├── .gitignore
├── LICENSE
├── README.md
├── diagram.png
├── generate.py
├── gmail.py
├── poetry.lock
├── pyproject.toml
├── sample_generated_code.py
└── summarize.py


/.gitignore:
--------------------------------------------------------------------------------
1 | credentials.json
2 | token.json
3 | __pycache__
4 | .env
5 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | The MIT License
 2 | 
 3 | Copyright (c) LlamaIndex 
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in
13 | all copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
21 | THE SOFTWARE.
22 | 


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/README.md:
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 1 | # "Training" Python code with an LLM
 2 | 
 3 | ## The problem
 4 | 
 5 | I thought it would be neat if an LLM could go through all my emails and tell me all the places I've traveled to in the world by extracting the destinations from the flight itineraries.
 6 | 
 7 | ## Set up: connecting to GMail
 8 | 
 9 | [LlamaHub](https://llamahub.ai) has a [GMail tool](https://llamahub.ai/l/tools/llama-index-tools-google) for use by agents. This is where I went first. You have to dance the authentication dance with Google first, however. Here's what I did:
10 | 
11 | * Created a new project in the [Google Cloud Console](https://console.cloud.google.com/)
12 | * Went to APIs & Services -> Library and searched for GMail, then enabled that API
13 | * Went to APIs & Services -> Credentials and created a new OAuth client ID
14 |     * Application type: Web application
15 |     * Authorized redirect URIs: http://localhost:8080/ (the last slash seems important)
16 | * Went to APIs & Services -> OAuth consent screen and made the app external, which allowed me to connect my personal GMail to it once I explicitly added it as an allowed test user
17 | * Downloaded the credentials JSON file and saved it as `credentials.json` in the root of my project
18 | 
19 | Unfortunately, the GMail tool doesn't have a way of paginating through lots of results, so I copied and modified it, which you'll find in [gmail.py](gmail.py).
20 | 
21 | ## First pass: get the LLM to categorize each email
22 | 
23 | In [summarize.py](summarize.py) you can see my first attempt, where I run through every message in a search matching "your flight itinerary" and try to get the LLM to categorize it, spitting out JSON every time. This works! But it's very slow, and it also uses up hella tokens -- it could get expensive!
24 | 
25 | ## Second attempt: get the LLM to generate an email categorization script
26 | 
27 | In [generate.py](generate.py) you can see my second solution: instead of running the LLM on every email, I get it to run through a subset of them. For each email, I give it the body of the email as well as a Python function whose purpose is to detect if an email body is an itinerary (this starts off just being an empty string).
28 | 
29 | If the LLM thinks the email is an itinerary, it is instructed to modify the Python function so that the email would be detected. It's also instructed to make sure the previous emails would still be detected. So it iterates, making a progressively more complicated Python function every time, that can detect more and more itineraries. This is the process:
30 | 
31 | ![diagram](diagram.png)
32 | 
33 | In [sample_generated_code.py](sample_generated_code.py) you can see the output of this process after running through about 100 emails, not all of which were actually itineraries (lots of spam from airlines matches the search). You can see it's slowly iterating towards having a detection block for each individual airline, which is what I imagine I would have come up with as a human anyway, but with a lot more futzing around.
34 | 
35 | ## Further work
36 | 
37 | Some next steps that have occurred to me:
38 | 
39 | * Improve the search string to exclude more spam so it gets trained on more actual itineraries (it's reading a lot of spam right now)
40 | * Use a local model to save me money (I've been looking at `codestral`, the latest from Mistral. Meta's `llama3` wasn't able to do it.)
41 | * Explicitly include in the prompt instructions to combine detection blocks when possible. This seems complicated! Not sure if it will be able to do that.
42 | 


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/diagram.png:
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https://raw.githubusercontent.com/run-llama/gmail-extractor/7c241795d861410fa4123d6735d9f71bd5d8f703/diagram.png


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/generate.py:
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  1 | # dotenv will load our API keys from .env
  2 | import dotenv
  3 | dotenv.load_dotenv()
  4 | 
  5 | # llamaindex deps
  6 | from llama_index.core import Settings
  7 | from llama_index.llms.openai import OpenAI
  8 | from llama_index.llms.gemini import Gemini
  9 | from llama_index.llms.ollama import Ollama
 10 | import tiktoken
 11 | import json
 12 | 
 13 | from gmail import GmailSearcher
 14 | searcher = GmailSearcher()
 15 | 
 16 | # if using openAI, this specifies which model to use
 17 | # and it will use the same model for counting tokens.
 18 | # If using non-OpenAI model, it will count as if for an openAI model because I'm lazy
 19 | #MODEL="gemini-1.5-pro-latest"
 20 | #MODEL = "gpt-4o"
 21 | MODEL = "gpt-3.5-turbo"
 22 | #Settings.llm = OpenAI(model=MODEL)
 23 | 
 24 | Settings.llm = Gemini(
 25 |     model="models/gemini-1.5-pro-latest",
 26 |     temperature=0.1
 27 | )
 28 | 
 29 | # Settings.llm = Ollama(model="llama3", request_timeout=30.0)
 30 | 
 31 | # some emails have attachments and are enormous and hard to parse
 32 | # so we slice everything down to 128k tokens or less.
 33 | def sliceUntilFits(string, max_tokens):
 34 |     enc = tiktoken.encoding_for_model(MODEL)
 35 |     while True:
 36 |         encoded = enc.encode(string)    
 37 |         print(f"Number of tokens: {len(encoded)}")
 38 |         if len(encoded) > 300000: # like, WAY too long
 39 |             string = string[-300000:] # get the last 300k chars
 40 |         elif len(encoded) > max_tokens:
 41 |             print("Message too long, slicing it down")
 42 |             string = string[:-10000] # remove the last 10k chars to shorten it
 43 |         else:
 44 |             return string
 45 | 
 46 | # this processes a batch of emails and modifies the python code via the LLM
 47 | def summarizeMessages(messages,extraction_code):
 48 |     for message in messages:
 49 |         print("Handling a message")
 50 |         print(message['extra_info'])
 51 |         instructions = f"""
 52 |             Attached is the body of an email message, and a block of python code (which might be empty). The Python code's job is to extract flight itineraries from emails. If you detect that the email is a flight itinerary, modify the Python code such that it would correctly extract the origin and destination of the flight from this email as well as the emails it already knows how to parse. The code should return JSON listing the origin and destination, like this:
 53 |             {{
 54 |                 "isItinerary": true,
 55 |                 "origin": "San Francisco, USA",
 56 |                 "destination": "New York City, USA"
 57 |             }}
 58 |             If the email is not an itinerary (most will not be), you do not need to modify the python code.
 59 |             
 60 |             In either case, or if you can't figure out what to do, return the following JSON:
 61 |             {{
 62 |                 "was_itinerary": True or False depending if it was an itinerary,
 63 |                 "modified_code": true or false depending if you modified the code,
 64 |                 "code": the python code, correctly enclosed in quotes and escaped for JSON
 65 |             }}
 66 |             You don't need to enclose the JSON in backticks or any other quoting, and you don't need to include any other information.
 67 |             
 68 |             The python code is between the next two lines of dashes:
 69 |             ------------
 70 |             {extraction_code}
 71 |             ------------
 72 | 
 73 |             And the text of the email is below this line:
 74 |             ------------
 75 |             {message['text']}
 76 |             """
 77 |         # openai has a maximum string length it can handle
 78 |         instructions = sliceUntilFits(instructions, 128000)
 79 |         response = Settings.llm.complete(instructions)
 80 |         try:
 81 |             result = json.loads(str(response))
 82 |             print(f"Was itinerary: {result['was_itinerary']}")
 83 |             extraction_code = result['code']
 84 |         except Exception as e:
 85 |             print("Error parsing response")
 86 |             print(e)
 87 |     return extraction_code
 88 | 
 89 | # this iterates through all emails matching the search
 90 | # it prints out the resulting python after each batch
 91 | next_token = None
 92 | extraction_code = ""
 93 | while True:
 94 |     # TODO: get the LLM to think of good searches
 95 |     messageResults = searcher.search_messages(
 96 |         "your flight itinerary", 
 97 |         max_results=4, # number of messages in a batch 
 98 |         next_token=next_token
 99 |     )
100 |     try:
101 |         extraction_code = summarizeMessages(messageResults['messages'],extraction_code)
102 |     except Exception as e:
103 |         print("Error summarizing messages")
104 |         print(e)
105 |     print("==== Current extraction code ====:")
106 |     print(extraction_code)
107 |     if messageResults['next_token']:
108 |         next_token = messageResults['next_token']
109 |     else:
110 |         break
111 | 


--------------------------------------------------------------------------------
/gmail.py:
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  1 | # heavily based on the Google reader from llamahub
  2 | # it adds the ability to paginate through the results
  3 | # and discards the ability to compose emails
  4 | 
  5 | import os
  6 | import base64
  7 | import email
  8 | from email.message import EmailMessage
  9 | from google.auth.transport.requests import Request
 10 | from google.oauth2.credentials import Credentials
 11 | from google_auth_oauthlib.flow import InstalledAppFlow
 12 | from typing import Any, List, Optional
 13 | 
 14 | SCOPES = [
 15 |     # "https://www.googleapis.com/auth/gmail.compose",
 16 |     "https://www.googleapis.com/auth/gmail.readonly",
 17 | ]
 18 | 
 19 | class GmailSearcher:
 20 |     query: str = None
 21 |     use_iterative_parser: bool = False
 22 |     max_results: int = 10
 23 |     service = None
 24 | 
 25 |     def _cache_service(self) -> None:
 26 |         from googleapiclient.discovery import build
 27 | 
 28 |         credentials = self._get_credentials()
 29 |         if not self.service:
 30 |             self.service = build("gmail", "v1", credentials=credentials)
 31 | 
 32 |     def _get_credentials(self) -> Any:
 33 |         """Get valid user credentials from storage.
 34 | 
 35 |         The file token.json stores the user's access and refresh tokens, and is
 36 |         created automatically when the authorization flow completes for the first
 37 |         time.
 38 | 
 39 |         Returns:
 40 |             Credentials, the obtained credential.
 41 |         """
 42 |         import os
 43 | 
 44 |         from google.auth.transport.requests import Request
 45 |         from google.oauth2.credentials import Credentials
 46 |         from google_auth_oauthlib.flow import InstalledAppFlow
 47 | 
 48 |         creds = None
 49 |         if os.path.exists("token.json"):
 50 |             creds = Credentials.from_authorized_user_file("token.json", SCOPES)
 51 |         # If there are no (valid) credentials available, let the user log in.
 52 |         if not creds or not creds.valid:
 53 |             if creds and creds.expired and creds.refresh_token:
 54 |                 creds.refresh(Request())
 55 |             else:
 56 |                 flow = InstalledAppFlow.from_client_secrets_file(
 57 |                     "credentials.json", SCOPES
 58 |                 )
 59 |                 creds = flow.run_local_server(port=8080)
 60 |             # Save the credentials for the next run
 61 |             with open("token.json", "w") as token:
 62 |                 token.write(creds.to_json())
 63 | 
 64 |         return creds
 65 |     
 66 |     def search_messages(self, query: str, max_results = None, next_token = None):
 67 |         if not max_results:
 68 |             max_results = self.max_results
 69 | 
 70 |         self._cache_service()
 71 | 
 72 |         # https://googleapis.github.io/google-api-python-client/docs/dyn/gmail_v1.users.messages.html#list
 73 |         messagesResult = (
 74 |             self.service.users()
 75 |             .messages()
 76 |             .list(userId="me", q=query, maxResults=int(max_results), pageToken=next_token)
 77 |             .execute()
 78 |         )
 79 |         messages = messagesResult.get("messages", [])
 80 |         next_token = messagesResult.get("nextPageToken", None)
 81 | 
 82 |         results = []
 83 |         try:
 84 |             for message in messages:
 85 |                 message_data = self.get_message_data(message)
 86 |                 text = message_data.pop("body")
 87 |                 extra_info = message_data
 88 |                 results.append({
 89 |                     "text":text, 
 90 |                     "extra_info":extra_info
 91 |                 })
 92 |         except Exception as e:
 93 |             raise Exception("Can't get message data" + str(e))
 94 | 
 95 |         return {
 96 |             "messages": results,
 97 |             "next_token": next_token
 98 |         }
 99 |     
100 |     def get_message_data(self, message):
101 |         message_id = message["id"]
102 |         message_data = (
103 |             self.service.users()
104 |             .messages()
105 |             .get(format="raw", userId="me", id=message_id)
106 |             .execute()
107 |         )
108 |         if self.use_iterative_parser:
109 |             body = self.extract_message_body_iterative(message_data)
110 |         else:
111 |             body = self.extract_message_body(message_data)
112 | 
113 |         if not body:
114 |             return None
115 | 
116 |         return {
117 |             "id": message_data["id"],
118 |             "threadId": message_data["threadId"],
119 |             "snippet": message_data["snippet"],
120 |             "body": body,
121 |         }
122 | 
123 |     def extract_message_body_iterative(self, message: dict):
124 |         if message["raw"]:
125 |             body = base64.urlsafe_b64decode(message["raw"].encode("utf8"))
126 |             mime_msg = email.message_from_bytes(body)
127 |         else:
128 |             mime_msg = message
129 | 
130 |         body_text = ""
131 |         if mime_msg.get_content_type() == "text/plain":
132 |             plain_text = mime_msg.get_payload(decode=True)
133 |             charset = mime_msg.get_content_charset("utf-8")
134 |             body_text = plain_text.decode(charset).encode("utf-8").decode("utf-8")
135 | 
136 |         elif mime_msg.get_content_maintype() == "multipart":
137 |             msg_parts = mime_msg.get_payload()
138 |             for msg_part in msg_parts:
139 |                 body_text += self.extract_message_body_iterative(msg_part)
140 | 
141 |         return body_text
142 | 
143 |     def extract_message_body(self, message: dict):
144 |         from bs4 import BeautifulSoup
145 | 
146 |         try:
147 |             body = base64.urlsafe_b64decode(message["raw"].encode("utf-8"))
148 |             mime_msg = email.message_from_bytes(body)
149 | 
150 |             # If the message body contains HTML, parse it with BeautifulSoup
151 |             if "text/html" in mime_msg:
152 |                 soup = BeautifulSoup(body, "html.parser")
153 |                 body = soup.get_text()
154 |             return body.decode("utf-8")
155 |         except Exception as e:
156 |             raise Exception("Can't parse message body" + str(e))
157 | 
158 | # # authorize accessing gmail.
159 | # # your app needs a credentials.json with access to the scopes listed above
160 | # # unless you get your app verified by google, you'll need to set it to test mode
161 | # # and manually add the emails of users you want to be able to use this app
162 | # creds = None
163 | # if os.path.exists("token.json"):
164 | #     creds = Credentials.from_authorized_user_file("token.json", SCOPES)
165 | # # If there are no (valid) credentials available, let the user log in.
166 | # if not creds or not creds.valid:
167 | #     if creds and creds.expired and creds.refresh_token:
168 | #         creds.refresh(Request())
169 | #     else:
170 | #         flow = InstalledAppFlow.from_client_secrets_file(
171 | #             "credentials.json", SCOPES
172 | #         )
173 | #         creds = flow.run_local_server(port=8080)
174 | #     # Save the credentials for the next run
175 | #     with open("token.json", "w") as token:
176 | #         token.write(creds.to_json())
177 | 


--------------------------------------------------------------------------------
/poetry.lock:
--------------------------------------------------------------------------------
   1 | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
   2 | 
   3 | [[package]]
   4 | name = "aiohttp"
   5 | version = "3.9.5"
   6 | description = "Async http client/server framework (asyncio)"
   7 | optional = false
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  87 | 
  88 | [package.dependencies]
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  91 | frozenlist = ">=1.1.1"
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  93 | yarl = ">=1.0,<2.0"
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 101 | description = "aiosignal: a list of registered asynchronous callbacks"
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 108 | 
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 115 | description = "Reusable constraint types to use with typing.Annotated"
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 137 | 
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 142 | 
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 153 | 
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 156 | dev = ["attrs[tests]", "pre-commit"]
 157 | docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
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 161 | 
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 165 | description = "Python package for providing Mozilla's CA Bundle."
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 270 | ]
 271 | 
 272 | [[package]]
 273 | name = "click"
 274 | version = "8.1.7"
 275 | description = "Composable command line interface toolkit"
 276 | optional = false
 277 | python-versions = ">=3.7"
 278 | files = [
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 281 | ]
 282 | 
 283 | [package.dependencies]
 284 | colorama = {version = "*", markers = "platform_system == \"Windows\""}
 285 | 
 286 | [[package]]
 287 | name = "colorama"
 288 | version = "0.4.6"
 289 | description = "Cross-platform colored terminal text."
 290 | optional = false
 291 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 292 | files = [
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 295 | ]
 296 | 
 297 | [[package]]
 298 | name = "dataclasses-json"
 299 | version = "0.6.6"
 300 | description = "Easily serialize dataclasses to and from JSON."
 301 | optional = false
 302 | python-versions = "<4.0,>=3.7"
 303 | files = [
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 306 | ]
 307 | 
 308 | [package.dependencies]
 309 | marshmallow = ">=3.18.0,<4.0.0"
 310 | typing-inspect = ">=0.4.0,<1"
 311 | 
 312 | [[package]]
 313 | name = "deprecated"
 314 | version = "1.2.14"
 315 | description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
 316 | optional = false
 317 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 318 | files = [
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 321 | ]
 322 | 
 323 | [package.dependencies]
 324 | wrapt = ">=1.10,<2"
 325 | 
 326 | [package.extras]
 327 | dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 328 | 
 329 | [[package]]
 330 | name = "dirtyjson"
 331 | version = "1.0.8"
 332 | description = "JSON decoder for Python that can extract data from the muck"
 333 | optional = false
 334 | python-versions = "*"
 335 | files = [
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 338 | ]
 339 | 
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 341 | name = "distro"
 342 | version = "1.9.0"
 343 | description = "Distro - an OS platform information API"
 344 | optional = false
 345 | python-versions = ">=3.6"
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 349 | ]
 350 | 
 351 | [[package]]
 352 | name = "frozenlist"
 353 | version = "1.4.1"
 354 | description = "A list-like structure which implements collections.abc.MutableSequence"
 355 | optional = false
 356 | python-versions = ">=3.8"
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 436 | 
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 438 | name = "fsspec"
 439 | version = "2024.5.0"
 440 | description = "File-system specification"
 441 | optional = false
 442 | python-versions = ">=3.8"
 443 | files = [
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 446 | ]
 447 | 
 448 | [package.extras]
 449 | abfs = ["adlfs"]
 450 | adl = ["adlfs"]
 451 | arrow = ["pyarrow (>=1)"]
 452 | dask = ["dask", "distributed"]
 453 | dev = ["pre-commit", "ruff"]
 454 | dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 455 | full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 456 | fuse = ["fusepy"]
 457 | gcs = ["gcsfs"]
 458 | git = ["pygit2"]
 459 | github = ["requests"]
 460 | gs = ["gcsfs"]
 461 | gui = ["panel"]
 462 | hdfs = ["pyarrow (>=1)"]
 463 | http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 464 | libarchive = ["libarchive-c"]
 465 | oci = ["ocifs"]
 466 | s3 = ["s3fs"]
 467 | sftp = ["paramiko"]
 468 | smb = ["smbprotocol"]
 469 | ssh = ["paramiko"]
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 473 | tqdm = ["tqdm"]
 474 | 
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 476 | name = "greenlet"
 477 | version = "3.0.3"
 478 | description = "Lightweight in-process concurrent programming"
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 541 | 
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 543 | docs = ["Sphinx", "furo"]
 544 | test = ["objgraph", "psutil"]
 545 | 
 546 | [[package]]
 547 | name = "h11"
 548 | version = "0.14.0"
 549 | description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
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 551 | python-versions = ">=3.7"
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 556 | 
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 559 | version = "1.0.5"
 560 | description = "A minimal low-level HTTP client."
 561 | optional = false
 562 | python-versions = ">=3.8"
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 567 | 
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 571 | 
 572 | [package.extras]
 573 | asyncio = ["anyio (>=4.0,<5.0)"]
 574 | http2 = ["h2 (>=3,<5)"]
 575 | socks = ["socksio (==1.*)"]
 576 | trio = ["trio (>=0.22.0,<0.26.0)"]
 577 | 
 578 | [[package]]
 579 | name = "httpx"
 580 | version = "0.27.0"
 581 | description = "The next generation HTTP client."
 582 | optional = false
 583 | python-versions = ">=3.8"
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 588 | 
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 591 | certifi = "*"
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 593 | idna = "*"
 594 | sniffio = "*"
 595 | 
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 598 | cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 599 | http2 = ["h2 (>=3,<5)"]
 600 | socks = ["socksio (==1.*)"]
 601 | 
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 603 | name = "idna"
 604 | version = "3.7"
 605 | description = "Internationalized Domain Names in Applications (IDNA)"
 606 | optional = false
 607 | python-versions = ">=3.5"
 608 | files = [
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 612 | 
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 615 | version = "1.4.2"
 616 | description = "Lightweight pipelining with Python functions"
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 618 | python-versions = ">=3.8"
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 622 | ]
 623 | 
 624 | [[package]]
 625 | name = "llama-index-core"
 626 | version = "0.10.42"
 627 | description = "Interface between LLMs and your data"
 628 | optional = false
 629 | python-versions = "<4.0,>=3.8.1"
 630 | files = [
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 637 | dataclasses-json = "*"
 638 | deprecated = ">=1.2.9.3"
 639 | dirtyjson = ">=1.0.8,<2.0.0"
 640 | fsspec = ">=2023.5.0"
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 643 | nest-asyncio = ">=1.5.8,<2.0.0"
 644 | networkx = ">=3.0"
 645 | nltk = ">=3.8.1,<4.0.0"
 646 | numpy = "*"
 647 | openai = ">=1.1.0"
 648 | pandas = "*"
 649 | pillow = ">=9.0.0"
 650 | PyYAML = ">=6.0.1"
 651 | requests = ">=2.31.0"
 652 | SQLAlchemy = {version = ">=1.4.49", extras = ["asyncio"]}
 653 | tenacity = ">=8.2.0,<9.0.0"
 654 | tiktoken = ">=0.3.3"
 655 | tqdm = ">=4.66.1,<5.0.0"
 656 | typing-extensions = ">=4.5.0"
 657 | typing-inspect = ">=0.8.0"
 658 | wrapt = "*"
 659 | 
 660 | [[package]]
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 662 | version = "0.1.19"
 663 | description = ""
 664 | optional = false
 665 | python-versions = "<4,>=3.8"
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 673 | pydantic = ">=1.10"
 674 | 
 675 | [[package]]
 676 | name = "marshmallow"
 677 | version = "3.21.2"
 678 | description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 679 | optional = false
 680 | python-versions = ">=3.8"
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 687 | packaging = ">=17.0"
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 692 | tests = ["pytest", "pytz", "simplejson"]
 693 | 
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 695 | name = "multidict"
 696 | version = "6.0.5"
 697 | description = "multidict implementation"
 698 | optional = false
 699 | python-versions = ">=3.7"
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1107 | 
1108 | [package.extras]
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1285 | 
1286 | [[package]]
1287 | name = "regex"
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1416 | 
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1474 | 
1475 | [package.dependencies]
1476 | greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or extra == \"asyncio\""}
1477 | typing-extensions = ">=4.6.0"
1478 | 
1479 | [package.extras]
1480 | aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
1481 | aioodbc = ["aioodbc", "greenlet (!=0.4.17)"]
1482 | aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing_extensions (!=3.10.0.1)"]
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1484 | asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
1485 | mariadb-connector = ["mariadb (>=1.0.1,!=1.1.2,!=1.1.5)"]
1486 | mssql = ["pyodbc"]
1487 | mssql-pymssql = ["pymssql"]
1488 | mssql-pyodbc = ["pyodbc"]
1489 | mypy = ["mypy (>=0.910)"]
1490 | mysql = ["mysqlclient (>=1.4.0)"]
1491 | mysql-connector = ["mysql-connector-python"]
1492 | oracle = ["cx_oracle (>=8)"]
1493 | oracle-oracledb = ["oracledb (>=1.0.1)"]
1494 | postgresql = ["psycopg2 (>=2.7)"]
1495 | postgresql-asyncpg = ["asyncpg", "greenlet (!=0.4.17)"]
1496 | postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
1497 | postgresql-psycopg = ["psycopg (>=3.0.7)"]
1498 | postgresql-psycopg2binary = ["psycopg2-binary"]
1499 | postgresql-psycopg2cffi = ["psycopg2cffi"]
1500 | postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
1501 | pymysql = ["pymysql"]
1502 | sqlcipher = ["sqlcipher3_binary"]
1503 | 
1504 | [[package]]
1505 | name = "tenacity"
1506 | version = "8.3.0"
1507 | description = "Retry code until it succeeds"
1508 | optional = false
1509 | python-versions = ">=3.8"
1510 | files = [
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1514 | 
1515 | [package.extras]
1516 | doc = ["reno", "sphinx"]
1517 | test = ["pytest", "tornado (>=4.5)", "typeguard"]
1518 | 
1519 | [[package]]
1520 | name = "tiktoken"
1521 | version = "0.7.0"
1522 | description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
1523 | optional = false
1524 | python-versions = ">=3.8"
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1563 | 
1564 | [package.dependencies]
1565 | regex = ">=2022.1.18"
1566 | requests = ">=2.26.0"
1567 | 
1568 | [package.extras]
1569 | blobfile = ["blobfile (>=2)"]
1570 | 
1571 | [[package]]
1572 | name = "tqdm"
1573 | version = "4.66.4"
1574 | description = "Fast, Extensible Progress Meter"
1575 | optional = false
1576 | python-versions = ">=3.7"
1577 | files = [
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1580 | ]
1581 | 
1582 | [package.dependencies]
1583 | colorama = {version = "*", markers = "platform_system == \"Windows\""}
1584 | 
1585 | [package.extras]
1586 | dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
1587 | notebook = ["ipywidgets (>=6)"]
1588 | slack = ["slack-sdk"]
1589 | telegram = ["requests"]
1590 | 
1591 | [[package]]
1592 | name = "typing-extensions"
1593 | version = "4.12.1"
1594 | description = "Backported and Experimental Type Hints for Python 3.8+"
1595 | optional = false
1596 | python-versions = ">=3.8"
1597 | files = [
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1600 | ]
1601 | 
1602 | [[package]]
1603 | name = "typing-inspect"
1604 | version = "0.9.0"
1605 | description = "Runtime inspection utilities for typing module."
1606 | optional = false
1607 | python-versions = "*"
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1612 | 
1613 | [package.dependencies]
1614 | mypy-extensions = ">=0.3.0"
1615 | typing-extensions = ">=3.7.4"
1616 | 
1617 | [[package]]
1618 | name = "tzdata"
1619 | version = "2024.1"
1620 | description = "Provider of IANA time zone data"
1621 | optional = false
1622 | python-versions = ">=2"
1623 | files = [
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1627 | 
1628 | [[package]]
1629 | name = "urllib3"
1630 | version = "2.2.1"
1631 | description = "HTTP library with thread-safe connection pooling, file post, and more."
1632 | optional = false
1633 | python-versions = ">=3.8"
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1638 | 
1639 | [package.extras]
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1641 | h2 = ["h2 (>=4,<5)"]
1642 | socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
1643 | zstd = ["zstandard (>=0.18.0)"]
1644 | 
1645 | [[package]]
1646 | name = "wrapt"
1647 | version = "1.16.0"
1648 | description = "Module for decorators, wrappers and monkey patching."
1649 | optional = false
1650 | python-versions = ">=3.6"
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1723 | 
1724 | [[package]]
1725 | name = "yarl"
1726 | version = "1.9.4"
1727 | description = "Yet another URL library"
1728 | optional = false
1729 | python-versions = ">=3.7"
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1820 |     {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
1821 | ]
1822 | 
1823 | [package.dependencies]
1824 | idna = ">=2.0"
1825 | multidict = ">=4.0"
1826 | 
1827 | [metadata]
1828 | lock-version = "2.0"
1829 | python-versions = "^3.12"
1830 | content-hash = "74e80cf7a862cd4d5229b0bbc9aef56beb8d8beef15d0265eafae51df8fdcabe"
1831 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "country-map"
 3 | version = "0.1.0"
 4 | description = ""
 5 | authors = ["Laurie Voss <github@seldo.com>"]
 6 | readme = "README.md"
 7 | 
 8 | [tool.poetry.dependencies]
 9 | python = "^3.12"
10 | llama-index-core = "^0.10.42"
11 | 
12 | 
13 | [build-system]
14 | requires = ["poetry-core"]
15 | build-backend = "poetry.core.masonry.api"
16 | 


--------------------------------------------------------------------------------
/sample_generated_code.py:
--------------------------------------------------------------------------------
  1 | import re
  2 | from typing import Dict, List, Any
  3 | 
  4 | def extract_itinerary_details(email_body: str) -> List[Dict[str, Any]]:
  5 |     """Extracts itinerary details from an email body.
  6 | 
  7 |     Args:
  8 |         email_body: The email body text.
  9 | 
 10 |     Returns:
 11 |         A list of dictionaries, where each dictionary represents an itinerary
 12 |         and contains the following keys:
 13 |             - isItinerary: True if the email is an itinerary, False otherwise
 14 |             - origin: The origin of the flight
 15 |             - destination: The destination of the flight
 16 |     """
 17 |     itineraries = []
 18 | 
 19 |     # Example: Extract origin and destination from Alaska Airlines email
 20 |     if 'alaskaair' in email_body.lower():
 21 |         match = re.search(r'from ([A-Z]+).* to ([A-Z]+)', email_body)
 22 |         if match:
 23 |             origin = match.group(1)
 24 |             destination = match.group(2)
 25 |             itineraries.append({
 26 |                 "isItinerary": True,
 27 |                 "origin": origin,
 28 |                 "destination": destination
 29 |             })
 30 | 
 31 |     # Extract origin and destination from Alaska Airlines email
 32 |     if 'Confirmation code' in email_body:
 33 |         match = re.search(r'(\d{2}:\d{2} [AP]M)\s+([A-Z]+)\s+<br />\s+([A-Za-z]+, [A-Za-z]+)\s+<br />.*\d{2}:\d{2} [AP]M\s+([A-Z]+)\s+<br />\s+([A-Za-z]+, [A-Za-z]+)', email_body)
 34 |         if match:
 35 |             origin = match.group(2) + ', ' + match.group(3)
 36 |             destination = match.group(4) + ', ' + match.group(5)
 37 |             itineraries.append({
 38 |                 "isItinerary": True,
 39 |                 "origin": origin,
 40 |                 "destination": destination
 41 |             })
 42 | 
 43 |     # Extract origin and destination from JetBlue email
 44 |     if 'Prices shown:' in email_body:
 45 |         match = re.search(r'Prices shown: ([A-Z]+) to ([A-Z]+)\.', email_body)
 46 |         if match:
 47 |             origin = match.group(1)
 48 |             destination = match.group(2)
 49 |             itineraries.append({
 50 |                 "isItinerary": True,
 51 |                 "origin": origin,
 52 |                 "destination": destination
 53 |             })
 54 | 
 55 |     # Extract origin and destination from this Alaska Airlines email
 56 |     if 'Your confirmation receipt:' in email_body:
 57 |         match = re.search(r'\* ([A-Z]{3}) \*.*\* ([A-Z]{3}) \*', email_body)
 58 |         if match:
 59 |             origin = match.group(1)
 60 |             destination = match.group(2)
 61 |             itineraries.append({
 62 |                 "isItinerary": True,
 63 |                 "origin": origin,
 64 |                 "destination": destination
 65 |             })
 66 |     # Extract origin and destination from this Alaska Airlines email
 67 |     if 'Alaska\nFlight' in email_body:
 68 |         match = re.search(r'06:00 AM\n([A-Z]{3}).*\n.*\n02:49 PM\n([A-Z]{3})', email_body)
 69 |         if match:
 70 |             origin = match.group(1)
 71 |             destination = match.group(2)
 72 |             itineraries.append({
 73 |                 "isItinerary": True,
 74 |                 "origin": origin,
 75 |                 "destination": destination
 76 |             })
 77 |     # Extract origin and destination from this JetBlue email
 78 |     if 'Your flight from' in email_body:
 79 |         match = re.search(r'Your flight from ([A-Za-z]+) departs.*\.', email_body)
 80 |         if match:
 81 |             origin = match.group(1)
 82 |             match = re.search(r'Check in for your flight to ([A-Za-z ]+)\.', email_body)
 83 |             if match:
 84 |                 destination = match.group(1)
 85 |                 itineraries.append({
 86 |                     "isItinerary": True,
 87 |                     "origin": origin,
 88 |                     "destination": destination
 89 |                 })
 90 |     # Extract origin and destination from this JetBlue email
 91 |     if 'Your JetBlue confirmation code is' in email_body:
 92 |         match = re.search(r'nowrap style=3D=22font-family: Arial,sans-serif;font-size:16px;padding-bottom:10px;color:=23000064=22>\n                                    ([A-Z]{3}) &nbsp;\n                                    <img src=3D=22https://email=2Ejetblue=2Ecom/assets/responsysimages/content/jetblue/JB_PreTrip_GreenArrow=2Epng=22 width=3D=2212=22 style=3D=22font-family: Arial, sans-serif;  margin: 0 auto; padding: 0;width:12px;=22 border=3D=220=22 alt=3D=22=22/>\n                                    &nbsp;=20\n                                    <span style=3D=22color:=23000064;font-size:16px;font-family: Arial, sans-serif;=22>([A-Z]{3})</span>', email_body)
 93 |         if match:
 94 |             origin = match.group(1)
 95 |             destination = match.group(2)
 96 |             itineraries.append({
 97 |                 "isItinerary": True,
 98 |                 "origin": origin,
 99 |                 "destination": destination
100 |             })
101 |     # Extract origin and destination from this JetBlue email
102 |     if 'Check in for your flight to' in email_body:
103 |         match = re.search(r'Your flight from ([A-Za-z]+) departs.*\.', email_body)
104 |         if match:
105 |             origin = match.group(1)
106 |         match = re.search(r'Check in for your flight to ([A-Za-z]+)\.', email_body)
107 |         if match:
108 |             destination = match.group(1)
109 |             itineraries.append({
110 |                 "isItinerary": True,
111 |                 "origin": origin,
112 |                 "destination": destination
113 |             })
114 |     # Extract origin and destination from this JetBlue email
115 |     if 'Your Flight Itinerary' in email_body:
116 |         match = re.search(r'nowrap style=3D=22font-family: Arial,sans-serif;font-size:16px;padding-bottom:10px;color:=23000064=22>\n                                    ([A-Z]{3}) &nbsp;\n                                    <img src=3D=22https://email=2Ejetblue=2Ecom/assets/responsysimages/content/jetblue/JB_PreTrip_GreenArrow=2Epng=22 width=3D=2212=22 style=3D=22font-family: Arial, sans-serif;  margin: 0 auto; padding: 0;width:12px;=22 border=3D=220=22 alt=3D=22=22/>\n                                    &nbsp;=20\n                                    <span style=3D=22color:=23000064;font-size:16px;font-family: Arial, sans-serif;=22>([A-Z]{3})</span>', email_body)
117 |         if match:
118 |             origin = match.group(1)
119 |             destination = match.group(2)
120 |             itineraries.append({
121 |                 "isItinerary": True,
122 |                 "origin": origin,
123 |                 "destination": destination
124 |             })
125 |     # Extract origin and destination from this JetBlue email
126 |     if 'mi_origin=' in email_body:
127 |         match = re.search(r'mi_origin=3D([A-Z]{3})&', email_body)
128 |         if match:
129 |             origin = match.group(1)
130 |         match = re.search(r'mi_destination=3D([A-Z]{3})&', email_body)
131 |         if match:
132 |             destination = match.group(2)
133 |             itineraries.append({
134 |                 "isItinerary": True,
135 |                 "origin": origin,
136 |                 "destination": destination
137 |             })
138 |     # Extract origin and destination from this JetBlue email
139 |     if 'Just want flights? Put added value on the itinerary with our everyday low fares' in email_body:
140 |         match = re.search(r'mi_origin=3D([A-Z]{3})&', email_body)
141 |         if match:
142 |             origin = match.group(1)
143 |         match = re.search(r'mi_destination=3D([A-Z]{3})&', email_body)
144 |         if match:
145 |             destination = match.group(2)
146 |             itineraries.append({
147 |                 "isItinerary": True,
148 |                 "origin": origin,
149 |                 "destination": destination
150 |             })
151 |     # Extract origin and destination from this Hawaiian Airlines email
152 |     if 'font-size:11pt;scrolling:no;" ><b >OAKLAND</b>' in email_body:
153 |         match = re.search(r'font-size:11pt;scrolling:no;" ><b >([A-Za-z]+)</b>.*font-size:11pt;scrolling:no;" ><b >([A-Za-z]+)</b>', email_body)
154 |         if match:
155 |             origin = match.group(1)
156 |             destination = match.group(2)
157 |             itineraries.append({
158 |                 "isItinerary": True,
159 |                 "origin": origin,
160 |                 "destination": destination
161 |             })
162 |     # Extract origin and destination from this TripIt email
163 |     if 'your trip to' in email_body:
164 |         match = re.search(r'your trip to (.*?) starts', email_body)
165 |         if match:
166 |             destination = match.group(1)
167 |             itineraries.append({
168 |                 "isItinerary": True,
169 |                 "origin": None,
170 |                 "destination": destination
171 |             })
172 | 
173 |     return itineraries
174 | 


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/summarize.py:
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 1 | import dotenv
 2 | dotenv.load_dotenv()
 3 | 
 4 | # llamaindex deps
 5 | from llama_index.core import Settings
 6 | from llama_index.llms.openai import OpenAI
 7 | from llama_index.llms.gemini import Gemini
 8 | import tiktoken
 9 | # from llama_index.core.agent import ReActAgent
10 | # from llama_index.tools.google import GmailToolSpec
11 | 
12 | from gmail import GmailSearcher
13 | 
14 | searcher = GmailSearcher()
15 | 
16 | # dotenv is taking care of the OpenAI API key for us
17 | #MODEL = "gpt-4o"
18 | MODEL = "gpt-3.5-turbo"
19 | Settings.llm = OpenAI(model=MODEL)
20 | 
21 | # Settings.llm = Gemini(
22 | #     model="models/gemini-1.5-pro-latest",
23 | #     temperature=0.1
24 | # )
25 | 
26 | def sliceUntilFits(string, max_tokens):
27 |     enc = tiktoken.encoding_for_model(MODEL)
28 |     while True:
29 |         encoded = enc.encode(string)    
30 |         print(f"Number of tokens: {len(encoded)}")
31 |         if len(encoded) > max_tokens:
32 |             print("Message too long, slicing it down")
33 |             string = string[-10000:] # slice off the last 10000 characters
34 |         else:
35 |             return string
36 | 
37 | 
38 | def summarizeMessages(messages):
39 |     for message in messages:
40 |         print("Handling a message")
41 |         print(message['extra_info'])
42 |         instructions = f"""
43 |             Attached is the body of an email message. If the email is a flight itinerary, summarize the origin and destination of the flight in JSON, like this:
44 |             {{
45 |                 isItinerary: true,
46 |                 origin: "San Francisco, USA",
47 |                 destination: "New York City, USA"
48 |             }}
49 |             If the email is not an itinerary (most will not be), just responsd with:
50 |             {{
51 |                 isItinerary: false
52 |             }}
53 |             Respond with JSON *only*, you do not need triple ticks or any other quoting.
54 |             
55 |             Message is below this line:
56 |             ------------
57 |             {message['text']}
58 |             """
59 |         # openai has a maximum string length it can handle
60 |         instructions = sliceUntilFits(instructions, 128000)
61 |         response = Settings.llm.complete(instructions)
62 |         print(response)
63 | 
64 | next_token = None
65 | while True:
66 |     # TODO: get the LLM to think of good searches
67 |     messageResults = searcher.search_messages(
68 |         "your flight itinerary", 
69 |         max_results=2, 
70 |         next_token=next_token
71 |     )
72 |     summarizeMessages(messageResults['messages'])
73 |     if messageResults['next_token']:
74 |         next_token = messageResults['next_token']
75 |     else:
76 |         break
77 | 


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