remove double quotes from query

paperqa/agents/main.py

@@ -226,16 +226,16 @@ async def run_agent(
 async def run_fake_agent(
     query: QueryRequest,
     docs: Docs,
-    on_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
-    on_step_callback: (
+    on_env_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
+    on_env_step_callback: (
         Callable[[list[Message], float, bool, bool], Awaitable] | None
     ) = None,
     **env_kwargs,
 ) -> tuple[Answer, AgentStatus]:
     env = Environment(query, docs, **env_kwargs)
     _, tools = await env.reset()
-    if on_reset_callback:
-        await on_reset_callback(env.state)
+    if on_env_reset_callback:
+        await on_env_reset_callback(env.state)
 
     question = env.state.answer.question
     search_tool = next(filter(lambda x: x.info.name == PaperSearch.TOOL_FN_NAME, tools))
@@ -252,8 +252,8 @@ async def step(tool: Tool, **call_kwargs) -> None:
                 tool_calls=[ToolCall.from_tool(tool, **call_kwargs)]
             )
         )
-        if on_step_callback:
-            await on_step_callback(obs, reward, done, truncated)
+        if on_env_step_callback:
+            await on_env_step_callback(obs, reward, done, truncated)
 
     # Seed docs with a few keyword searches
     for search in await litellm_get_search_query(
@@ -282,11 +282,11 @@ async def run_aviary_agent(
     query: QueryRequest,
     docs: Docs,
     agent: ToolSelector,
-    on_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
-    on_action_callback: (
+    on_env_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
+    on_agent_action_callback: (
         Callable[[ToolRequestMessage, BaseModel], Awaitable] | None
     ) = None,
-    on_step_callback: (
+    on_env_step_callback: (
         Callable[[list[Message], float, bool, bool], Awaitable] | None
     ) = None,
     **env_kwargs,
@@ -297,8 +297,8 @@ async def run_aviary_agent(
     try:
         async with asyncio.timeout(query.settings.agent.timeout):
             obs, tools = await env.reset()
-            if on_reset_callback:
-                await on_reset_callback(env.state)
+            if on_env_reset_callback:
+                await on_env_reset_callback(env.state)
 
             agent_state = ToolSelectorLedger(
                 messages=(
@@ -318,12 +318,12 @@ async def run_aviary_agent(
                 agent_state.messages += obs
                 action = await agent(agent_state.messages, tools)
                 agent_state.messages = [*agent_state.messages, action]
-                if on_action_callback:
-                    await on_action_callback(action, agent_state)
+                if on_agent_action_callback:
+                    await on_agent_action_callback(action, agent_state)
 
                 obs, reward, done, truncated = await env.step(action)
-                if on_step_callback:
-                    await on_step_callback(obs, reward, done, truncated)
+                if on_env_step_callback:
+                    await on_env_step_callback(obs, reward, done, truncated)
             status = AgentStatus.SUCCESS  # pylint: disable=redefined-outer-name
     except TimeoutError:
         logger.warning(
@@ -341,9 +341,9 @@ async def run_ldp_agent(
     query: QueryRequest,
     docs: Docs,
     agent: "Agent[SimpleAgentState]",
-    on_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
-    on_action_callback: "Callable[[OpResult[ToolRequestMessage], SimpleAgentState, float], Awaitable] | None" = None,
-    on_step_callback: (
+    on_env_reset_callback: Callable[[EnvironmentState], Awaitable] | None = None,
+    on_agent_action_callback: "Callable[[OpResult[ToolRequestMessage], SimpleAgentState, float], Awaitable] | None" = None,  # noqa: E501
+    on_env_step_callback: (
         Callable[[list[Message], float, bool, bool], Awaitable] | None
     ) = None,
     **env_kwargs,
@@ -354,19 +354,19 @@ async def run_ldp_agent(
     try:
         async with asyncio.timeout(query.settings.agent.timeout):
             obs, tools = await env.reset()
-            if on_reset_callback:
-                await on_reset_callback(env.state)
+            if on_env_reset_callback:
+                await on_env_reset_callback(env.state)
 
             agent_state = await agent.init_state(tools=tools)
 
             while not done:
                 action, agent_state, value = await agent.get_asv(agent_state, obs)
-                if on_action_callback:
-                    await on_action_callback(action, agent_state, value)
+                if on_agent_action_callback:
+                    await on_agent_action_callback(action, agent_state, value)
 
                 obs, reward, done, truncated = await env.step(action.value)
-                if on_step_callback:
-                    await on_step_callback(obs, reward, done, truncated)
+                if on_env_step_callback:
+                    await on_env_step_callback(obs, reward, done, truncated)
             status = AgentStatus.SUCCESS  # pylint: disable=redefined-outer-name
     except TimeoutError:
         logger.warning(

paperqa/configs/contracrow.json

@@ -44,7 +44,7 @@
   },
   "agent": {
     "agent_llm": "gpt-4o-2024-08-06",
-    "agent_type": "OpenAIFunctionsAgent",
+    "agent_type": "ToolSelector",
     "agent_system_prompt": "You are a helpful AI assistant.",
     "agent_prompt": "Answer question: {question}\n\nSearch for papers, gather evidence, collect papers cited in evidence then re-gather evidence, and answer. Gathering evidence will do nothing if you have not done a new search or collected new papers. If you do not have enough evidence to generate a good answer, you can:\n- Search for more papers (preferred)\n- Collect papers cited by previous evidence (preferred)\n- Gather more evidence using a different phrase\nIf you search for more papers or collect new papers cited by previous evidence, remember to gather evidence again. Once you have five or more pieces of evidence from multiple sources, or you have tried a few times, call {gen_answer_tool_name} tool. The {gen_answer_tool_name} tool output is visible to the user, so you do not need to restate the answer and can simply terminate if the answer looks sufficient. The current status of evidence/papers/cost is {status}",
     "search_count": 12,

paperqa/configs/wikicrow.json

@@ -44,7 +44,7 @@
   },
   "agent": {
     "agent_llm": "gpt-4-turbo-2024-04-09",
-    "agent_type": "OpenAIFunctionsAgent",
+    "agent_type": "ToolSelector",
     "agent_system_prompt": "You are a helpful AI assistant.",
     "agent_prompt": "Answer question: {question}\n\nSearch for papers, gather evidence, collect papers cited in evidence then re-gather evidence, and answer. Gathering evidence will do nothing if you have not done a new search or collected new papers. If you do not have enough evidence to generate a good answer, you can:\n- Search for more papers (preferred)\n- Collect papers cited by previous evidence (preferred)\n- Gather more evidence using a different phrase\nIf you search for more papers or collect new papers cited by previous evidence, remember to gather evidence again. Once you have five or more pieces of evidence from multiple sources, or you have tried a few times, call {gen_answer_tool_name} tool. The {gen_answer_tool_name} tool output is visible to the user, so you do not need to restate the answer and can simply terminate if the answer looks sufficient. The current status of evidence/papers/cost is {status}",
     "search_count": 12,