Seeker with create_agent_from_model_file

[stanleyshly]

I'm trying to adapt the code from #2872 to work with Seeker. Here is the adapted code so far:

from parlai.core.agents import create_agent_from_model_file
seeker_agent = create_agent_from_model_file("zoo:seeker/seeker_dialogue_400M/model")

# forget everything. Important if you run this multiple times.
seeker_agent.reset()

# concatenate the persona and the first thing the human says
first_turn = "\n".join([
    "your persona: I like cheese",
    "your persona: I am from New York City",
    "Hi, where are you from"
])
# Model actually witnesses the human's text
seeker_agent.observe({'text': first_turn, 'episode_done': False})
print(f"You said: {first_turn}")
# model produces a response
response = seeker_agent.act()
print("Seeker replied: {}".format(response['text']))
print()

# now another turn
second_turn = "do you like cheese?"
print(f"You said: {second_turn}")
seeker_agent.observe({'text': second_turn, "episode_done": False})
response2 = seeker_agent.act()
print("Seeker replied: {}".format(response2['text']))
print()
print('-' * 40)
print()
print("Seeker's history view:")
print(seeker_agent.history.get_history_str())

However, I get a KeyError:

You said: your persona: I like cheese
your persona: I am from New York City
Hi, where are you from
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
[<ipython-input-12-56bc8de15941>](https://localhost:8080/#) in <module>()
     13 print(f"You said: {first_turn}")
     14 # model produces a response
---> 15 response = seeker_agent.act()
     16 print("Seeker replied: {}".format(response['text']))
     17 print()

1 frames
[/usr/local/lib/python3.7/dist-packages/parlai/core/torch_agent.py](https://localhost:8080/#) in self_observe(self, self_message)
   1924         # otherwise, we use the last output the model generated
   1925         if self_message is not None:
-> 1926             last_reply = self_message['text']
   1927             self.history.add_reply(last_reply)
   1928             return

KeyError: 'text'

How do adapt this code intended for Blenderbot to work with Seeker?

[stanleyshly]

Passing in opts seems to solve my issue. Here is my working coding:

from parlai.core.agents import create_agent_from_model_file
opt = {
  "beam_disregard_knowledge_for_context_blocking": False,
  "datatype": "valid",
  "drm_beam_block_full_context": True,
  "drm_beam_block_ngram": 3,
  "drm_beam_context_block_ngram": 3,
  "drm_beam_min_length": 20,
  "drm_beam_size": 10,
  "drm_inference": "beam",
  "drm_message_mutators": None,
  "drm_model": "projects.seeker.agents.seeker:ComboFidSearchQueryAgent",
  "exclude_context_in_krm_context_blocking": False,
  "include_knowledge_in_krm_context_blocking": True,
  "inject_query_string": None,
  "knowledge_response_control_token": None,
  "krm_beam_block_ngram": 3,
  "krm_beam_context_block_ngram": 3,
  "krm_beam_min_length": 1,
  "krm_beam_size": 3,
  "krm_doc_chunks_ranker": "woi_chunk_retrieved_docs",
  "krm_inference": "beam",
  "krm_message_mutators": None,
  "krm_model": "projects.seeker.agents.seeker:ComboFidSearchQueryAgent",
  "krm_n_ranked_doc_chunks": 1,
  "krm_rag_retriever_type": "search_engine",
  "krm_search_query_generator_model_file": "''",
  "krm_search_server": "",
  "loglevel": "debug",
  "min_knowledge_length_when_search": 10,
  "model": "projects.seeker.agents.seeker:SeekerAgent",
  "model_file": "zoo:seeker/seeker_dialogue_400M/model",
  "sdm_beam_block_ngram": -1,
  "sdm_beam_min_length": 1,
  "sdm_beam_size": 1,
  "sdm_history_size": 1,
  "sdm_inference": "greedy",
  "sdm_model": "projects.seeker.agents.seeker:ComboFidSearchQueryAgent",
  "search_decision": "always",
  "search_decision_control_token": "__is-search-required__",
  "search_decision_do_search_reply": "__do-search__",
  "search_decision_dont_search_reply": "__do-not-search__",
  "search_query_control_token": "__generate-query__",
  "search_server": "0.0.0.0:8080",
  "sqm_beam_block_ngram": -1,
  "sqm_beam_min_length": 2,
  "sqm_beam_size": 1,
  "sqm_inference": "beam",
  "sqm_model": "projects.seeker.agents.seeker:ComboFidSearchQueryAgent"
}
seeker_agent = create_agent_from_model_file("zoo:seeker/seeker_dialogue_400M/model", opt)

# forget everything. Important if you run this multiple times.
seeker_agent.reset()

# concatenate the persona and the first thing the human says
first_turn = {'text': "hi", 'episode_done': False}
print(first_turn["episode_done"])
# Model actually witnesses the human's text
#seeker_agent.observe({'text': first_turn, 'episode_done': False})
seeker_agent.observe(first_turn)
print(f"You said: {first_turn}")
# model produces a response
response = seeker_agent.act()
print("Seeker replied: {}".format(response['text']))
print()

# now another turn
second_turn = "do you like cheese?"
print(f"You said: {second_turn}")
seeker_agent.observe({'text': second_turn, "episode_done": False})
response2 = seeker_agent.act()
print("Seeker replied: {}".format(response2['text']))
print()
print('-' * 40)
print()
print("Seeker's history view:")
print(seeker_agent.history.get_history_str())