From 8f3aaee39cf42a0d3975ad229ce4d1defe719bb7 Mon Sep 17 00:00:00 2001 From: Ismail Pelaseyed Date: Wed, 28 Feb 2024 00:49:42 -0800 Subject: [PATCH] Add support for queryig code interpreter --- README.md | 3 +- models/query.py | 1 + poetry.lock | 462 ++++++++++++++++++------------------ service/code_interpreter.py | 74 ++++-- service/router.py | 25 +- 5 files changed, 302 insertions(+), 263 deletions(-) diff --git a/README.md b/README.md index bb5f2f78..48e6dbd7 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,8 @@ Input example: "encoder": { "type": "openai", "name": "text-embedding-3-small", - } + }, + "interpreter_mode": False, # Set to True if you wish to run computation Q&A with a code interpreter "session_id": "my_session_id" # keeps micro-vm sessions and enables caching } ``` diff --git a/models/query.py b/models/query.py index a7dbfd04..13929277 100644 --- a/models/query.py +++ b/models/query.py @@ -12,6 +12,7 @@ class RequestPayload(BaseModel): index_name: str encoder: Encoder session_id: Optional[str] = None + interpreter_mode: Optional[bool] = False class ResponseData(BaseModel): diff --git a/poetry.lock b/poetry.lock index 53985cd1..2488c6dc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -325,13 +325,13 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "cachetools" -version = "5.3.2" +version = "5.3.3" description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" files = [ - {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, - {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, + {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"}, + {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"}, ] [[package]] @@ -632,13 +632,13 @@ 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b/service/code_interpreter.py index 7c456fa5..8258c7dd 100644 --- a/service/code_interpreter.py +++ b/service/code_interpreter.py @@ -1,12 +1,21 @@ +import re import asyncio import logging import time +import textwrap from typing import List +from decouple import config +from openai import AsyncOpenAI +import pandas as pd from e2b import Sandbox logging.getLogger("e2b").setLevel(logging.INFO) +client = AsyncOpenAI( + api_key=config("OPENAI_API_KEY"), +) + class CodeInterpreterService: timeout = 3 * 60 # 3 minutes @@ -73,36 +82,65 @@ async def __aexit__(self, _exc_type, _exc_value, _traceback): self.sandbox.keep_alive(self.timeout) self.sandbox.close() - def get_files_code(self): + def get_dataframe(self): """ Get the code to read the files in the sandbox. This can be used for instructing the LLM how to access the loaded files. """ - # TODO: Add support for xslx, json - files_code = "\n".join( - f'df{i} = pd.read_csv("{self._get_file_path(url)}") # {url}' - for i, url in enumerate(self.file_urls) + df = pd.read_csv(self.file_urls[0]) + return df, self.file_urls[0] + + def genreate_prompt(self, query: str) -> str: + df, url = self.get_dataframe() + return textwrap.dedent( + f""" + You are provided with a following pandas dataframe (`df`): + {df.info()} + + Using the provided dataframe (`df`), update the following python code using pandas that returns the answer to question: \"{query}\" + + This is the initial python code to be updated: + + ```python + import pandas as pd + + df = pd.read_csv("{url}") + 1. Process: Manipulating data for analysis (grouping, filtering, aggregating, etc.) + 2. Analyze: Conducting the actual analysis + 3. Output: Returning the answer as a string + ``` + """ ) - return f""" -import pandas as pd - -{files_code} + def extract_code(self, code: str) -> str: + pattern = r"```(?:python)?(.*?)```" + matches = re.findall(pattern, code, re.DOTALL) + if matches: + return matches[0].strip() + return "" -""" + async def generate_code( + self, + query: str, + ) -> str: + content = self.genreate_prompt(query=query) + completion = await client.chat.completions.create( + messages=[ + { + "role": "user", + "content": content, + } + ], + model="gpt-3.5-turbo-0125", + ) + output = completion.choices[0].message.content + return self.extract_code(code=output) async def run_python(self, code: str): - files_code = self.get_files_code() - - templated_code = f""" -{files_code} -{code} -""" - epoch_time = time.time() codefile_path = f"/tmp/main-{epoch_time}.py" - self.sandbox.filesystem.write(codefile_path, templated_code) + self.sandbox.filesystem.write(codefile_path, code) process = await asyncio.to_thread( self.sandbox.process.start_and_wait, f"python {codefile_path}", diff --git a/service/router.py b/service/router.py index e2ff9560..9d307998 100644 --- a/service/router.py +++ b/service/router.py @@ -6,12 +6,14 @@ from models.document import BaseDocumentChunk from models.query import RequestPayload -# from service.code_interpreter import CodeInterpreterService +from service.code_interpreter import CodeInterpreterService from service.embedding import get_encoder from utils.logger import logger from utils.summarise import SUMMARY_SUFFIX from vectordbs import BaseVectorDatabase, get_vector_service +STRUTURED_DATA = [".xlsx", ".csv", ".json"] + def create_route_layer() -> RouteLayer: routes = [ @@ -35,10 +37,18 @@ async def get_documents( *, vector_service: BaseVectorDatabase, payload: RequestPayload ) -> list[BaseDocumentChunk]: chunks = await vector_service.query(input=payload.input, top_k=5) - if not len(chunks): logger.error(f"No documents found for query: {payload.input}") return [] + is_structured = chunks[0].metadata.get("document_type") in STRUTURED_DATA + if is_structured and payload.interpreter_mode: + code_interpreter = CodeInterpreterService( + session_id=payload.session_id, file_urls=[chunks[0].metadata.get("doc_url")] + ) + code = await code_interpreter.generate_code(query=payload.input) + async with code_interpreter as service: + response = await service.run_python(code=code) + output = response.stdout reranked_chunks = await vector_service.rerank(query=payload.input, documents=chunks) return reranked_chunks @@ -63,15 +73,4 @@ async def query(payload: RequestPayload) -> list[BaseDocumentChunk]: encoder=encoder, ) - # async with CodeInterpreterService( - # session_id=payload.session_id, - # file_urls=[ - # "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" - # ], - # ) as service: - # code = "df0.info()" - # output = await service.run_python(code=code) - # print(output.stderr) - # print(output.stdout) - return await get_documents(vector_service=vector_service, payload=payload)