# Relevant query to a single-document Index returns zero results **URL:** \ **Category:** Support\ **Created:** [December 16, 2024, 7:15pm UTC](https://community.pinecone.io/t/relevant-query-to-a-single-document-index-returns-zero-results/7179 "2024-12-16T19:15:00Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![nmvega](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/nmvega/32/3650_2.png) [@nmvega](https://community.pinecone.io/u/nmvega)\ **Post date:** [December 16, 2024, 7:15pm UTC](https://community.pinecone.io/t/relevant-query-to-a-single-document-index-returns-zero-results/7179/1 "2024-12-16T19:15:00Z")
Hello Friends: I’m newbie to **Pinecone** so your help is greatly appreciated. Here is my issue details: 1. Via the **WebUI** , I manually created the following **index** , specifying the **text-embedding-3-large** model: **my-test-index** 2. Then, using the **first Python script** below, I successfully uploaded a `./FAQ.txt` document to it. _It is the only document in the index, and it’s contents appears at the very bottom of this post_. 3. Finally, using the **second Python script** below, I queried the index with **variations** of the text: _ **What classes are offered at your facility?** _ The issue I’m facing is that I always receive this response: ```auto {'matches': [{'id': 'DOC0001', 'score': 0.576828599, 'values': []}], 'namespace': 'namespace01', 'usage': {'read_units': 6}} ``` Naturally, I’m missing something and would appreciate your guidance. Thank you in advance. 😊 **(1) Document upsert Python script**: ```auto #! /usr/bin/env python3 import openai from pinecone import Pinecone, ServerlessSpec # ---------------------------------------------------------------------------------- openai.api_key = 'sk-proj-yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy' PINECONE_API_KEY = 'pcsk_xxxxxx_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx' (DOC_PATH, DOC_UNIQUE_ID, DOC_PINECONE_NAMESPACE) = ('./FAQ.txt', 'DOC0001', 'namespace01') (PINECONE_INDEX, PINECONE_INDEX_MODEL, PINECONE_ENVIRONMENT) = ('my-test-index', 'text-embedding-3-large', 'us-east-1') # ---------------------------------------------------------------------------------- def get_openai_embeddings(text): response = openai.embeddings.create(input=text, model=PINECONE_INDEX_MODEL) return response.data[0].embedding pc = Pinecone(api_key = PINECONE_API_KEY) if not pc.has_index(PINECONE_INDEX): pc.create_index(name=PINECONE_INDEX, dimension=3072, metric="cosine", deletion_protection="disabled", spec=ServerlessSpec(cloud="aws", region="us-east-1")) with open(DOC_PATH, "r", encoding="utf-8") as f: text = f.read() embedding = get_openai_embeddings(text) index = pc.Index(PINECONE_INDEX) index.upsert([(DOC_UNIQUE_ID, embedding),], namespace = DOC_PINECONE_NAMESPACE) print(f"Document with ID '{DOC_UNIQUE_ID}' has been upserted into the Pinecone index.") ``` **(2) Query Python script**: ```auto from pinecone import Pinecone, ServerlessSpec import openai OPENAI_API_KEY = 'sk-proj-yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy' OPENAI_EMBEDDING_MODEL = 'text-embedding-3-large' PINECONE_API_KEY = 'pcsk_xxxxxx_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx' PINECONE_INDEX = 'my-test-index' PINECONE_ENVIRONMENT = 'us-east-1' PINECONE_NAMESPACE = "namespace01" openai.api_key = OPENAI_API_KEY pc = Pinecone(api_key = PINECONE_API_KEY) index = pc.Index(PINECONE_INDEX) def get_embedding(text: str, model: str = OPENAI_EMBEDDING_MODEL) -> list: response = openai.embeddings.create(input=text, model=model) return response.data[0].embedding def query_pinecone(embedding: list, top_k: int = 5) -> list: query_response = index.query( namespace=PINECONE_NAMESPACE, top_k=top_k, vector=embedding, include_metadata=True ) return [f"{query_response['namespace']}: {match}" for match in query_response['matches']] def query_with_natural_language(query: str, top_k: int = 5) -> list: embedding = get_embedding(query, model=OPENAI_EMBEDDING_MODEL) responses = query_pinecone(embedding, top_k=top_k) return responses if __name__ == " __main__": user_query = "What classes are offered at your facility?" responses = query_with_natural_language(user_query, top_k=5) print("\nTop Responses:") for i, response in enumerate(responses, start=1): print(f"{i}. {response}") ``` **(3) Document contents**: ```auto Synergy Fitness Gym FAQ. Q1: What are your hours of operation? A1: Our gym is open Monday to Thursday from 5am to 10pm, Friday from 5am to 9pm, Saturday from 8am to 7pm, and Sunday from 9am to 6pm. Q2: Do I need a membership to use the facilities? A2: No, we offer day passes for out-of-town visitors or those who prefer not to commit to a membership. Day passes are available online or at the front desk. Q3: What types of cardio equipment do you have? A3: Our gym features treadmills, elliptical machines, stationary bikes, and rowing machines. Q4: Can I take group fitness classes if I'm not a member? A4: Yes, many of our group fitness classes are open to non-members. Please check the schedule at the front desk or online for availability. Q5: How do I sign up for personal training sessions? A5: To schedule a personal training session, please visit our website and fill out the online form. Our staff will contact you to arrange a time that suits your schedule. Q6: Do you offer childcare services? A6: Unfortunately, we do not offer on-site childcare services. However, there are several nearby daycare centers and family-friendly activities available in the area. Q7: Can I bring my dog to the gym? A7: No, dogs are not allowed inside the gym with the exception of service animals. Q8: What is your policy on food and beverages? A8: We have a small selection of healthy snacks and beverages for sale at our cafe. Outside food and drinks are permitted in designated areas only. Q9: How do I cancel or modify my membership? A9: To cancel or modify your membership, please contact our customer service team via phone or email. Q10: Do you have showers and locker rooms available for members? A10: Yes, we have clean and well-maintained showers and locker rooms available for use by all members. Q11: Can I reserve a spot on a machine or equipment? A11: Yes, we offer a reservation system for high-demand equipment such as treadmills and stationary bikes. Please check our website or mobile app for availability. Q12: Do you offer discounts for students, seniors, or military personnel? A12: Yes, we offer discounted membership rates for students, seniors, and military personnel with valid ID. Q13: How do I track my workouts and progress? A13: We provide access to our online fitness tracking platform, where you can log your workouts, view your progress, and set goals. Q14: Can I rent equipment or machines if mine is not in working order? A14: Yes, we offer a rental program for equipment that is not available for purchase. Please contact our staff to arrange a rental. Q15: Do you have any special events or activities planned throughout the year? A15: Yes, we regularly host workshops, seminars, and fitness events. Check our website or social media accounts for upcoming events and schedule updates. Q16: How much does membership cost? A16: There is a month-to-month membership costing $25/month; and a annual membership costing $275/year. Q17: What Gym classes do you offer? A17: We currently offer five (qty. 5) classes. They are: (1) Beginners Class, (2) Intermediate Class, (3) Cycling, (4) Jazzercise, and (5) Zumba. ``` ---
**Author:** ![lauren.s](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/lauren.s/32/3473_2.png) [@lauren.s](https://community.pinecone.io/u/lauren.s)\ **Post date:** [December 16, 2024, 8:05pm UTC](https://community.pinecone.io/t/relevant-query-to-a-single-document-index-returns-zero-results/7179/2 "2024-12-16T20:05:52Z")
Hi @nmvega, welcome to the forum! The response object you’re getting back is what I would expect (see [docs](https://docs.pinecone.io/guides/data/query-data#query-by-vector)), so I think there might be something going on with how the data was indexed. `values` being empty in the response suggest that there is no actual vector data being stored/retrieved during the query. Let’s start first by ruling that out any issues with the query script. Can you try fetching the vector data by ID: ```auto from pinecone import Pinecone pc = Pinecone(api_key="YOUR_API_KEY") index.fetch(ids="DOC0001", namespace="namespace01") ``` This should return the vector record and values. If the values are empty on the fetch, you can start to troubleshoot the embedding/upsert script. If there are indeed values in the vector record, we can start to troubleshoot the second query script. ---
**Author:** ![lauren.s](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/lauren.s/32/3473_2.png) [@lauren.s](https://community.pinecone.io/u/lauren.s)\ **Post date:** [December 16, 2024, 8:09pm UTC](https://community.pinecone.io/t/relevant-query-to-a-single-document-index-returns-zero-results/7179/3 "2024-12-16T20:09:43Z")
@nmvega - Actually, if you try adding `include_values=True` to your query, the values should print. Try this first! ---
**Author:** ![nmvega](https://sea2.discourse-cdn.com/flex020/user_avatar/community.pinecone.io/nmvega/32/3650_2.png) [@nmvega](https://community.pinecone.io/u/nmvega)\ **Post date:** [December 16, 2024, 10:27pm UTC](https://community.pinecone.io/t/relevant-query-to-a-single-document-index-returns-zero-results/7179/4 "2024-12-16T22:27:15Z")
Hi @lauren.s Indeed your suggestion to add `include_values=True` remedied my issue (… _I’m still familiarizing myself with the library and API_). Now, I can turn attention back to my **n8n** instance and resume troubleshooting why the same query isn’t returning anything there (_… having confirmed proper behavior under the hood with Python_). Thank you. 😊 I marked your answer as the solution.