VectorShift

Although you can use any supported file type or data in any supported source type for the input into Pinecone, this demonstration uses the text of the United States Constitution in PDF format.

1. Sign in to your Unstructured account.
2. Create a source connector, if you do not already have one, to connect Unstructured to the source location where the PDF file is stored.
3. Create a Pinecone destination connector, if you do not already have one, to connect Unstructured to your Pinecone serverless index.
4. Create a workflow that references this source connector and destination connector.
5. Run the workflow.

1. Sign in to your VectorShift account dashboard.
2. On the sidebar, click Pipelines.
3. Click New.
4. Click Create Pipeline from Scratch.

In this step, you add a node to the pipeline. This node takes user-supplied chat messages and sends them as input to Pinecone, and as input to a text-based LLM, for contextual searching.

In the top pipeline node chooser bar, on the General tab, click Input.

In this step, you add a node that connects to the Pinecone serverless index.

1. In the top pipeline node chooser bar, on the Integrations tab, click Pinecone.

2. In the Pinecone node, for Embedding Model, select openai/text-embedding-3-large.

3. Click Connected Account.

4. In the Select Pinecone Account dialog, click Connect New.

5. Enter the API Key and Region for your Pinecone serverless index, and then click Save.

6. For Index, selet the name of your Pinecone serverless index.

7. Connect the input_1 output from the Input node to the query input in the Pinecone node.

   To make the connection, click and hold your mouse pointer inside of the circle next to input_1 in the Input node. While holding your mouse pointer, drag it over into the circle next to query in the Pinecone node. Then release your mouse pointer. A line appears between these two circles.

In this step, you add a node that builds a prompt and then sends it to a text-based LLM.

1. In the top pipeline node chooser bar, on the LLMs tab, click OpenAI.

2. In the OpenAI LLM node, for System, enter the following text:

   Answer the Question based on Context. Use Memory when relevant.
   

   To answer the question, the preceding prompt uses the context along with general information that the text-based LLM is trained on. To use only the context to answer the question, you can change the prompt, for example to something like this:

   Answer the Question based only on the Context. Do not use any other sources of
   information. If the context does not provide enough information to answer the 
   question, reply with 'I do not have enough context to answer the question.' 
   Use Memory when relevant.
   

3. For Prompt, enter the following text:

   Question: {{Question}}
   Context: {{Context}}
   Memory: {{Memory}}
   

4. For Model, select gpt-4o-mini.

5. Check the box titled Use Personal API Key.

6. For API Key, enter your OpenAI API key.

7. Connect the input_1 output from the Input node to the Question input in the OpenAI LLM node.

8. Connect the output output from the Pinecone node to the Context input in the OpenAI LLM node.

In this step, you add a node that adds chat memory to the session.

1. In the top pipeline node chooser bar, on the Chat tab, click Chat Memory.
2. Connect the output from the Chat Memory node to the Memory input in the OpenAI LLM node.

In this step, you add a node that displays the chat output.

1. In the top pipeline node chooser bar, on the General tab, click Output.
2. Connect the response output from the OpenAI LLM node to the input in the Output node.

1. In the upper corner of the pipeline designer, click the play (Run Pipeline) button.

2. In the chat pane, on the Chatbot tab, enter a question into the Message Assistant box, for example, What rights does the fifth amendment guarantee? Then press the send button.

3. Wait until the answer appears.

4. Ask as many additional questions as you want to.