How Can I Upsert Freshdesk Ticket Data into Pinecone and Query It Using Sentence Transformers?

[carterlasalle]

I am trying to upsert Freshdesk ticket data into Pinecone and then query that data. Despite trying multiple approaches, I'm still struggling with what seems to be a simple task. I'd appreciate any guidance on what might be going wrong.

Upserting Data

I have the following code to upsert Freshdesk ticket data into Pinecone:

import os
import json
import pinecone
from sentence_transformers import SentenceTransformer
import numpy as np
import logging
import time
from requests.exceptions import SSLError

logging.basicConfig(level=logging.INFO)

def initialize_pinecone(api_key, environment, index_name, dimension):
    pinecone.init(api_key=api_key, environment=environment)
    if index_name not in pinecone.list_indexes():
        pinecone.create_index(index_name, dimension=dimension)

def load_model(model_name, device='cuda'):
    model = SentenceTransformer(model_name)
    return model.to(device)

def upsert_batches(index, vectors, index_name, batch_size=100, max_retries=5, sleep_time=0.5):
    for i in range(0, len(vectors), batch_size):
        batch = vectors[i:i + batch_size]
        retries = 0

        while retries <= max_retries:
            try:
                index.upsert(batch, namespace=index_name)
                logging.info(f"Upserted batch {i // batch_size + 1} of {len(vectors) // batch_size}")
                break  # Success, exit the retry loop
            except SSLError as e:
                retries += 1
                logging.warning(f"SSL error on batch {i // batch_size + 1}. Retrying {retries}/{max_retries}. Error: {e}")
                time.sleep(sleep_time * (2 ** retries))  # Exponential backoff
        else:
            logging.error(f"Max retries exceeded for batch {i // batch_size + 1}. Skipping.")

        time.sleep(sleep_time)  # Sleep between batches

def process_tickets(directory, text_model, meta_model, index_name):
    ticket_files = os.listdir(directory)
    vectors = []

    for file in ticket_files:
        file_path = os.path.join(directory, file)
        try:
            with open(file_path) as f:
                data = f.read().replace('\u0000', '')
                tickets = json.loads(data)
                text_fields = [t['helpdesk_ticket']['description'] for t in tickets]
                meta_fields = [t['helpdesk_ticket']['subject'] for t in tickets]

                # Encode into embeddings
                text_embeds = np.asarray(text_model.encode(text_fields)).tolist()
                meta_embeds = np.asarray(meta_model.encode(meta_fields)).tolist()

                # Prepare vectors
                for text_embed, meta_embed, ticket in zip(text_embeds, meta_embeds, tickets):
                    vectors.extend([
                        {'id': str(ticket['helpdesk_ticket']['id']) + '_text', 'values': text_embed},
                        {'id': str(ticket['helpdesk_ticket']['id']) + '_meta', 'values': meta_embed}
                    ])

        except Exception as e:
            line, column = e.lineno, e.colno
            logging.error(f"JSON parse error at line {line} column {column}")

    index = pinecone.Index(index_name)
    upsert_batches(index, vectors, index_name)  # Replace the original upsert call with this

API_KEY = "KEY"
ENVIRONMENT = "ENV"
INDEX_NAME = 'index'
DIMENSION = 768

initialize_pinecone(API_KEY, ENVIRONMENT, INDEX_NAME, DIMENSION)
text_model = load_model('multi-qa-mpnet-base-cos-v1')
meta_model = load_model('multi-qa-mpnet-base-cos-v1')
process_tickets('drive/MyDrive/ticketsall', text_model, meta_model, INDEX_NAME)

Querying Data

Here is the code to query the ticket data:

import os
import pinecone
import logging
from sentence_transformers import SentenceTransformer
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

logging.basicConfig(level=logging.INFO)

def initialize_models():
    query_model = SentenceTransformer('multi-qa-mpnet-base-cos-v1')
    return query_model

def format_ticket_context(fetched_tickets):
    # Assuming fetched_tickets contains the required ticket information
    return '\n'.join([f"Ticket ID: {t['id']}\nDescription: {t['description']}\nSubject: {t['subject']}" for t in fetched_tickets])

def query_tickets(question, index_name):
    question_embed = query_model.encode([question])
    results = index.query(queries=question_embed.tolist(), top_k=5, namespace=index_name)

    logging.info("Query results:", results)

    if results.get('matches'):
        ticket_ids = [r['id'].split('_')[0] for r in results['matches']]
        fetched = index.fetch(ids=ticket_ids, namespace=index_name)
        return format_ticket_context(fetched)
    else:
        return "No matching tickets found."

# Download GPT4All's Nous-Hermes2 model
local_path = "./models/ggml-gpt4all-l13b-snoozy.bin"
if not os.path.exists(local_path):
    import requests
    from pathlib import Path
    from tqdm import tqdm
    Path(local_path).parent.mkdir(parents=True, exist_ok=True)
    url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'
    response = requests.get(url, stream=True)
    with open(local_path, 'wb') as f:
        for chunk in tqdm(response.iter_content(chunk_size=8192)):
            if chunk:
                f.write(chunk)

# Initialize LLM with GPT4All
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, verbose=True)

# Define PromptTemplate
template = """You are an AI specialized in providing insights based on ticket data. Below is the context from similar tickets: {context} Here's the question: {question} Please provide the best response:"""
prompt = PromptTemplate(template=template, input_variables=["context", "question"])

llm_chain = LLMChain(prompt=prompt, llm=llm)

index = pinecone.Index('index')
query_model = initialize_models()

question = "What happens in the ticket that mentions AutoZone?"
context = query_tickets(question, 'index')

final_response = llm_chain.run(context=context, question=question)

print(final_response)

I am not receiving any errors currently, but the code doesn't perform as expected. Can anyone help me understand what might be wrong? Thank you!

I looked through the official Pinecone and Freshdesk documentation to find relevant examples and guides for upserting and querying the data. Additionally, I consulted tutorials and online forums that provided insights into similar tasks. I even sought assistance from GPT-4 to generate code snippets and get an understanding of potential solutions. I expected that by following these resources, I would be able to successfully upsert Freshdesk ticket data into Pinecone and then query that data according to my requirements. However, despite trying these multiple approaches, the code didn't perform as expected, and I am still struggling to achieve the desired result.

[dosubot[bot]]

🤖

Hello,

Thank you for providing detailed information about your issue. From what I understand, you're trying to upsert Freshdesk ticket data into Pinecone and then query that data using Sentence Transformers.

Looking at your code, it seems like you're correctly initializing Pinecone, loading the SentenceTransformer model, processing the tickets, and upserting the data into Pinecone. However, without specific details about what's not working as expected, it's a bit challenging to provide a precise solution.

Here are a few things you might want to check:

1. Data Processing: Ensure that the data you're processing from Freshdesk is correctly formatted and that the fields you're trying to access (description, subject, etc.) exist in the data. Also, make sure that the JSON files are correctly formatted and do not contain any errors that might cause the JSON parsing to fail.

2. Model Encoding: Check that the SentenceTransformer model is correctly encoding the text and meta fields into embeddings. You can do this by printing out the embeddings after the encoding step and ensuring they're not empty or filled with NaNs.

3. Upserting Data: Make sure that the data is being upserted into Pinecone without any errors. You can do this by checking the logs for any SSL errors or other exceptions that might occur during the upserting process.

4. Querying Data: Ensure that the query model is correctly encoding the question into an embedding and that the query to Pinecone is returning results. You can do this by printing out the results after the query step and checking if they contain any matches.

5. LLMChain: Make sure that the LLMChain is correctly initialized and that the prompt template is correctly formatted. Also, ensure that the GPT4All model is correctly downloaded and loaded into the LLM.

If you've checked all of these and are still facing issues, could you please provide more specific details about what's not working as expected? Are there any error messages being printed out? Is the data not being upserted correctly, or are the queries not returning the expected results? Any additional information would be helpful in diagnosing the issue.

Best,
Dosu

Sources

• libs/langchain/langchain/vectorstores/pinecone.py
• libs/langchain/langchain/embeddings/sentence_transformer.py

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