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LLamaRecipes
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examples_log.txt

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  1. python  main.py
    2. 

  2. Hello! I'm Gmagent, your friendly Gmail assistant. I'm here to help you with any Gmail-related tasks. What can I do for you today? Do you need help checking email, composing a new message, or perhaps something else?
    3. 

  3. 

  4. Your ask: do i have any emails with attachments?
    5. 

  5. 

  6. -------------------------
    7. 

  7. Calling Llama...
    8. 

  8. 

  9. Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment'}}.
    10. 

  10. 

  11. Calling tool to access Gmail API: list_emails, {'query': 'has:attachment'}...
    12. 

  12. 

  13. Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang <gmagent_tester2@gmail.com>', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}, {'message_id': '18fa7f38f83197d3', 'sender': 'LangSmith <noreply@langchain.com>', 'subject': 'New Data Retention policy for LangSmith', 'received_time': '2024-05-23 17:14:22 PDT'}]
    14. 

  14. 

  15. -------------------------
    16. 

  16. 

  17. Gmagent: I found 3 emails matching your query:
    18. 

  18. 1. From: gmagent_tester1@gmail.com, Subject: Fwd: papers to read, Received on: 2024-11-27 10:51:51 PST
    19. 

  19. 2. From: Jeff Tang <gmagent_tester2@gmail.com>, Subject: papers to read, Received on: 2024-11-26 18:44:19 PST
    20. 

  20. 3. From: LangSmith <noreply@langchain.com>, Subject: New Data Retention policy for LangSmith, Received on: 2024-05-23 17:14:22 PDT
    21. 

  21. 

  22. 

  23. Your ask: what's the content of the email from LangSmith
    24. 

  24. 

  25. -------------------------
    26. 

  26. Calling Llama...
    27. 

  27. 

  28. Llama returned: {'function_name': 'get_email_detail', 'parameters': {'detail': 'body', 'which': 'from <sender info>'}}.
    29. 

  29. 

  30. Calling tool to access Gmail API: get_email_detail, {'detail': 'body', 'which': '18fa7f38f83197d3'}...
    31. 

  31. 

  32. Tool calling returned: New Data Retention policy for LangSmith
    33. 

  33. Hi Jeff,
    34. 

  34. You are receiving this email because you have an active account on LangSmith. We are transitioning to a new data retention strategy that limits the length of time trace data is stored in LangSmith.
    35. 

  35. Effective with traces received on or after Wednesday, May 22, all traces stored in LangSmith now have a maximum retention of 400 days from the time of ingest.
    36. 

  36. For more information about this change please visit the LangSmith Docs.
    37. 

  37. If after visiting the docs you still have additional questions, please reach out to LangSmith Support via support@langchain.dev or, if you are an Enterprise plan customer, your dedicated Slack channel.
    38. 

  38. 

  39. -------------------------
    40. 

  40. 

  41. Gmagent: New Data Retention policy for LangSmith
    42. 

  42. Hi Jeff,
    43. 

  43. You are receiving this email because you have an active account on LangSmith. We are transitioning to a new data retention strategy that limits the length of time trace data is stored in LangSmith.
    44. 

  44. Effective with traces received on or after Wednesday, May 22, all traces stored in LangSmith now have a maximum retention of 400 days from the time of ingest.
    45. 

  45. For more information about this change please visit the LangSmith Docs.
    46. 

  46. If after visiting the docs you still have additional questions, please reach out to LangSmith Support via support@langchain.dev or, if you are an Enterprise plan customer, your dedicated Slack channel.
    47. 

  47. 

  48. 

  49. 

  50. 

  51. 

  52. 

  53. 

  54. 

  55. Unsubscribe
    56. 

  56. 

  57. Your ask: do i have emails with attachment larger than 1mb?
    58. 

  58. 

  59. -------------------------
    60. 

  60. Calling Llama...
    61. 

  61. 

  62. Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:1mb'}}.
    63. 

  63. 

  64. Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:1mb'}...
    65. 

  65. 

  66. Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang <gmagent_tester2@gmail.com>', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}]
    67. 

  67. 

  68. -------------------------
    69. 

  69. 

  70. Gmagent: I found 2 emails matching your query:
    71. 

  71. 1. From: gmagent_tester1@gmail.com, Subject: Fwd: papers to read, Received on: 2024-11-27 10:51:51 PST
    72. 

  72. 2. From: Jeff Tang <gmagent_tester2@gmail.com>, Subject: papers to read, Received on: 2024-11-26 18:44:19 PST
    73. 

  73. 

  74. 

  75. Your ask: what kind of attachments for the email with subject papers to read?
    76. 

  76. 

  77. -------------------------
    78. 

  78. Calling Llama...
    79. 

  79. 

  80. Llama returned: {'function_name': 'get_email_detail', 'parameters': {'detail': 'attachment', 'which': 'subject papers to read'}}.
    81. 

  81. 

  82. Calling tool to access Gmail API: get_email_detail, {'detail': 'attachment', 'which': '1936b819706a4923'}...
    83. 

  83. 

  84. Tool calling returned: thinking_llm.pdf - 1115430 bytes
    85. 

  85. logic-of-thought.pdf - 1719126 bytes
    86. 

  86. 

  87. 

  88. -------------------------
    89. 

  89. 

  90. Gmagent: thinking_llm.pdf - 1115430 bytes
    91. 

  91. logic-of-thought.pdf - 1719126 bytes
    92. 

  92. 

  93. 

  94. Your ask: give me a summary of the pdf thinking_llm.pdf
    95. 

  95. 

  96. -------------------------
    97. 

  97. Calling Llama...
    98. 

  98. 

  99. Llama returned: {'function_name': 'get_pdf_summary', 'parameters': {'file_name': 'thinking_llm.pdf'}}.
    100. 

  100. 

  101. Calling tool to access Gmail API: get_pdf_summary, {'file_name': 'thinking_llm.pdf'}...
    102. 

  102. 

  103. PDF text length: 80464
    104. 

  104. 

  105. Calling Llama 31 to generate a summary...
    106. 

  106. 

  107. Tool calling returned: It appears that you've provided several examples of responses generated by a model, along with some analysis and comparisons.
    108. 

  108. 

  109. From what I can see, these examples illustrate various aspects of model performance, including:
    110. 

  110. 

  111. 1. **Inconsistent or incorrect outputs**: In some cases, the model generates responses that are not accurate or consistent (e.g., Examples 17-19).
    112. 

  112. 2. **Self-correction in math tasks**: The model sometimes makes mistakes but then corrects them based on further thought and analysis (e.g., Example 20).
    113. 

  113. 3. **Improved performance with specific prompts**: Using specific prompts can help the model generate more accurate or helpful responses, especially in cases where it initially made a mistake (e.g., Examples 17-19).
    114. 

  114. 4. **Comparison to direct models**: The TPO model's ability to correct its own mistakes and generate more accurate responses is highlighted through comparisons with direct models (e.g., Example 20).
    115. 

  115. 

  116. These examples are likely from the report you mentioned, which explores the performance of a sequence-to-sequence model for generating natural language responses.
    117. 

  117. 

  118. If you'd like to discuss any specific points or ask questions about these examples, I'm here to help!
    119. 

  119. 

  120. -------------------------
    121. 

  121. 

  122. Gmagent: It appears that you've provided several examples of responses generated by a model, along with some analysis and comparisons.
    123. 

  123. 

  124. From what I can see, these examples illustrate various aspects of model performance, including:
    125. 

  125. 

  126. 1. **Inconsistent or incorrect outputs**: In some cases, the model generates responses that are not accurate or consistent (e.g., Examples 17-19).
    127. 

  127. 2. **Self-correction in math tasks**: The model sometimes makes mistakes but then corrects them based on further thought and analysis (e.g., Example 20).
    128. 

  128. 3. **Improved performance with specific prompts**: Using specific prompts can help the model generate more accurate or helpful responses, especially in cases where it initially made a mistake (e.g., Examples 17-19).
    129. 

  129. 4. **Comparison to direct models**: The TPO model's ability to correct its own mistakes and generate more accurate responses is highlighted through comparisons with direct models (e.g., Example 20).
    130. 

  130. 

  131. These examples are likely from the report you mentioned, which explores the performance of a sequence-to-sequence model for generating natural language responses.
    132. 

  132. 

  133. If you'd like to discuss any specific points or ask questions about these examples, I'm here to help!
    134. 

  134. 

  135. Your ask: Draft an email to jeffxtang@meta.com saying working on it and will keep you updated. thanks for your patience.
    136. 

  136. 

  137. -------------------------
    138. 

  138. Calling Llama...
    139. 

  139. 

  140. Llama returned: {'function_name': 'create_draft', 'parameters': {'action': 'new', 'to': 'jeffxtang@meta.com', 'subject': 'Update on [Your Project]', 'body': 'Hi Jeff,\\n\\nI wanted to touch base with you regarding [Your Project]. I am currently working on it and will keep you updated. Thank you for your patience.\\n\\nBest regards,'}}.
    141. 

  141. 

  142. Calling tool to access Gmail API: create_draft, {'action': 'new', 'to': 'jeffxtang@meta.com', 'subject': 'Update on [Your Project]', 'body': 'Hi Jeff,\\n\\nI wanted to touch base with you regarding [Your Project]. I am currently working on it and will keep you updated. Thank you for your patience.\\n\\nBest regards,'}...
    143. 

  143. Draft created with ID: r5930232887125763038
    144. 

  144. 

  145. Tool calling returned: r5930232887125763038
    146. 

  146. 

  147. -------------------------
    148. 

  148. 

  149. Gmagent: Draft created.
    150. 

  150. 

  151. Your ask: send the draft
    152. 

  152. 

  153. -------------------------
    154. 

  154. Calling Llama...
    155. 

  155. 

  156. Llama returned: {'function_name': 'send_draft', 'parameters': {'id': '...'}}.
    157. 

  157. 

  158. Calling tool to access Gmail API: send_draft, {'id': 'r5930232887125763038'}...
    159. 

  159. 

  160. Tool calling returned: Draft sent with email ID: 1936fc3db1993629
    161. 

  161. 

  162. -------------------------
    163. 

  163. 

  164. Gmagent: Draft sent with email ID: 1936fc3db1993629
    165. 

  165. 

  166. Your ask: do i have any emails with attachment larger than 10mb?
    167. 

  167. 

  168. -------------------------
    169. 

  169. Calling Llama...
    170. 

  170. 

  171. Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:10mb'}}.
    172. 

  172. 

  173. Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:10mb'}...
    174. 

  174. 

  175. Tool calling returned: []
    176. 

  176. 

  177. -------------------------
    178. 

  178. 

  179. Gmagent: I couldn't find any such emails. What else would you like to do?
    180. 

  180. 

  181. Your ask: how about 5mb
    182. 

  182. 

  183. -------------------------
    184. 

  184. Calling Llama...
    185. 

  185. 

  186. Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:5mb'}}.
    187. 

  187. 

  188. Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:5mb'}...
    189. 

  189. 

  190. Tool calling returned: [{'message_id': '1936fce85e88a3ef', 'sender': 'Jeff Tang <gmagent_tester2@gmail.com>', 'subject': 'this is an interesting paper', 'received_time': '2024-11-27 14:46:50 PST'}]
    191. 

  191. 

  192. -------------------------
    193. 

  193. 

  194. Gmagent: I found 1 email matching your query:
    195. 

  195. 1. From: Jeff Tang <gmagent_tester2@gmail.com>, Subject: this is an interesting paper, Received on: 2024-11-27 14:46:50 PST
    196. 

  196. 

  197. 

  198. Your ask: reply to the email saying thanks for sharing!
    199. 

  199. 

  200. -------------------------
    201. 

  201. Calling Llama...
    202. 

  202. 

  203. Llama returned: {'function_name': 'send_email', 'parameters': {'action': 'reply', 'to': 'gmagent_tester2@gmail.com', 'subject': 'Re: this is an interesting paper', 'body': 'Thanks for sharing!', 'email_id': '1936fce85e88a3ef'}}.
    204. 

  204. 

  205. Calling tool to access Gmail API: send_email, {'action': 'reply', 'to': 'gmagent_tester2@gmail.com', 'subject': 'Re: this is an interesting paper', 'body': 'Thanks for sharing!', 'email_id': '1936fce85e88a3ef'}...
    206. 

  206. Reply sent. Message ID: 1936fd65f9f24e70
    207. 

  207. 

  208. Tool calling returned: None
    209. 

  209. 

  210. -------------------------
    211. 

  211. 

  212. Gmagent: Email sent.
    213. 

  213. 

  214. Your ask: forward the email to jeffxtang@meta.com
    215. 

  215. 

  216. -------------------------
    217. 

  217. Calling Llama...
    218. 

  218. 

  219. Llama returned: {'function_name': 'send_email', 'parameters': {'action': 'forward', 'to': 'jeffxtang@meta.com', 'subject': 'this is an interesting paper', 'body': '', 'email_id': '1936fce85e88a3ef'}}.
    220. 

  220. 

  221. Calling tool to access Gmail API: send_email, {'action': 'forward', 'to': 'jeffxtang@meta.com', 'subject': 'this is an interesting paper', 'body': '', 'email_id': '1936fce85e88a3ef'}...
    222. 

  222. Message forwarded successfully! Message ID: 1936fdd039c68451
    223. 

  223. 

  224. Tool calling returned: None
    225. 

  225. 

  226. -------------------------
    227. 

  227. 

  228. Gmagent: Email sent.
    229. 

  229. 

  230. Your ask:
    231. 

  231.