Spaces:
No application file
No application file
File size: 3,320 Bytes
a85c9b8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
title: '❓ query'
---
`.query()` method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below:
### Parameters
<ParamField path="input_query" type="str">
Question to ask
</ParamField>
<ParamField path="config" type="BaseLlmConfig" optional>
Configure different llm settings such as prompt, temprature, number_documents etc.
</ParamField>
<ParamField path="dry_run" type="bool" optional>
The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
</ParamField>
<ParamField path="where" type="dict" optional>
A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
</ParamField>
<ParamField path="citations" type="bool" optional>
Return citations along with the LLM answer. Defaults to `False`
</ParamField>
### Returns
<ResponseField name="answer" type="str | tuple">
If `citations=False`, return a stringified answer to the question asked. <br />
If `citations=True`, returns a tuple with answer and citations respectively.
</ResponseField>
## Usage
### With citations
If you want to get the answer to question and return both answer and citations, use the following code snippet:
```python With Citations
from embedchain import App
# Initialize app
app = App()
# Add data source
app.add("https://www.forbes.com/profile/elon-musk")
# Get relevant answer for your query
answer, sources = app.query("What is the net worth of Elon?", citations=True)
print(answer)
# Answer: The net worth of Elon Musk is $221.9 billion.
print(sources)
# [
# (
# 'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
# {
# 'url': 'https://www.forbes.com/profile/elon-musk',
# 'score': 0.89,
# ...
# }
# ),
# (
# '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
# {
# 'url': 'https://www.forbes.com/profile/elon-musk',
# 'score': 0.81,
# ...
# }
# ),
# (
# 'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
# {
# 'url': 'https://www.forbes.com/profile/elon-musk',
# 'score': 0.73,
# ...
# }
# )
# ]
```
<Note>
When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):
1. source chunk
2. dictionary with metadata about the source chunk
- `url`: url of the source
- `doc_id`: document id (used for book keeping purposes)
- `score`: score of the source chunk with respect to the question
- other metadata you might have added at the time of adding the source
</Note>
### Without citations
If you just want to return answers and don't want to return citations, you can use the following example:
```python Without Citations
from embedchain import App
# Initialize app
app = App()
# Add data source
app.add("https://www.forbes.com/profile/elon-musk")
# Get relevant answer for your query
answer = app.query("What is the net worth of Elon?")
print(answer)
# Answer: The net worth of Elon Musk is $221.9 billion.
```
|