+ "What 5 high-level insights can you infer from the above statements?"
            + " (example format: insight (because of 1, 5, 3))"
        )
        related_memories = self.fetch_memories(topic)
        related_statements = "\n".join(
            [
                f"{i+1}. {memory.page_content}"
                for i, memory in enumerate(related_memories)
            ]
        )
        result = self.chain(prompt).run(
            topic=topic, related_statements=related_statements
        )
        # TODO: Parse the connections between memories and insights
        return self._parse_list(result)
[docs]    def pause_to_reflect(self) -> List[str]:
        """Reflect on recent observations and generate 'insights'."""
        if self.verbose:
            logger.info("Character is reflecting")
        new_insights = []
        topics = self._get_topics_of_reflection()
        for topic in topics:
            insights = self._get_insights_on_topic(topic)
            for insight in insights:
                self.add_memory(insight)
            new_insights.extend(insights)
        return new_insights
    def _score_memory_importance(self, memory_content: str) -> float:
        """Score the absolute importance of the given memory."""
        prompt = PromptTemplate.from_template(
            "On the scale of 1 to 10, where 1 is purely mundane"
            + " (e.g., brushing teeth, making bed) and 10 is"
            + " extremely poignant (e.g., a break up, college"
            + " acceptance), rate the likely poignancy of the"
            + " following piece of memory. Respond with a single integer."
            + "\nMemory: {memory_content}"


+ "\nMemory: {memory_content}"
            + "\nRating: "
        )
        score = self.chain(prompt).run(memory_content=memory_content).strip()
        if self.verbose:
            logger.info(f"Importance score: {score}")
        match = re.search(r"^\D*(\d+)", score)
        if match:
            return (float(score[0]) / 10) * self.importance_weight
        else:
            return 0.0
[docs]    def add_memory(self, memory_content: str) -> List[str]:
        """Add an observation or memory to the agent's memory."""
        importance_score = self._score_memory_importance(memory_content)
        self.aggregate_importance += importance_score
        document = Document(
            page_content=memory_content, metadata={"importance": importance_score}
        )
        result = self.memory_retriever.add_documents([document])
        # After an agent has processed a certain amount of memories (as measured by
        # aggregate importance), it is time to reflect on recent events to add
        # more synthesized memories to the agent's memory stream.
        if (
            self.reflection_threshold is not None
            and self.aggregate_importance > self.reflection_threshold
        ):
            self.pause_to_reflect()
            # Hack to clear the importance from reflection
            self.aggregate_importance = 0.0
        return result
[docs]    def fetch_memories(self, observation: str) -> List[Document]:
        """Fetch related memories."""
        return self.memory_retriever.get_relevant_documents(observation)
    def format_memories_detail(self, relevant_memories: List[Document]) -> str:
        content_strs = set()
        content = []
        for mem in relevant_memories:


content = []
        for mem in relevant_memories:
            if mem.page_content in content_strs:
                continue
            content_strs.add(mem.page_content)
            created_time = mem.metadata["created_at"].strftime("%B %d, %Y, %I:%M %p")
            content.append(f"- {created_time}: {mem.page_content.strip()}")
        return "\n".join([f"{mem}" for mem in content])
    def format_memories_simple(self, relevant_memories: List[Document]) -> str:
        return "; ".join([f"{mem.page_content}" for mem in relevant_memories])
    def _get_memories_until_limit(self, consumed_tokens: int) -> str:
        """Reduce the number of tokens in the documents."""
        result = []
        for doc in self.memory_retriever.memory_stream[::-1]:
            if consumed_tokens >= self.max_tokens_limit:
                break
            consumed_tokens += self.llm.get_num_tokens(doc.page_content)
            if consumed_tokens < self.max_tokens_limit:
                result.append(doc)
        return self.format_memories_simple(result)
    @property
    def memory_variables(self) -> List[str]:
        """Input keys this memory class will load dynamically."""
        return []
[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """Return key-value pairs given the text input to the chain."""
        queries = inputs.get(self.queries_key)
        if queries is not None:
            relevant_memories = [
                mem for query in queries for mem in self.fetch_memories(query)
            ]
            return {
                self.relevant_memories_key: self.format_memories_detail(
                    relevant_memories
                ),


relevant_memories
                ),
                self.relevant_memories_simple_key: self.format_memories_simple(
                    relevant_memories
                ),
            }
        most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
        if most_recent_memories_token is not None:
            return {
                self.most_recent_memories_key: self._get_memories_until_limit(
                    most_recent_memories_token
                )
            }
        return {}
[docs]    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save the context of this model run to memory."""
        # TODO: fix the save memory key
        mem = outputs.get(self.add_memory_key)
        if mem:
            self.add_memory(mem)
[docs]    def clear(self) -> None:
        """Clear memory contents."""
        # TODO
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.experimental.autonomous_agents.baby_agi.baby_agi
"""BabyAGI agent."""
from collections import deque
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.experimental.autonomous_agents.baby_agi.task_creation import (
    TaskCreationChain,
)
from langchain.experimental.autonomous_agents.baby_agi.task_execution import (
    TaskExecutionChain,
)
from langchain.experimental.autonomous_agents.baby_agi.task_prioritization import (
    TaskPrioritizationChain,
)
from langchain.vectorstores.base import VectorStore
[docs]class BabyAGI(Chain, BaseModel):
    """Controller model for the BabyAGI agent."""
    task_list: deque = Field(default_factory=deque)
    task_creation_chain: Chain = Field(...)
    task_prioritization_chain: Chain = Field(...)
    execution_chain: Chain = Field(...)
    task_id_counter: int = Field(1)
    vectorstore: VectorStore = Field(init=False)
    max_iterations: Optional[int] = None
[docs]    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    def add_task(self, task: Dict) -> None:
        self.task_list.append(task)
    def print_task_list(self) -> None:
        print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
        for t in self.task_list:
            print(str(t["task_id"]) + ": " + t["task_name"])


print(str(t["task_id"]) + ": " + t["task_name"])
    def print_next_task(self, task: Dict) -> None:
        print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
        print(str(task["task_id"]) + ": " + task["task_name"])
    def print_task_result(self, result: str) -> None:
        print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
        print(result)
    @property
    def input_keys(self) -> List[str]:
        return ["objective"]
    @property
    def output_keys(self) -> List[str]:
        return []
[docs]    def get_next_task(
        self, result: str, task_description: str, objective: str
    ) -> List[Dict]:
        """Get the next task."""
        task_names = [t["task_name"] for t in self.task_list]
        incomplete_tasks = ", ".join(task_names)
        response = self.task_creation_chain.run(
            result=result,
            task_description=task_description,
            incomplete_tasks=incomplete_tasks,
            objective=objective,
        )
        new_tasks = response.split("\n")
        return [
            {"task_name": task_name} for task_name in new_tasks if task_name.strip()
        ]
[docs]    def prioritize_tasks(self, this_task_id: int, objective: str) -> List[Dict]:
        """Prioritize tasks."""
        task_names = [t["task_name"] for t in list(self.task_list)]
        next_task_id = int(this_task_id) + 1


next_task_id = int(this_task_id) + 1
        response = self.task_prioritization_chain.run(
            task_names=", ".join(task_names),
            next_task_id=str(next_task_id),
            objective=objective,
        )
        new_tasks = response.split("\n")
        prioritized_task_list = []
        for task_string in new_tasks:
            if not task_string.strip():
                continue
            task_parts = task_string.strip().split(".", 1)
            if len(task_parts) == 2:
                task_id = task_parts[0].strip()
                task_name = task_parts[1].strip()
                prioritized_task_list.append(
                    {"task_id": task_id, "task_name": task_name}
                )
        return prioritized_task_list
    def _get_top_tasks(self, query: str, k: int) -> List[str]:
        """Get the top k tasks based on the query."""
        results = self.vectorstore.similarity_search(query, k=k)
        if not results:
            return []
        return [str(item.metadata["task"]) for item in results]
[docs]    def execute_task(self, objective: str, task: str, k: int = 5) -> str:
        """Execute a task."""
        context = self._get_top_tasks(query=objective, k=k)
        return self.execution_chain.run(
            objective=objective, context="\n".join(context), task=task
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run the agent."""
        objective = inputs["objective"]


"""Run the agent."""
        objective = inputs["objective"]
        first_task = inputs.get("first_task", "Make a todo list")
        self.add_task({"task_id": 1, "task_name": first_task})
        num_iters = 0
        while True:
            if self.task_list:
                self.print_task_list()
                # Step 1: Pull the first task
                task = self.task_list.popleft()
                self.print_next_task(task)
                # Step 2: Execute the task
                result = self.execute_task(objective, task["task_name"])
                this_task_id = int(task["task_id"])
                self.print_task_result(result)
                # Step 3: Store the result in Pinecone
                result_id = f"result_{task['task_id']}"
                self.vectorstore.add_texts(
                    texts=[result],
                    metadatas=[{"task": task["task_name"]}],
                    ids=[result_id],
                )
                # Step 4: Create new tasks and reprioritize task list
                new_tasks = self.get_next_task(result, task["task_name"], objective)
                for new_task in new_tasks:
                    self.task_id_counter += 1
                    new_task.update({"task_id": self.task_id_counter})
                    self.add_task(new_task)
                self.task_list = deque(self.prioritize_tasks(this_task_id, objective))
            num_iters += 1
            if self.max_iterations is not None and num_iters == self.max_iterations:
                print(
                    "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
                )
                break
        return {}
[docs]    @classmethod
    def from_llm(


break
        return {}
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        vectorstore: VectorStore,
        verbose: bool = False,
        task_execution_chain: Optional[Chain] = None,
        **kwargs: Dict[str, Any],
    ) -> "BabyAGI":
        """Initialize the BabyAGI Controller."""
        task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
        task_prioritization_chain = TaskPrioritizationChain.from_llm(
            llm, verbose=verbose
        )
        if task_execution_chain is None:
            execution_chain: Chain = TaskExecutionChain.from_llm(llm, verbose=verbose)
        else:
            execution_chain = task_execution_chain
        return cls(
            task_creation_chain=task_creation_chain,
            task_prioritization_chain=task_prioritization_chain,
            execution_chain=execution_chain,
            vectorstore=vectorstore,
            **kwargs,
        )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.experimental.autonomous_agents.autogpt.agent
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.autogpt.output_parser import (
    AutoGPTOutputParser,
    BaseAutoGPTOutputParser,
)
from langchain.experimental.autonomous_agents.autogpt.prompt import AutoGPTPrompt
from langchain.experimental.autonomous_agents.autogpt.prompt_generator import (
    FINISH_NAME,
)
from langchain.schema import (
    AIMessage,
    BaseMessage,
    Document,
    HumanMessage,
    SystemMessage,
)
from langchain.tools.base import BaseTool
from langchain.tools.human.tool import HumanInputRun
from langchain.vectorstores.base import VectorStoreRetriever
[docs]class AutoGPT:
    """Agent class for interacting with Auto-GPT."""
    def __init__(
        self,
        ai_name: str,
        memory: VectorStoreRetriever,
        chain: LLMChain,
        output_parser: BaseAutoGPTOutputParser,
        tools: List[BaseTool],
        feedback_tool: Optional[HumanInputRun] = None,
    ):
        self.ai_name = ai_name
        self.memory = memory
        self.full_message_history: List[BaseMessage] = []
        self.next_action_count = 0
        self.chain = chain
        self.output_parser = output_parser
        self.tools = tools
        self.feedback_tool = feedback_tool
    @classmethod
    def from_llm_and_tools(
        cls,
        ai_name: str,
        ai_role: str,
        memory: VectorStoreRetriever,


ai_role: str,
        memory: VectorStoreRetriever,
        tools: List[BaseTool],
        llm: BaseChatModel,
        human_in_the_loop: bool = False,
        output_parser: Optional[BaseAutoGPTOutputParser] = None,
    ) -> AutoGPT:
        prompt = AutoGPTPrompt(
            ai_name=ai_name,
            ai_role=ai_role,
            tools=tools,
            input_variables=["memory", "messages", "goals", "user_input"],
            token_counter=llm.get_num_tokens,
        )
        human_feedback_tool = HumanInputRun() if human_in_the_loop else None
        chain = LLMChain(llm=llm, prompt=prompt)
        return cls(
            ai_name,
            memory,
            chain,
            output_parser or AutoGPTOutputParser(),
            tools,
            feedback_tool=human_feedback_tool,
        )
    def run(self, goals: List[str]) -> str:
        user_input = (
            "Determine which next command to use, "
            "and respond using the format specified above:"
        )
        # Interaction Loop
        loop_count = 0
        while True:
            # Discontinue if continuous limit is reached
            loop_count += 1
            # Send message to AI, get response
            assistant_reply = self.chain.run(
                goals=goals,
                messages=self.full_message_history,
                memory=self.memory,
                user_input=user_input,
            )
            # Print Assistant thoughts
            print(assistant_reply)
            self.full_message_history.append(HumanMessage(content=user_input))
            self.full_message_history.append(AIMessage(content=assistant_reply))
            # Get command name and arguments


# Get command name and arguments
            action = self.output_parser.parse(assistant_reply)
            tools = {t.name: t for t in self.tools}
            if action.name == FINISH_NAME:
                return action.args["response"]
            if action.name in tools:
                tool = tools[action.name]
                try:
                    observation = tool.run(action.args)
                except ValidationError as e:
                    observation = (
                        f"Validation Error in args: {str(e)}, args: {action.args}"
                    )
                except Exception as e:
                    observation = (
                        f"Error: {str(e)}, {type(e).__name__}, args: {action.args}"
                    )
                result = f"Command {tool.name} returned: {observation}"
            elif action.name == "ERROR":
                result = f"Error: {action.args}. "
            else:
                result = (
                    f"Unknown command '{action.name}'. "
                    f"Please refer to the 'COMMANDS' list for available "
                    f"commands and only respond in the specified JSON format."
                )
            memory_to_add = (
                f"Assistant Reply: {assistant_reply} " f"\nResult: {result} "
            )
            if self.feedback_tool is not None:
                feedback = f"\n{self.feedback_tool.run('Input: ')}"
                if feedback in {"q", "stop"}:
                    print("EXITING")
                    return "EXITING"
                memory_to_add += feedback
            self.memory.add_documents([Document(page_content=memory_to_add)])
            self.full_message_history.append(SystemMessage(content=result))
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.tfidf
"""TF-IDF Retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class TFIDFRetriever(BaseRetriever, BaseModel):
    vectorizer: Any
    docs: List[Document]
    tfidf_array: Any
    k: int = 4
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        tfidf_params: Optional[Dict[str, Any]] = None,
        **kwargs: Any
    ) -> "TFIDFRetriever":
        from sklearn.feature_extraction.text import TfidfVectorizer
        tfidf_params = tfidf_params or {}
        vectorizer = TfidfVectorizer(**tfidf_params)
        tfidf_array = vectorizer.fit_transform(texts)
        docs = [Document(page_content=t) for t in texts]
        return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array, **kwargs)
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        from sklearn.metrics.pairwise import cosine_similarity
        query_vec = self.vectorizer.transform(
            [query]
        )  # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)
        results = cosine_similarity(self.tfidf_array, query_vec).reshape(
            (-1,)


results = cosine_similarity(self.tfidf_array, query_vec).reshape(
            (-1,)
        )  # Op -- (n_docs,1) -- Cosine Sim with each doc
        return_docs = []
        for i in results.argsort()[-self.k :][::-1]:
            return_docs.append(self.docs[i])
        return return_docs
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
    from vespa.application import Vespa
[docs]class VespaRetriever(BaseRetriever):
    def __init__(self, app: Vespa, body: dict, content_field: str):
        self._application = app
        self._query_body = body
        self._content_field = content_field
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        body = self._query_body.copy()
        body["query"] = query
        response = self._application.query(body)
        if not str(response.status_code).startswith("2"):
            raise RuntimeError(
                "Could not retrieve data from Vespa. Error code: {}".format(
                    response.status_code
                )
            )
        root = response.json["root"]
        if "errors" in root:
            raise RuntimeError(json.dumps(root["errors"]))
        hits = []
        for child in response.hits:
            page_content = child["fields"][self._content_field]
            metadata = {"id": child["id"]}
            hits.append(Document(page_content=page_content, metadata=metadata))
        return hits
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.weaviate_hybrid_search
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from pydantic import Extra
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class WeaviateHybridSearchRetriever(BaseRetriever):
    def __init__(
        self,
        client: Any,
        index_name: str,
        text_key: str,
        alpha: float = 0.5,
        k: int = 4,
        attributes: Optional[List[str]] = None,
    ):
        try:
            import weaviate
        except ImportError:
            raise ValueError(
                "Could not import weaviate python package. "
                "Please install it with `pip install weaviate-client`."
            )
        if not isinstance(client, weaviate.Client):
            raise ValueError(
                f"client should be an instance of weaviate.Client, got {type(client)}"
            )
        self._client = client
        self.k = k
        self.alpha = alpha
        self._index_name = index_name
        self._text_key = text_key
        self._query_attrs = [self._text_key]
        if attributes is not None:
            self._query_attrs.extend(attributes)
[docs]    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    # added text_key
[docs]    def add_documents(self, docs: List[Document]) -> List[str]:
        """Upload documents to Weaviate."""
        from weaviate.util import get_valid_uuid


"""Upload documents to Weaviate."""
        from weaviate.util import get_valid_uuid
        with self._client.batch as batch:
            ids = []
            for i, doc in enumerate(docs):
                metadata = doc.metadata or {}
                data_properties = {self._text_key: doc.page_content, **metadata}
                _id = get_valid_uuid(uuid4())
                batch.add_data_object(data_properties, self._index_name, _id)
                ids.append(_id)
        return ids
[docs]    def get_relevant_documents(
        self, query: str, where_filter: Optional[Dict[str, object]] = None
    ) -> List[Document]:
        """Look up similar documents in Weaviate."""
        query_obj = self._client.query.get(self._index_name, self._query_attrs)
        if where_filter:
            query_obj = query_obj.with_where(where_filter)
        result = query_obj.with_hybrid(query, alpha=self.alpha).with_limit(self.k).do()
        if "errors" in result:
            raise ValueError(f"Error during query: {result['errors']}")
        docs = []
        for res in result["data"]["Get"][self._index_name]:
            text = res.pop(self._text_key)
            docs.append(Document(page_content=text, metadata=res))
        return docs
[docs]    async def aget_relevant_documents(
        self, query: str, where_filter: Optional[Dict[str, object]] = None
    ) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChainRetriever(BaseRetriever, BaseModel):
    url: str
    headers: Optional[dict] = None
    input_key: str = "message"
    response_key: str = "response"
    page_content_key: str = "page_content"
    metadata_key: str = "metadata"
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        response = requests.post(
            self.url, json={self.input_key: query}, headers=self.headers
        )
        result = response.json()
        return [
            Document(
                page_content=r[self.page_content_key], metadata=r[self.metadata_key]
            )
            for r in result[self.response_key]
        ]
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        async with aiohttp.ClientSession() as session:
            async with session.request(
                "POST", self.url, headers=self.headers, json={self.input_key: query}
            ) as response:
                result = await response.json()
        return [
            Document(
                page_content=r[self.page_content_key], metadata=r[self.metadata_key]
            )
            for r in result[self.response_key]
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.contextual_compression
"""Retriever that wraps a base retriever and filters the results."""
from typing import List
from pydantic import BaseModel, Extra
from langchain.retrievers.document_compressors.base import (
    BaseDocumentCompressor,
)
from langchain.schema import BaseRetriever, Document
[docs]class ContextualCompressionRetriever(BaseRetriever, BaseModel):
    """Retriever that wraps a base retriever and compresses the results."""
    base_compressor: BaseDocumentCompressor
    """Compressor for compressing retrieved documents."""
    base_retriever: BaseRetriever
    """Base Retriever to use for getting relevant documents."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        """Get documents relevant for a query.
        Args:
            query: string to find relevant documents for
        Returns:
            Sequence of relevant documents
        """
        docs = self.base_retriever.get_relevant_documents(query)
        compressed_docs = self.base_compressor.compress_documents(docs, query)
        return list(compressed_docs)
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        """Get documents relevant for a query.
        Args:
            query: string to find relevant documents for
        Returns:
            List of relevant documents
        """
        docs = await self.base_retriever.aget_relevant_documents(query)
        compressed_docs = await self.base_compressor.acompress_documents(docs, query)
        return list(compressed_docs)
By Harrison Chase


return list(compressed_docs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
    datastore_url: str
    top_k: Optional[int]
    api_key: Optional[str]
    def __init__(
        self,
        datastore_url: str,
        top_k: Optional[int] = None,
        api_key: Optional[str] = None,
    ):
        self.datastore_url = datastore_url
        self.api_key = api_key
        self.top_k = top_k
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        response = requests.post(
            self.datastore_url,
            json={
                "query": query,
                **({"topK": self.top_k} if self.top_k is not None else {}),
            },
            headers={
                "Content-Type": "application/json",
                **(
                    {"Authorization": f"Bearer {self.api_key}"}
                    if self.api_key is not None
                    else {}
                ),
            },
        )
        data = response.json()
        return [
            Document(
                page_content=r["text"],
                metadata={"source": r["source"], "score": r["score"]},
            )
            for r in data["results"]
        ]
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        async with aiohttp.ClientSession() as session:
            async with session.request(
                "POST",
                self.datastore_url,
                json={
                    "query": query,


self.datastore_url,
                json={
                    "query": query,
                    **({"topK": self.top_k} if self.top_k is not None else {}),
                },
                headers={
                    "Content-Type": "application/json",
                    **(
                        {"Authorization": f"Bearer {self.api_key}"}
                        if self.api_key is not None
                        else {}
                    ),
                },
            ) as response:
                data = await response.json()
        return [
            Document(
                page_content=r["text"],
                metadata={"source": r["source"], "score": r["score"]},
            )
            for r in data["results"]
        ]
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def hash_text(text: str) -> str:
    return str(hashlib.sha256(text.encode("utf-8")).hexdigest())
def create_index(
    contexts: List[str],
    index: Any,
    embeddings: Embeddings,
    sparse_encoder: Any,
    ids: Optional[List[str]] = None,
) -> None:
    batch_size = 32
    _iterator = range(0, len(contexts), batch_size)
    try:
        from tqdm.auto import tqdm
        _iterator = tqdm(_iterator)
    except ImportError:
        pass
    if ids is None:
        # create unique ids using hash of the text
        ids = [hash_text(context) for context in contexts]
    for i in _iterator:
        # find end of batch
        i_end = min(i + batch_size, len(contexts))
        # extract batch
        context_batch = contexts[i:i_end]
        batch_ids = ids[i:i_end]
        # add context passages as metadata
        meta = [{"context": context} for context in context_batch]
        # create dense vectors
        dense_embeds = embeddings.embed_documents(context_batch)
        # create sparse vectors
        sparse_embeds = sparse_encoder.encode_documents(context_batch)
        for s in sparse_embeds:
            s["values"] = [float(s1) for s1 in s["values"]]
        vectors = []


vectors = []
        # loop through the data and create dictionaries for upserts
        for doc_id, sparse, dense, metadata in zip(
            batch_ids, sparse_embeds, dense_embeds, meta
        ):
            vectors.append(
                {
                    "id": doc_id,
                    "sparse_values": sparse,
                    "values": dense,
                    "metadata": metadata,
                }
            )
        # upload the documents to the new hybrid index
        index.upsert(vectors)
[docs]class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
    embeddings: Embeddings
    sparse_encoder: Any
    index: Any
    top_k: int = 4
    alpha: float = 0.5
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    def add_texts(self, texts: List[str], ids: Optional[List[str]] = None) -> None:
        create_index(texts, self.index, self.embeddings, self.sparse_encoder, ids=ids)
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        try:
            from pinecone_text.hybrid import hybrid_convex_scale  # noqa:F401
            from pinecone_text.sparse.base_sparse_encoder import (
                BaseSparseEncoder,  # noqa:F401
            )
        except ImportError:
            raise ValueError(
                "Could not import pinecone_text python package. "
                "Please install it with `pip install pinecone_text`."
            )
        return values
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:


[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        from pinecone_text.hybrid import hybrid_convex_scale
        sparse_vec = self.sparse_encoder.encode_queries(query)
        # convert the question into a dense vector
        dense_vec = self.embeddings.embed_query(query)
        # scale alpha with hybrid_scale
        dense_vec, sparse_vec = hybrid_convex_scale(dense_vec, sparse_vec, self.alpha)
        sparse_vec["values"] = [float(s1) for s1 in sparse_vec["values"]]
        # query pinecone with the query parameters
        result = self.index.query(
            vector=dense_vec,
            sparse_vector=sparse_vec,
            top_k=self.top_k,
            include_metadata=True,
        )
        final_result = []
        for res in result["matches"]:
            final_result.append(Document(page_content=res["metadata"]["context"]))
        # return search results as json
        return final_result
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema import BaseRetriever, Document
from langchain.vectorstores.base import VectorStore
def _get_hours_passed(time: datetime, ref_time: datetime) -> float:
    """Get the hours passed between two datetime objects."""
    return (time - ref_time).total_seconds() / 3600
[docs]class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
    """Retriever combining embededing similarity with recency."""
    vectorstore: VectorStore
    """The vectorstore to store documents and determine salience."""
    search_kwargs: dict = Field(default_factory=lambda: dict(k=100))
    """Keyword arguments to pass to the vectorstore similarity search."""
    # TODO: abstract as a queue
    memory_stream: List[Document] = Field(default_factory=list)
    """The memory_stream of documents to search through."""
    decay_rate: float = Field(default=0.01)
    """The exponential decay factor used as (1.0-decay_rate)**(hrs_passed)."""
    k: int = 4
    """The maximum number of documents to retrieve in a given call."""
    other_score_keys: List[str] = []
    """Other keys in the metadata to factor into the score, e.g. 'importance'."""
    default_salience: Optional[float] = None
    """The salience to assign memories not retrieved from the vector store.
    None assigns no salience to documents not fetched from the vector store.
    """
    class Config:


"""
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    def _get_combined_score(
        self,
        document: Document,
        vector_relevance: Optional[float],
        current_time: datetime,
    ) -> float:
        """Return the combined score for a document."""
        hours_passed = _get_hours_passed(
            current_time,
            document.metadata["last_accessed_at"],
        )
        score = (1.0 - self.decay_rate) ** hours_passed
        for key in self.other_score_keys:
            if key in document.metadata:
                score += document.metadata[key]
        if vector_relevance is not None:
            score += vector_relevance
        return score
[docs]    def get_salient_docs(self, query: str) -> Dict[int, Tuple[Document, float]]:
        """Return documents that are salient to the query."""
        docs_and_scores: List[Tuple[Document, float]]
        docs_and_scores = self.vectorstore.similarity_search_with_relevance_scores(
            query, **self.search_kwargs
        )
        results = {}
        for fetched_doc, relevance in docs_and_scores:
            buffer_idx = fetched_doc.metadata["buffer_idx"]
            doc = self.memory_stream[buffer_idx]
            results[buffer_idx] = (doc, relevance)
        return results
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        """Return documents that are relevant to the query."""
        current_time = datetime.now()
        docs_and_scores = {
            doc.metadata["buffer_idx"]: (doc, self.default_salience)
            for doc in self.memory_stream[-self.k :]
        }


for doc in self.memory_stream[-self.k :]
        }
        # If a doc is considered salient, update the salience score
        docs_and_scores.update(self.get_salient_docs(query))
        rescored_docs = [
            (doc, self._get_combined_score(doc, relevance, current_time))
            for doc, relevance in docs_and_scores.values()
        ]
        rescored_docs.sort(key=lambda x: x[1], reverse=True)
        result = []
        # Ensure frequently accessed memories aren't forgotten
        current_time = datetime.now()
        for doc, _ in rescored_docs[: self.k]:
            # TODO: Update vector store doc once `update` method is exposed.
            buffered_doc = self.memory_stream[doc.metadata["buffer_idx"]]
            buffered_doc.metadata["last_accessed_at"] = current_time
            result.append(buffered_doc)
        return result
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        """Return documents that are relevant to the query."""
        raise NotImplementedError
[docs]    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
        """Add documents to vectorstore."""
        current_time = kwargs.get("current_time", datetime.now())
        # Avoid mutating input documents
        dup_docs = [deepcopy(d) for d in documents]
        for i, doc in enumerate(dup_docs):
            if "last_accessed_at" not in doc.metadata:
                doc.metadata["last_accessed_at"] = current_time
            if "created_at" not in doc.metadata:
                doc.metadata["created_at"] = current_time
            doc.metadata["buffer_idx"] = len(self.memory_stream) + i
        self.memory_stream.extend(dup_docs)


self.memory_stream.extend(dup_docs)
        return self.vectorstore.add_documents(dup_docs, **kwargs)
[docs]    async def aadd_documents(
        self, documents: List[Document], **kwargs: Any
    ) -> List[str]:
        """Add documents to vectorstore."""
        current_time = kwargs.get("current_time", datetime.now())
        # Avoid mutating input documents
        dup_docs = [deepcopy(d) for d in documents]
        for i, doc in enumerate(dup_docs):
            if "last_accessed_at" not in doc.metadata:
                doc.metadata["last_accessed_at"] = current_time
            if "created_at" not in doc.metadata:
                doc.metadata["created_at"] = current_time
            doc.metadata["buffer_idx"] = len(self.memory_stream) + i
        self.memory_stream.extend(dup_docs)
        return await self.vectorstore.aadd_documents(dup_docs, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
    def __init__(self, client: Any, params: Optional[dict] = None):
        from metal_sdk.metal import Metal
        if not isinstance(client, Metal):
            raise ValueError(
                "Got unexpected client, should be of type metal_sdk.metal.Metal. "
                f"Instead, got {type(client)}"
            )
        self.client: Metal = client
        self.params = params or {}
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        results = self.client.search({"text": query}, **self.params)
        final_results = []
        for r in results["data"]:
            metadata = {k: v for k, v in r.items() if k != "text"}
            final_results.append(Document(page_content=r["text"], metadata=metadata))
        return final_results
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
    url: str
    bearer_token: str
    top_k: int = 3
    filter: Optional[dict] = None
    aiosession: Optional[aiohttp.ClientSession] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        url, json, headers = self._create_request(query)
        response = requests.post(url, json=json, headers=headers)
        results = response.json()["results"][0]["results"]
        docs = []
        for d in results:
            content = d.pop("text")
            docs.append(Document(page_content=content, metadata=d))
        return docs
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        url, json, headers = self._create_request(query)
        if not self.aiosession:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, headers=headers, json=json) as response:
                    res = await response.json()
        else:
            async with self.aiosession.post(
                url, headers=headers, json=json
            ) as response:
                res = await response.json()
        results = res["results"][0]["results"]
        docs = []
        for d in results:
            content = d.pop("text")


docs = []
        for d in results:
            content = d.pop("text")
            docs.append(Document(page_content=content, metadata=d))
        return docs
    def _create_request(self, query: str) -> tuple[str, dict, dict]:
        url = f"{self.url}/query"
        json = {
            "queries": [
                {
                    "query": query,
                    "filter": self.filter,
                    "top_k": self.top_k,
                }
            ]
        }
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.bearer_token}",
        }
        return url, json, headers
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.svm
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
    with concurrent.futures.ThreadPoolExecutor() as executor:
        return np.array(list(executor.map(embeddings.embed_query, contexts)))
[docs]class SVMRetriever(BaseRetriever, BaseModel):
    embeddings: Embeddings
    index: Any
    texts: List[str]
    k: int = 4
    relevancy_threshold: Optional[float] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    @classmethod
    def from_texts(
        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
    ) -> SVMRetriever:
        index = create_index(texts, embeddings)
        return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        from sklearn import svm
        query_embeds = np.array(self.embeddings.embed_query(query))
        x = np.concatenate([query_embeds[None, ...], self.index])
        y = np.zeros(x.shape[0])
        y[0] = 1
        clf = svm.LinearSVC(


y[0] = 1
        clf = svm.LinearSVC(
            class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
        )
        clf.fit(x, y)
        similarities = clf.decision_function(x)
        sorted_ix = np.argsort(-similarities)
        # svm.LinearSVC in scikit-learn is non-deterministic.
        # if a text is the same as a query, there is no guarantee
        # the query will be in the first index.
        # this performs a simple swap, this works because anything
        # left of the 0 should be equivalent.
        zero_index = np.where(sorted_ix == 0)[0][0]
        if zero_index != 0:
            sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
        denominator = np.max(similarities) - np.min(similarities) + 1e-6
        normalized_similarities = (similarities - np.min(similarities)) / denominator
        top_k_results = []
        for row in sorted_ix[1 : self.k + 1]:
            if (
                self.relevancy_threshold is None
                or normalized_similarities[row] >= self.relevancy_threshold
            ):
                top_k_results.append(Document(page_content=self.texts[row - 1]))
        return top_k_results
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.elastic_search_bm25
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class ElasticSearchBM25Retriever(BaseRetriever):
    """Wrapper around Elasticsearch using BM25 as a retrieval method.
    To connect to an Elasticsearch instance that requires login credentials,
    including Elastic Cloud, use the Elasticsearch URL format
    https://username:password@es_host:9243. For example, to connect to Elastic
    Cloud, create the Elasticsearch URL with the required authentication details and
    pass it to the ElasticVectorSearch constructor as the named parameter
    elasticsearch_url.
    You can obtain your Elastic Cloud URL and login credentials by logging in to the
    Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
    navigating to the "Deployments" page.
    To obtain your Elastic Cloud password for the default "elastic" user:
    1. Log in to the Elastic Cloud console at https://cloud.elastic.co
    2. Go to "Security" > "Users"
    3. Locate the "elastic" user and click "Edit"
    4. Click "Reset password"
    5. Follow the prompts to reset the password
    The format for Elastic Cloud URLs is
    https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
    """
    def __init__(self, client: Any, index_name: str):
        self.client = client
        self.index_name = index_name
[docs]    @classmethod
    def create(


self.index_name = index_name
[docs]    @classmethod
    def create(
        cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
    ) -> ElasticSearchBM25Retriever:
        from elasticsearch import Elasticsearch
        # Create an Elasticsearch client instance
        es = Elasticsearch(elasticsearch_url)
        # Define the index settings and mappings
        settings = {
            "analysis": {"analyzer": {"default": {"type": "standard"}}},
            "similarity": {
                "custom_bm25": {
                    "type": "BM25",
                    "k1": k1,
                    "b": b,
                }
            },
        }
        mappings = {
            "properties": {
                "content": {
                    "type": "text",
                    "similarity": "custom_bm25",  # Use the custom BM25 similarity
                }
            }
        }
        # Create the index with the specified settings and mappings
        es.indices.create(index=index_name, mappings=mappings, settings=settings)
        return cls(es, index_name)
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        refresh_indices: bool = True,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the retriver.
        Args:
            texts: Iterable of strings to add to the retriever.
            refresh_indices: bool to refresh ElasticSearch indices
        Returns:
            List of ids from adding the texts into the retriever.
        """
        try:
            from elasticsearch.helpers import bulk
        except ImportError:
            raise ValueError(
                "Could not import elasticsearch python package. "


raise ValueError(
                "Could not import elasticsearch python package. "
                "Please install it with `pip install elasticsearch`."
            )
        requests = []
        ids = []
        for i, text in enumerate(texts):
            _id = str(uuid.uuid4())
            request = {
                "_op_type": "index",
                "_index": self.index_name,
                "content": text,
                "_id": _id,
            }
            ids.append(_id)
            requests.append(request)
        bulk(self.client, requests)
        if refresh_indices:
            self.client.indices.refresh(index=self.index_name)
        return ids
[docs]    def get_relevant_documents(self, query: str) -> List[Document]:
        query_dict = {"query": {"match": {"content": query}}}
        res = self.client.search(index=self.index_name, body=query_dict)
        docs = []
        for r in res["hits"]["hits"]:
            docs.append(Document(page_content=r["_source"]["content"]))
        return docs
[docs]    async def aget_relevant_documents(self, query: str) -> List[Document]:
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import BasePromptTemplate, LLMChain, PromptTemplate
from langchain.base_language import BaseLanguageModel
from langchain.output_parsers.boolean import BooleanOutputParser
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.retrievers.document_compressors.chain_filter_prompt import (
    prompt_template,
)
from langchain.schema import Document
def _get_default_chain_prompt() -> PromptTemplate:
    return PromptTemplate(
        template=prompt_template,
        input_variables=["question", "context"],
        output_parser=BooleanOutputParser(),
    )
def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
    """Return the compression chain input."""
    return {"question": query, "context": doc.page_content}
[docs]class LLMChainFilter(BaseDocumentCompressor):
    """Filter that drops documents that aren't relevant to the query."""
    llm_chain: LLMChain
    """LLM wrapper to use for filtering documents.
    The chain prompt is expected to have a BooleanOutputParser."""
    get_input: Callable[[str, Document], dict] = default_get_input
    """Callable for constructing the chain input from the query and a Document."""
[docs]    def compress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Filter down documents based on their relevance to the query."""
        filtered_docs = []
        for doc in documents:
            _input = self.get_input(query, doc)
            include_doc = self.llm_chain.predict_and_parse(**_input)


include_doc = self.llm_chain.predict_and_parse(**_input)
            if include_doc:
                filtered_docs.append(doc)
        return filtered_docs
[docs]    async def acompress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Filter down documents."""
        raise NotImplementedError
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: Optional[BasePromptTemplate] = None,
        **kwargs: Any
    ) -> "LLMChainFilter":
        _prompt = prompt if prompt is not None else _get_default_chain_prompt()
        llm_chain = LLMChain(llm=llm, prompt=_prompt)
        return cls(llm_chain=llm_chain, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.document_compressors.chain_extract
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import LLMChain, PromptTemplate
from langchain.base_language import BaseLanguageModel
from langchain.retrievers.document_compressors.base import BaseDocumentCompressor
from langchain.retrievers.document_compressors.chain_extract_prompt import (
    prompt_template,
)
from langchain.schema import BaseOutputParser, Document
def default_get_input(query: str, doc: Document) -> Dict[str, Any]:
    """Return the compression chain input."""
    return {"question": query, "context": doc.page_content}
class NoOutputParser(BaseOutputParser[str]):
    """Parse outputs that could return a null string of some sort."""
    no_output_str: str = "NO_OUTPUT"
    def parse(self, text: str) -> str:
        cleaned_text = text.strip()
        if cleaned_text == self.no_output_str:
            return ""
        return cleaned_text
def _get_default_chain_prompt() -> PromptTemplate:
    output_parser = NoOutputParser()
    template = prompt_template.format(no_output_str=output_parser.no_output_str)
    return PromptTemplate(
        template=template,
        input_variables=["question", "context"],
        output_parser=output_parser,
    )
[docs]class LLMChainExtractor(BaseDocumentCompressor):
    llm_chain: LLMChain
    """LLM wrapper to use for compressing documents."""
    get_input: Callable[[str, Document], dict] = default_get_input
    """Callable for constructing the chain input from the query and a Document."""
[docs]    def compress_documents(


[docs]    def compress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Compress page content of raw documents."""
        compressed_docs = []
        for doc in documents:
            _input = self.get_input(query, doc)
            output = self.llm_chain.predict_and_parse(**_input)
            if len(output) == 0:
                continue
            compressed_docs.append(Document(page_content=output, metadata=doc.metadata))
        return compressed_docs
[docs]    async def acompress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        raise NotImplementedError
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: Optional[PromptTemplate] = None,
        get_input: Optional[Callable[[str, Document], str]] = None,
        llm_chain_kwargs: Optional[dict] = None,
    ) -> LLMChainExtractor:
        """Initialize from LLM."""
        _prompt = prompt if prompt is not None else _get_default_chain_prompt()
        _get_input = get_input if get_input is not None else default_get_input
        llm_chain = LLMChain(llm=llm, prompt=_prompt, **(llm_chain_kwargs or {}))
        return cls(llm_chain=llm_chain, get_input=_get_input)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.document_compressors.embeddings_filter
"""Document compressor that uses embeddings to drop documents unrelated to the query."""
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from pydantic import root_validator
from langchain.document_transformers import (
    _get_embeddings_from_stateful_docs,
    get_stateful_documents,
)
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.retrievers.document_compressors.base import (
    BaseDocumentCompressor,
)
from langchain.schema import Document
[docs]class EmbeddingsFilter(BaseDocumentCompressor):
    embeddings: Embeddings
    """Embeddings to use for embedding document contents and queries."""
    similarity_fn: Callable = cosine_similarity
    """Similarity function for comparing documents. Function expected to take as input
    two matrices (List[List[float]]) and return a matrix of scores where higher values
    indicate greater similarity."""
    k: Optional[int] = 20
    """The number of relevant documents to return. Can be set to None, in which case
    `similarity_threshold` must be specified. Defaults to 20."""
    similarity_threshold: Optional[float]
    """Threshold for determining when two documents are similar enough
    to be considered redundant. Defaults to None, must be specified if `k` is set
    to None."""
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @root_validator()
    def validate_params(cls, values: Dict) -> Dict:
        """Validate similarity parameters."""
        if values["k"] is None and values["similarity_threshold"] is None:
            raise ValueError("Must specify one of `k` or `similarity_threshold`.")
        return values


return values
[docs]    def compress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Filter documents based on similarity of their embeddings to the query."""
        stateful_documents = get_stateful_documents(documents)
        embedded_documents = _get_embeddings_from_stateful_docs(
            self.embeddings, stateful_documents
        )
        embedded_query = self.embeddings.embed_query(query)
        similarity = self.similarity_fn([embedded_query], embedded_documents)[0]
        included_idxs = np.arange(len(embedded_documents))
        if self.k is not None:
            included_idxs = np.argsort(similarity)[::-1][: self.k]
        if self.similarity_threshold is not None:
            similar_enough = np.where(
                similarity[included_idxs] > self.similarity_threshold
            )
            included_idxs = included_idxs[similar_enough]
        return [stateful_documents[i] for i in included_idxs]
[docs]    async def acompress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Filter down documents."""
        raise NotImplementedError
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.retrievers.document_compressors.base
"""Interface for retrieved document compressors."""
from abc import ABC, abstractmethod
from typing import List, Sequence, Union
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
class BaseDocumentCompressor(BaseModel, ABC):
    """Base abstraction interface for document compression."""
    @abstractmethod
    def compress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Compress retrieved documents given the query context."""
    @abstractmethod
    async def acompress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Compress retrieved documents given the query context."""
[docs]class DocumentCompressorPipeline(BaseDocumentCompressor):
    """Document compressor that uses a pipeline of transformers."""
    transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]]
    """List of document filters that are chained together and run in sequence."""
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
[docs]    def compress_documents(
        self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Transform a list of documents."""
        for _transformer in self.transformers:
            if isinstance(_transformer, BaseDocumentCompressor):
                documents = _transformer.compress_documents(documents, query)
            elif isinstance(_transformer, BaseDocumentTransformer):
                documents = _transformer.transform_documents(documents)
            else:
                raise ValueError(f"Got unexpected transformer type: {_transformer}")
        return documents
[docs]    async def acompress_documents(
        self, documents: Sequence[Document], query: str


self, documents: Sequence[Document], query: str
    ) -> Sequence[Document]:
        """Compress retrieved documents given the query context."""
        for _transformer in self.transformers:
            if isinstance(_transformer, BaseDocumentCompressor):
                documents = await _transformer.acompress_documents(documents, query)
            elif isinstance(_transformer, BaseDocumentTransformer):
                documents = await _transformer.atransform_documents(documents)
            else:
                raise ValueError(f"Got unexpected transformer type: {_transformer}")
        return documents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.tools import Tool
from langchain.agents.types import AGENT_TO_CLASS
from langchain.chains.loading import load_chain, load_chain_from_config
from langchain.llms.base import BaseLLM
from langchain.utilities.loading import try_load_from_hub
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
def _load_agent_from_tools(
    config: dict, llm: BaseLLM, tools: List[Tool], **kwargs: Any
) -> BaseSingleActionAgent:
    config_type = config.pop("_type")
    if config_type not in AGENT_TO_CLASS:
        raise ValueError(f"Loading {config_type} agent not supported")
    agent_cls = AGENT_TO_CLASS[config_type]
    combined_config = {**config, **kwargs}
    return agent_cls.from_llm_and_tools(llm, tools, **combined_config)
def load_agent_from_config(
    config: dict,
    llm: Optional[BaseLLM] = None,
    tools: Optional[List[Tool]] = None,
    **kwargs: Any,
) -> BaseSingleActionAgent:
    """Load agent from Config Dict."""
    if "_type" not in config:
        raise ValueError("Must specify an agent Type in config")
    load_from_tools = config.pop("load_from_llm_and_tools", False)
    if load_from_tools:
        if llm is None:
            raise ValueError(
                "If `load_from_llm_and_tools` is set to True, "


"If `load_from_llm_and_tools` is set to True, "
                "then LLM must be provided"
            )
        if tools is None:
            raise ValueError(
                "If `load_from_llm_and_tools` is set to True, "
                "then tools must be provided"
            )
        return _load_agent_from_tools(config, llm, tools, **kwargs)
    config_type = config.pop("_type")
    if config_type not in AGENT_TO_CLASS:
        raise ValueError(f"Loading {config_type} agent not supported")
    agent_cls = AGENT_TO_CLASS[config_type]
    if "llm_chain" in config:
        config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
    elif "llm_chain_path" in config:
        config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
    else:
        raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
    combined_config = {**config, **kwargs}
    return agent_cls(**combined_config)  # type: ignore
[docs]def load_agent(path: Union[str, Path], **kwargs: Any) -> BaseSingleActionAgent:
    """Unified method for loading a agent from LangChainHub or local fs."""
    if hub_result := try_load_from_hub(
        path, _load_agent_from_file, "agents", {"json", "yaml"}
    ):
        return hub_result
    else:
        return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
    file: Union[str, Path], **kwargs: Any
) -> BaseSingleActionAgent:
    """Load agent from file."""
    # Convert file to Path object.


"""Load agent from file."""
    # Convert file to Path object.
    if isinstance(file, str):
        file_path = Path(file)
    else:
        file_path = file
    # Load from either json or yaml.
    if file_path.suffix == ".json":
        with open(file_path) as f:
            config = json.load(f)
    elif file_path.suffix == ".yaml":
        with open(file_path, "r") as f:
            config = yaml.safe_load(f)
    else:
        raise ValueError("File type must be json or yaml")
    # Load the agent from the config now.
    return load_agent_from_config(config, **kwargs)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
from langchain.chains.api.base import APIChain
from langchain.chains.llm_math.base import LLMMathChain
from langchain.chains.pal.base import PALChain
from langchain.llms.base import BaseLLM
from langchain.requests import TextRequestsWrapper
from langchain.tools.arxiv.tool import ArxivQueryRun
from langchain.tools.base import BaseTool
from langchain.tools.bing_search.tool import BingSearchRun
from langchain.tools.ddg_search.tool import DuckDuckGoSearchRun
from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
from langchain.tools.human.tool import HumanInputRun
from langchain.tools.python.tool import PythonREPLTool
from langchain.tools.requests.tool import (
    RequestsDeleteTool,
    RequestsGetTool,
    RequestsPatchTool,
    RequestsPostTool,
    RequestsPutTool,
)
from langchain.tools.searx_search.tool import SearxSearchResults, SearxSearchRun
from langchain.tools.shell.tool import ShellTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.utilities import ArxivAPIWrapper
from langchain.utilities.apify import ApifyWrapper
from langchain.utilities.bash import BashProcess
from langchain.utilities.bing_search import BingSearchAPIWrapper


from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.awslambda import LambdaWrapper
from langchain.utilities.searx_search import SearxSearchWrapper
from langchain.utilities.serpapi import SerpAPIWrapper
from langchain.utilities.wikipedia import WikipediaAPIWrapper
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
def _get_python_repl() -> BaseTool:
    return PythonREPLTool()
def _get_tools_requests_get() -> BaseTool:
    return RequestsGetTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_post() -> BaseTool:
    return RequestsPostTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_patch() -> BaseTool:
    return RequestsPatchTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_put() -> BaseTool:
    return RequestsPutTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_delete() -> BaseTool:
    return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
    return ShellTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
    "python_repl": _get_python_repl,
    "requests": _get_tools_requests_get,  # preserved for backwards compatability
    "requests_get": _get_tools_requests_get,
    "requests_post": _get_tools_requests_post,
    "requests_patch": _get_tools_requests_patch,
    "requests_put": _get_tools_requests_put,
    "requests_delete": _get_tools_requests_delete,


"requests_delete": _get_tools_requests_delete,
    "terminal": _get_terminal,
}
def _get_pal_math(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="PAL-MATH",
        description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
        func=PALChain.from_math_prompt(llm).run,
    )
def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="PAL-COLOR-OBJ",
        description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.",
        func=PALChain.from_colored_object_prompt(llm).run,
    )
def _get_llm_math(llm: BaseLLM) -> BaseTool:
    return Tool(
        name="Calculator",
        description="Useful for when you need to answer questions about math.",
        func=LLMMathChain.from_llm(llm=llm).run,
        coroutine=LLMMathChain.from_llm(llm=llm).arun,
    )
def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:
    chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
    return Tool(
        name="Open Meteo API",
        description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )


func=chain.run,
    )
_LLM_TOOLS: Dict[str, Callable[[BaseLLM], BaseTool]] = {
    "pal-math": _get_pal_math,
    "pal-colored-objects": _get_pal_colored_objects,
    "llm-math": _get_llm_math,
    "open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    news_api_key = kwargs["news_api_key"]
    chain = APIChain.from_llm_and_api_docs(
        llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
    )
    return Tool(
        name="News API",
        description="Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_tmdb_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    tmdb_bearer_token = kwargs["tmdb_bearer_token"]
    chain = APIChain.from_llm_and_api_docs(
        llm,
        tmdb_docs.TMDB_DOCS,
        headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
    )
    return Tool(
        name="TMDB API",
        description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_podcast_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
    listen_api_key = kwargs["listen_api_key"]


listen_api_key = kwargs["listen_api_key"]
    chain = APIChain.from_llm_and_api_docs(
        llm,
        podcast_docs.PODCAST_DOCS,
        headers={"X-ListenAPI-Key": listen_api_key},
    )
    return Tool(
        name="Podcast API",
        description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
        func=chain.run,
    )
def _get_lambda_api(**kwargs: Any) -> BaseTool:
    return Tool(
        name=kwargs["awslambda_tool_name"],
        description=kwargs["awslambda_tool_description"],
        func=LambdaWrapper(**kwargs).run,
    )
def _get_wolfram_alpha(**kwargs: Any) -> BaseTool:
    return WolframAlphaQueryRun(api_wrapper=WolframAlphaAPIWrapper(**kwargs))
def _get_google_search(**kwargs: Any) -> BaseTool:
    return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
    return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
    return ArxivQueryRun(api_wrapper=ArxivAPIWrapper(**kwargs))
def _get_google_serper(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Serper Search",
        func=GoogleSerperAPIWrapper(**kwargs).run,
        description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
    )
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:


)
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
    return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_serpapi(**kwargs: Any) -> BaseTool:
    return Tool(
        name="Search",
        description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
        func=SerpAPIWrapper(**kwargs).run,
        coroutine=SerpAPIWrapper(**kwargs).arun,
    )
def _get_searx_search(**kwargs: Any) -> BaseTool:
    return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
    wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
    return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapper_kwargs), **kwargs)
def _get_bing_search(**kwargs: Any) -> BaseTool:
    return BingSearchRun(api_wrapper=BingSearchAPIWrapper(**kwargs))
def _get_ddg_search(**kwargs: Any) -> BaseTool:
    return DuckDuckGoSearchRun(api_wrapper=DuckDuckGoSearchAPIWrapper(**kwargs))
def _get_human_tool(**kwargs: Any) -> BaseTool:
    return HumanInputRun(**kwargs)
_EXTRA_LLM_TOOLS: Dict[
    str, Tuple[Callable[[Arg(BaseLLM, "llm"), KwArg(Any)], BaseTool], List[str]]
] = {
    "news-api": (_get_news_api, ["news_api_key"]),
    "tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),


"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
    "podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
    "wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
    "google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
    "google-search-results-json": (
        _get_google_search_results_json,
        ["google_api_key", "google_cse_id", "num_results"],
    ),
    "searx-search-results-json": (
        _get_searx_search_results_json,
        ["searx_host", "engines", "num_results", "aiosession"],
    ),
    "bing-search": (_get_bing_search, ["bing_subscription_key", "bing_search_url"]),
    "ddg-search": (_get_ddg_search, []),
    "google-serper": (_get_google_serper, ["serper_api_key"]),
    "serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
    "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
    "wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
    "arxiv": (
        _get_arxiv,
        ["top_k_results", "load_max_docs", "load_all_available_meta"],
    ),
    "human": (_get_human_tool, ["prompt_func", "input_func"]),
    "awslambda": (
        _get_lambda_api,


"awslambda": (
        _get_lambda_api,
        ["awslambda_tool_name", "awslambda_tool_description", "function_name"],
    ),
}
[docs]def load_tools(
    tool_names: List[str],
    llm: Optional[BaseLLM] = None,
    callback_manager: Optional[BaseCallbackManager] = None,
    **kwargs: Any,
) -> List[BaseTool]:
    """Load tools based on their name.
    Args:
        tool_names: name of tools to load.
        llm: Optional language model, may be needed to initialize certain tools.
        callback_manager: Optional callback manager. If not provided, default global callback manager will be used.
    Returns:
        List of tools.
    """
    tools = []
    for name in tool_names:
        if name == "requests":
            warnings.warn(
                "tool name `requests` is deprecated - "
                "please use `requests_all` or specify the requests method"
            )
        if name == "requests_all":
            # expand requests into various methods
            requests_method_tools = [
                _tool for _tool in _BASE_TOOLS if _tool.startswith("requests_")
            ]
            tool_names.extend(requests_method_tools)
        elif name in _BASE_TOOLS:
            tools.append(_BASE_TOOLS[name]())
        elif name in _LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            tool = _LLM_TOOLS[name](llm)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        elif name in _EXTRA_LLM_TOOLS:


tools.append(tool)
        elif name in _EXTRA_LLM_TOOLS:
            if llm is None:
                raise ValueError(f"Tool {name} requires an LLM to be provided")
            _get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[name]
            missing_keys = set(extra_keys).difference(kwargs)
            if missing_keys:
                raise ValueError(
                    f"Tool {name} requires some parameters that were not "
                    f"provided: {missing_keys}"
                )
            sub_kwargs = {k: kwargs[k] for k in extra_keys}
            tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        elif name in _EXTRA_OPTIONAL_TOOLS:
            _get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
            sub_kwargs = {k: kwargs[k] for k in extra_keys if k in kwargs}
            tool = _get_tool_func(**sub_kwargs)
            if callback_manager is not None:
                tool.callback_manager = callback_manager
            tools.append(tool)
        else:
            raise ValueError(f"Got unknown tool {name}")
    return tools
[docs]def get_all_tool_names() -> List[str]:
    """Get a list of all possible tool names."""
    return (
        list(_BASE_TOOLS)
        + list(_EXTRA_OPTIONAL_TOOLS)
        + list(_EXTRA_LLM_TOOLS)
        + list(_LLM_TOOLS)
    )
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 01, 2023.


Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import yaml
from pydantic import BaseModel, root_validator
from langchain.agents.tools import InvalidTool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
    Callbacks,
)
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.input import get_color_mapping
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import (
    AgentAction,
    AgentFinish,
    BaseMessage,
    BaseOutputParser,
)
from langchain.tools.base import BaseTool
from langchain.utilities.asyncio import asyncio_timeout
logger = logging.getLogger(__name__)
[docs]class BaseSingleActionAgent(BaseModel):
    """Base Agent class."""
    @property
    def return_values(self) -> List[str]:
        """Return values of the agent."""
        return ["output"]
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return None
[docs]    @abstractmethod
    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:


**kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
[docs]    @abstractmethod
    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
    @property
    @abstractmethod
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish(
                {"output": "Agent stopped due to iteration limit or time limit."}, ""
            )
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )
[docs]    @classmethod


)
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        **kwargs: Any,
    ) -> BaseSingleActionAgent:
        raise NotImplementedError
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        raise NotImplementedError
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _dict["_type"] = str(self._agent_type)
        return _dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.
        Args:
            file_path: Path to file to save the agent to.
        Example:
        .. code-block:: python
            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        agent_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:


yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {}
[docs]class BaseMultiActionAgent(BaseModel):
    """Base Agent class."""
    @property
    def return_values(self) -> List[str]:
        """Return values of the agent."""
        return ["output"]
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return None
[docs]    @abstractmethod
    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Actions specifying what tool to use.
        """
[docs]    @abstractmethod
    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Actions specifying what tool to use.
        """
    @property
    @abstractmethod
    def input_keys(self) -> List[str]:


@property
    @abstractmethod
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
        else:
            raise ValueError(
                f"Got unsupported early_stopping_method `{early_stopping_method}`"
            )
    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        raise NotImplementedError
[docs]    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of agent."""
        _dict = super().dict()
        _dict["_type"] = str(self._agent_type)
        return _dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the agent.
        Args:
            file_path: Path to file to save the agent to.
        Example:
        .. code-block:: python
            # If working with agent executor
            agent.agent.save(file_path="path/agent.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save


directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        agent_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(agent_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(agent_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {}
[docs]class AgentOutputParser(BaseOutputParser):
[docs]    @abstractmethod
    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
        """Parse text into agent action/finish."""
[docs]class LLMSingleActionAgent(BaseSingleActionAgent):
    llm_chain: LLMChain
    output_parser: AgentOutputParser
    stop: List[str]
    @property
    def input_keys(self) -> List[str]:
        return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
[docs]    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        output = self.llm_chain.run(


"""
        output = self.llm_chain.run(
            intermediate_steps=intermediate_steps,
            stop=self.stop,
            callbacks=callbacks,
            **kwargs,
        )
        return self.output_parser.parse(output)
[docs]    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        output = await self.llm_chain.arun(
            intermediate_steps=intermediate_steps,
            stop=self.stop,
            callbacks=callbacks,
            **kwargs,
        )
        return self.output_parser.parse(output)
[docs]    def tool_run_logging_kwargs(self) -> Dict:
        return {
            "llm_prefix": "",
            "observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
        }
[docs]class Agent(BaseSingleActionAgent):
    """Class responsible for calling the language model and deciding the action.
    This is driven by an LLMChain. The prompt in the LLMChain MUST include
    a variable called "agent_scratchpad" where the agent can put its
    intermediary work.
    """
    llm_chain: LLMChain
    output_parser: AgentOutputParser
    allowed_tools: Optional[List[str]] = None
[docs]    def get_allowed_tools(self) -> Optional[List[str]]:
        return self.allowed_tools
    @property


return self.allowed_tools
    @property
    def return_values(self) -> List[str]:
        return ["output"]
    def _fix_text(self, text: str) -> str:
        """Fix the text."""
        raise ValueError("fix_text not implemented for this agent.")
    @property
    def _stop(self) -> List[str]:
        return [
            f"\n{self.observation_prefix.rstrip()}",
            f"\n\t{self.observation_prefix.rstrip()}",
        ]
    def _construct_scratchpad(
        self, intermediate_steps: List[Tuple[AgentAction, str]]
    ) -> Union[str, List[BaseMessage]]:
        """Construct the scratchpad that lets the agent continue its thought process."""
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
        return thoughts
[docs]    def plan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
        return self.output_parser.parse(full_output)
[docs]    async def aplan(
        self,


[docs]    async def aplan(
        self,
        intermediate_steps: List[Tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
        return self.output_parser.parse(full_output)
[docs]    def get_full_inputs(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Dict[str, Any]:
        """Create the full inputs for the LLMChain from intermediate steps."""
        thoughts = self._construct_scratchpad(intermediate_steps)
        new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
        full_inputs = {**kwargs, **new_inputs}
        return full_inputs
    @property
    def input_keys(self) -> List[str]:
        """Return the input keys.
        :meta private:
        """
        return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
    @root_validator()
    def validate_prompt(cls, values: Dict) -> Dict:
        """Validate that prompt matches format."""
        prompt = values["llm_chain"].prompt
        if "agent_scratchpad" not in prompt.input_variables:
            logger.warning(


if "agent_scratchpad" not in prompt.input_variables:
            logger.warning(
                "`agent_scratchpad` should be a variable in prompt.input_variables."
                " Did not find it, so adding it at the end."
            )
            prompt.input_variables.append("agent_scratchpad")
            if isinstance(prompt, PromptTemplate):
                prompt.template += "\n{agent_scratchpad}"
            elif isinstance(prompt, FewShotPromptTemplate):
                prompt.suffix += "\n{agent_scratchpad}"
            else:
                raise ValueError(f"Got unexpected prompt type {type(prompt)}")
        return values
    @property
    @abstractmethod
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
    @property
    @abstractmethod
    def llm_prefix(self) -> str:
        """Prefix to append the LLM call with."""
[docs]    @classmethod
    @abstractmethod
    def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
        """Create a prompt for this class."""
    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        """Validate that appropriate tools are passed in."""
        for tool in tools:
            if not tool.is_single_input:
                raise ValueError(
                    f"{cls.__name__} does not support multi-input tool {tool.name}."
                )
    @classmethod
    @abstractmethod
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        """Get default output parser for this class."""
[docs]    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],


llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        cls._validate_tools(tools)
        llm_chain = LLMChain(
            llm=llm,
            prompt=cls.create_prompt(tools),
            callback_manager=callback_manager,
        )
        tool_names = [tool.name for tool in tools]
        _output_parser = output_parser or cls._get_default_output_parser()
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            output_parser=_output_parser,
            **kwargs,
        )
[docs]    def return_stopped_response(
        self,
        early_stopping_method: str,
        intermediate_steps: List[Tuple[AgentAction, str]],
        **kwargs: Any,
    ) -> AgentFinish:
        """Return response when agent has been stopped due to max iterations."""
        if early_stopping_method == "force":
            # `force` just returns a constant string
            return AgentFinish(
                {"output": "Agent stopped due to iteration limit or time limit."}, ""
            )
        elif early_stopping_method == "generate":
            # Generate does one final forward pass
            thoughts = ""
            for action, observation in intermediate_steps:
                thoughts += action.log
                thoughts += (
                    f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
                )
            # Adding to the previous steps, we now tell the LLM to make a final pred
            thoughts += (