-
Assistant identifies the file: The Assistant identifies the file in the vector store that should be provided to the user.
-
Your application retrieves the link: Your application, upon receiving the Assistant's response, identifies the file that needs to be provided to the user. It then communicates with your server to generate a secure link for this file.
-
Your application sends the link to the user: Your application includes the secure link in its response to the user. The user can then use this link to download the file.
This process would require your application to handle the communication between the Assistant API, your server, and the user. The exact implementation would depend on the specific requirements of your application and the technologies you are using.