class LLM(BaseLLM):    def _call(        self,        prompt: str,        stop: Optional[List[str]] = None,        run_manager: Optional[CallbackManagerForLLMRun] = None,        **kwargs: Any,    ) -> str:        """Run the LLM on the given prompt and input."""
  def _generate(        self,        prompts: List[str],        stop: Optional[List[str]] = None,        run_manager: Optional[CallbackManagerForLLMRun] = None,        **kwargs: Any,    ) -> LLMResult:        """Run the LLM on the given prompt and input."""

class Embeddings(ABC):    """Interface for embedding models."""
    @abstractmethod    def embed_documents(self, texts: List[str]) -> List[List[float]]:        """Embed search docs."""
    @abstractmethod    def embed_query(self, text: str) -> List[float]:

class VectorStore(ABC):    """Interface for vector store."""
    @abstractmethod    def add_texts(        self,        texts: Iterable[str],        metadatas: Optional[List[dict]] = None,        **kwargs: Any,    ) -> List[str]:

    def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:

import osimport jsonimport wgetfrom langchain.vectorstores.analyticdb import AnalyticDB
CONNECTION_STRING = AnalyticDB.connection_string_from_db_params(    driver=os.environ.get("PG_DRIVER", "psycopg2cffi"),    host=os.environ.get("PG_HOST", "localhost"),    port=int(os.environ.get("PG_PORT", "5432")),    database=os.environ.get("PG_DATABASE", "postgres"),    user=os.environ.get("PG_USER", "postgres"),    password=os.environ.get("PG_PASSWORD", "postgres"),)
# All the examples come from https://ai.google.com/research/NaturalQuestions# This is a sample of the training set that we download and extract for some# further processing.wget.download("https://storage.googleapis.com/dataset-natural-questions/questions.json")wget.download("https://storage.googleapis.com/dataset-natural-questions/answers.json")
# 导入数据with open("questions.json", "r") as fp:    questions = json.load(fp)
with open("answers.json", "r") as fp:    answers = json.load(fp)

from langchain.vectorstores import AnalyticDBfrom langchain.embeddings import DashScopeEmbeddingsfrom langchain import VectorDBQA, OpenAI
embeddings = DashScopeEmbeddings(    model="text-embedding-v1", dashscope_api_key="your-dashscope-api-key")
doc_store = AnalyticDB.from_texts(    texts=answers, embedding=embeddings, connection_string=CONNECTION_STRING,    pre_delete_collection=True,)

from langchain.chains import RetrievalQAfrom langchain.llms import Tongyi
os.environ["DASHSCOPE_API_KEY"] = "your-dashscope-api-key"llm = Tongyi()

from langchain.prompts import PromptTemplatecustom_prompt = """Use the following pieces of context to answer the question at the end. Please providea short single-sentence summary answer only. If you don't know the answer or if it'snot present in given context, don't try to make up an answer, but suggest me a randomunrelated song title I could listen to.Context: {context}Question: {question}Helpful Answer:"""
custom_prompt_template = PromptTemplate(    template=custom_prompt, input_variables=["context", "question"]
custom_qa = VectorDBQA.from_chain_type(    llm=llm,    chain_type="stuff",    vectorstore=doc_store,    return_source_documents=False,    chain_type_kwargs={"prompt": custom_prompt_template},)
random.seed(41)for question in random.choices(questions, k=5):    print(">", question)    print(custom_qa.run(question), end="\n\n")

> what was uncle jesse's original last name on full houseUncle Jesse's original last name on Full House was Cochran.
> when did the volcano erupt in indonesia 2018No information about a volcano erupting in Indonesia in 2018 is present in the given context. Suggested song title: "Volcano" by U2.
> what does a dualist way of thinking meanA dualist way of thinking means believing that humans possess a non-physical mind or soul which is distinct from their physical body.
> the first civil service commission in india was set up on the basis of recommendation ofThe first Civil Service Commission in India was not set up on the basis of a recommendation.
> how old do you have to be to get a tattoo in utahIn Utah, you must be at least 18 years old to get a tattoo.

#大标题1-中标题1-小标题1#:内容1#大标题1-中标题1-小标题1#:内容2#大标题1-中标题1-小标题2#:内容1#大标题2-中标题1-小标题1#:内容1

Thought: ...Action: ...Observation: ...... (Repeated many times)

You are {{ai-name}}, {{user-provided AI bot description}}.Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.
GOALS:
1. {{user-provided goal 1}}2. {{user-provided goal 2}}3. ...4. ...5. ...
Constraints:1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.3. No user assistance4. Exclusively use the commands listed in double quotes e.g. "command name"5. Use subprocesses for commands that will not terminate within a few minutes
Commands:1. Google Search: "google", args: "input": "<search>"2. Browse Website: "browse_website", args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"3. Start GPT Agent: "start_agent", args: "name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"4. Message GPT Agent: "message_agent", args: "key": "<key>", "message": "<message>"5. List GPT Agents: "list_agents", args:6. Delete GPT Agent: "delete_agent", args: "key": "<key>"7. Clone Repository: "clone_repository", args: "repository_url": "<url>", "clone_path": "<directory>"8. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"9. Read file: "read_file", args: "file": "<file>"10. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"11. Delete file: "delete_file", args: "file": "<file>"12. Search Files: "search_files", args: "directory": "<directory>"13. Analyze Code: "analyze_code", args: "code": "<full_code_string>"14. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"15. Write Tests: "write_tests", args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"16. Execute Python File: "execute_python_file", args: "file": "<file>"17. Generate Image: "generate_image", args: "prompt": "<prompt>"18. Send Tweet: "send_tweet", args: "text": "<text>"19. Do Nothing: "do_nothing", args:20. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
Resources:1. Internet access for searches and information gathering.2. Long Term memory management.3. GPT-3.5 powered Agents for delegation of simple tasks.4. File output.
Performance Evaluation:1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.2. Constructively self-criticize your big-picture behavior constantly.3. Reflect on past decisions and strategies to refine your approach.4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
You should only respond in JSON format as described belowResponse Format:{    "thoughts": {        "text": "thought",        "reasoning": "reasoning",        "plan": "- short bulleted\n- list that conveys\n- long-term plan",        "criticism": "constructive self-criticism",        "speak": "thoughts summary to say to user"    },    "command": {        "name": "command name",        "args": {            "arg name": "value"        }    }}Ensure the response can be parsed by Python json.loads

class BaseToolkit(BaseModel, ABC):    """Base Toolkit representing a collection of related tools."""
    @abstractmethod    def get_tools(self) -> List[BaseTool]:        """Get the tools in the toolkit."""

class BaseTool(BaseModel, Runnable[Union[str, Dict], Any]):    name: str    """The unique name of the tool that clearly communicates its purpose."""        description: str    """Used to tell the model how/when/why to use the tool.    You can provide few-shot examples as a part of the description.    """    ...
class Tool(BaseTool):    """Tool that takes in function or coroutine directly."""
    description: str = ""    func: Optional[Callable[..., str]]    """The function to run when the tool is called."""

from langchain.agents import initialize_agent, AgentType, Toolfrom langchain.chains import LLMMathChainfrom langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.utilities import SerpAPIWrapperllm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")search = SerpAPIWrapper()llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
tools = [    Tool(        name = "Search",        func=search.run,        description="useful for when you need to answer questions about current events. You should ask targeted questions"    ),    Tool(        name="Calculator",        func=llm_math_chain.run,        description="useful for when you need to answer questions about math"    )]
agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")

    > Entering new  chain...        Invoking: `Search` with `{'query': 'Leo DiCaprio girlfriend'}`            Amidst his casual romance with Gigi, Leo allegedly entered a relationship with 19-year old model, Eden Polani, in February 2023.    Invoking: `Calculator` with `{'expression': '19^0.43'}`    
    > Entering new  chain...    19^0.43```text    19**0.43    ```    ...numexpr.evaluate("19**0.43")...        Answer: 3.547023357958959    > Finished chain.    Answer: 3.547023357958959Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.        > Finished chain.

    "Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55."

_postgres_prompt = """You are a PostgreSQL expert. Given an input question, first create a syntactically correct PostgreSQL query to run, then look at the results of the query and return the answer to the input question.Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL. You can order the results to return the most informative data in the database.Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.Pay attention to use CURRENT_DATE function to get the current date, if the question involves "today".
Use the following format:
Question: Question hereSQLQuery: SQL Query to runSQLResult: Result of the SQLQueryAnswer: Final answer here
"""

## export your openai key first export OPENAI_API_KEY=sk-xxxxx
from langchain.agents import create_sql_agentfrom langchain.agents.agent_toolkits import SQLDatabaseToolkitfrom langchain.agents import AgentExecutorfrom langchain.llms.tongyi import Tongyi
from langchain.sql_database import SQLDatabaseimport psycopg2cffi as psycopg2 # pip install psycopg-binary if on linux, just use psycopg2from langchain.chat_models import ChatOpenAI
db = SQLDatabase.from_uri('postgresql+psycopg2cffi://admin:password123@localhost/admin')

llm = ChatOpenAI(model_name="gpt-3.5-turbo")
toolkit = SQLDatabaseToolkit(db=db,llm=llm)
agent_executor = create_sql_agent(    llm=llm,    toolkit=toolkit,    verbose=True)
agent_executor.run("using the teachers table, find the first_name and last name of teachers who earn less the mean salary?")

> Entering new AgentExecutor chain...Action: sql_db_list_tablesAction Input: ""Observation: teachersThought:I can query the "teachers" table to find the first_name and last_name columns.Action: sql_db_schemaAction Input: "teachers"Observation: CREATE TABLE teachers (        id INTEGER,         first_name VARCHAR(25),         last_name VARCHAR(50),         school VARCHAR(50),         hire_data DATE,         salary NUMERIC)
/*3 rows from teachers table:id      first_name      last_name       school  hire_data       salaryNone    Janet   Smith   F.D. Roosevelt HS       2011-10-30      36200None    Lee     Reynolds        F.D. Roosevelt HS       1993-05-22      65000None    Samuel  Cole    Myers Middle School     2005-08-01      43500*/Thought:I can now construct a query to find the first_name and last_name of teachers who earn less than the mean salary.Action: sql_db_queryAction Input: "SELECT first_name, last_name FROM teachers WHERE salary < (SELECT AVG(salary) FROM teachers) LIMIT 10"Observation: [('Janet', 'Smith'), ('Samuel', 'Cole'), ('Samantha', 'Bush'), ('Betty', 'Diaz'), ('Kathleen', 'Roush')]Thought:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-FDYSniIsv0FIQBi9p4P9Dinn on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-FDYSniIsv0FIQBi9p4P9Dinn on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-FDYSniIsv0FIQBi9p4P9Dinn on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 8.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-FDYSniIsv0FIQBi9p4P9Dinn on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..The first_name and last_name of teachers who earn less than the mean salary are Janet Smith, Samuel Cole, Samantha Bush, Betty Diaz, and Kathleen Roush.Final Answer: Janet Smith, Samuel Cole, Samantha Bush, Betty Diaz, Kathleen Roush
> Finished chain.'Janet Smith, Samuel Cole, Samantha Bush, Betty Diaz, Kathleen Roush'