script

.scripts/community_split/libs/community/langchain_community/agent_toolkits/conversational_retrieval/openai_functions.py

@@ -0,0 +1,88 @@
+from __future__ import annotations
+
+from typing import Any, List, Optional, TYPE_CHECKING
+
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.memory import BaseMemory
+from langchain_core.messages import SystemMessage
+from langchain_core.prompts.chat import MessagesPlaceholder
+from langchain_core.tools import BaseTool
+
+if TYPE_CHECKING:
+    from langchain.agents.agent import AgentExecutor
+
+
+def _get_default_system_message() -> SystemMessage:
+    return SystemMessage(
+        content=(
+            "Do your best to answer the questions. "
+            "Feel free to use any tools available to look up "
+            "relevant information, only if necessary"
+        )
+    )
+
+def create_conversational_retrieval_agent(
+    llm: BaseLanguageModel,
+    tools: List[BaseTool],
+    remember_intermediate_steps: bool = True,
+    memory_key: str = "chat_history",
+    system_message: Optional[SystemMessage] = None,
+    verbose: bool = False,
+    max_token_limit: int = 2000,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """A convenience method for creating a conversational retrieval agent.
+
+    Args:
+        llm: The language model to use, should be ChatOpenAI
+        tools: A list of tools the agent has access to
+        remember_intermediate_steps: Whether the agent should remember intermediate
+            steps or not. Intermediate steps refer to prior action/observation
+            pairs from previous questions. The benefit of remembering these is if
+            there is relevant information in there, the agent can use it to answer
+            follow up questions. The downside is it will take up more tokens.
+        memory_key: The name of the memory key in the prompt.
+        system_message: The system message to use. By default, a basic one will
+            be used.
+        verbose: Whether or not the final AgentExecutor should be verbose or not,
+            defaults to False.
+        max_token_limit: The max number of tokens to keep around in memory.
+            Defaults to 2000.
+
+    Returns:
+        An agent executor initialized appropriately
+    """
+    from langchain.agents.agent import AgentExecutor
+    from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
+        AgentTokenBufferMemory,
+    )
+    from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+    from langchain.memory.token_buffer import ConversationTokenBufferMemory
+
+    if remember_intermediate_steps:
+        memory: BaseMemory = AgentTokenBufferMemory(
+            memory_key=memory_key, llm=llm, max_token_limit=max_token_limit
+        )
+    else:
+        memory = ConversationTokenBufferMemory(
+            memory_key=memory_key,
+            return_messages=True,
+            output_key="output",
+            llm=llm,
+            max_token_limit=max_token_limit,
+        )
+
+    _system_message = system_message or _get_default_system_message()
+    prompt = OpenAIFunctionsAgent.create_prompt(
+        system_message=_system_message,
+        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
+    )
+    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
+    return AgentExecutor(
+        agent=agent,
+        tools=tools,
+        memory=memory,
+        verbose=verbose,
+        return_intermediate_steps=remember_intermediate_steps,
+        **kwargs,
+    )

.scripts/community_split/libs/community/langchain_community/agent_toolkits/json/base.py

@@ -0,0 +1,53 @@
+"""Json agent."""
+from __future__ import annotations
+from typing import Any, Dict, List, Optional, TYPE_CHECKING
+
+from langchain_core.callbacks import BaseCallbackManager
+from langchain_core.language_models import BaseLanguageModel
+
+from langchain_community.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
+from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
+
+if TYPE_CHECKING:
+    from langchain.agents.agent import AgentExecutor
+
+
+def create_json_agent(
+    llm: BaseLanguageModel,
+    toolkit: JsonToolkit,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    prefix: str = JSON_PREFIX,
+    suffix: str = JSON_SUFFIX,
+    format_instructions: Optional[str] = None,
+    input_variables: Optional[List[str]] = None,
+    verbose: bool = False,
+    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Construct a json agent from an LLM and tools."""
+    from langchain.agents.agent import AgentExecutor
+    from langchain.agents.mrkl.base import ZeroShotAgent
+    from langchain.chains.llm import LLMChain
+    tools = toolkit.get_tools()
+    prompt_params = {"format_instructions": format_instructions} if format_instructions is not None else {}
+    prompt = ZeroShotAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        input_variables=input_variables,
+        **prompt_params,
+    )
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        callback_manager=callback_manager,
+    )
+    tool_names = [tool.name for tool in tools]
+    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+    return AgentExecutor.from_agent_and_tools(
+        agent=agent,
+        tools=tools,
+        callback_manager=callback_manager,
+        verbose=verbose,
+        **(agent_executor_kwargs or {}),
+    )

.scripts/community_split/libs/community/langchain_community/agent_toolkits/nla/tool.py

@@ -0,0 +1,57 @@
+"""Tool for interacting with a single API with natural language definition."""
+
+from __future__ import annotations
+from typing import Any, Optional, TYPE_CHECKING
+
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.tools import Tool
+
+from langchain_community.tools.openapi.utils.api_models import APIOperation
+from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec
+from langchain_community.utilities.requests import Requests
+
+if TYPE_CHECKING:
+    from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
+
+
+class NLATool(Tool):
+    """Natural Language API Tool."""
+
+    @classmethod
+    def from_open_api_endpoint_chain(
+        cls, chain: OpenAPIEndpointChain, api_title: str
+    ) -> "NLATool":
+        """Convert an endpoint chain to an API endpoint tool."""
+        expanded_name = (
+            f'{api_title.replace(" ", "_")}.{chain.api_operation.operation_id}'
+        )
+        description = (
+            f"I'm an AI from {api_title}. Instruct what you want,"
+            " and I'll assist via an API with description:"
+            f" {chain.api_operation.description}"
+        )
+        return cls(name=expanded_name, func=chain.run, description=description)
+
+    @classmethod
+    def from_llm_and_method(
+        cls,
+        llm: BaseLanguageModel,
+        path: str,
+        method: str,
+        spec: OpenAPISpec,
+        requests: Optional[Requests] = None,
+        verbose: bool = False,
+        return_intermediate_steps: bool = False,
+        **kwargs: Any,
+    ) -> "NLATool":
+        """Instantiate the tool from the specified path and method."""
+        api_operation = APIOperation.from_openapi_spec(spec, path, method)
+        chain = OpenAPIEndpointChain.from_api_operation(
+            api_operation,
+            llm,
+            requests=requests,
+            verbose=verbose,
+            return_intermediate_steps=return_intermediate_steps,
+            **kwargs,
+        )
+        return cls.from_open_api_endpoint_chain(chain, spec.info.title)

.scripts/community_split/libs/community/langchain_community/agent_toolkits/openapi/base.py

@@ -0,0 +1,77 @@
+"""OpenAPI spec agent."""
+from __future__ import annotations
+from typing import Any, Dict, List, Optional, TYPE_CHECKING
+
+from langchain_core.callbacks import BaseCallbackManager
+from langchain_core.language_models import BaseLanguageModel
+
+from langchain_community.agent_toolkits.openapi.prompt import (
+    OPENAPI_PREFIX,
+    OPENAPI_SUFFIX,
+)
+from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
+
+if TYPE_CHECKING:
+    from langchain.agents.agent import AgentExecutor
+
+
+def create_openapi_agent(
+    llm: BaseLanguageModel,
+    toolkit: OpenAPIToolkit,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    prefix: str = OPENAPI_PREFIX,
+    suffix: str = OPENAPI_SUFFIX,
+    format_instructions: Optional[str] = None,
+    input_variables: Optional[List[str]] = None,
+    max_iterations: Optional[int] = 15,
+    max_execution_time: Optional[float] = None,
+    early_stopping_method: str = "force",
+    verbose: bool = False,
+    return_intermediate_steps: bool = False,
+    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Construct an OpenAPI agent from an LLM and tools.
+
+    *Security Note*: When creating an OpenAPI agent, check the permissions
+        and capabilities of the underlying toolkit.
+
+        For example, if the default implementation of OpenAPIToolkit
+        uses the RequestsToolkit which contains tools to make arbitrary
+        network requests against any URL (e.g., GET, POST, PATCH, PUT, DELETE),
+
+        Control access to who can submit issue requests using this toolkit and
+        what network access it has.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+    from langchain.agents.agent import AgentExecutor
+    from langchain.agents.mrkl.base import ZeroShotAgent
+    from langchain.chains.llm import LLMChain
+    tools = toolkit.get_tools()
+    prompt_params = {"format_instructions": format_instructions} if format_instructions is not None else {}
+    prompt = ZeroShotAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        input_variables=input_variables,
+        **prompt_params
+    )
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        callback_manager=callback_manager,
+    )
+    tool_names = [tool.name for tool in tools]
+    agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+    return AgentExecutor.from_agent_and_tools(
+        agent=agent,
+        tools=tools,
+        callback_manager=callback_manager,
+        verbose=verbose,
+        return_intermediate_steps=return_intermediate_steps,
+        max_iterations=max_iterations,
+        max_execution_time=max_execution_time,
+        early_stopping_method=early_stopping_method,
+        **(agent_executor_kwargs or {}),
+    )