Chatgpt plugin

ChatGPTRetrievalPluginClient

Bases: BasePydanticVectorStore

ChatGPT Retrieval Plugin Client.

In this client, we make use of the endpoints defined by ChatGPT.

Parameters:

Name	Type	Description	Default
endpoint_url	str	URL of the ChatGPT Retrieval Plugin.	required
bearer_token	Optional[str]	Bearer token for the ChatGPT Retrieval Plugin.	None
retries	Optional[Retry]	Retry object for the ChatGPT Retrieval Plugin.	None
batch_size	int	Batch size for the ChatGPT Retrieval Plugin.	100

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/llama_index/vector_stores/chatgpt_plugin/base.py

class ChatGPTRetrievalPluginClient(BasePydanticVectorStore):
    """ChatGPT Retrieval Plugin Client.

    In this client, we make use of the endpoints defined by ChatGPT.

    Args:
        endpoint_url (str): URL of the ChatGPT Retrieval Plugin.
        bearer_token (Optional[str]): Bearer token for the ChatGPT Retrieval Plugin.
        retries (Optional[Retry]): Retry object for the ChatGPT Retrieval Plugin.
        batch_size (int): Batch size for the ChatGPT Retrieval Plugin.
    """

    stores_text: bool = True
    is_embedding_query: bool = False

    _endpoint_url: str = PrivateAttr()
    _bearer_token: Optional[str] = PrivateAttr()
    _retries: Optional[Retry] = PrivateAttr()
    _batch_size: int = PrivateAttr()
    _s: requests.Session = PrivateAttr()

    def __init__(
        self,
        endpoint_url: str,
        bearer_token: Optional[str] = None,
        retries: Optional[Retry] = None,
        batch_size: int = 100,
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        super().__init__()

        self._endpoint_url = endpoint_url
        self._bearer_token = bearer_token or os.getenv("BEARER_TOKEN")
        self._retries = retries
        self._batch_size = batch_size

        self._s = requests.Session()
        self._s.mount("http://", HTTPAdapter(max_retries=self._retries))

    @classmethod
    def class_name(cls) -> str:
        return "ChatGPTRetrievalPluginClient"

    @property
    def client(self) -> None:
        """Get client."""
        return

    def add(
        self,
        nodes: List[BaseNode],
        **add_kwargs: Any,
    ) -> List[str]:
        """Add nodes to index."""
        headers = {"Authorization": f"Bearer {self._bearer_token}"}

        docs_to_upload = convert_docs_to_json(nodes)
        iterable_docs = get_tqdm_iterable(
            range(0, len(docs_to_upload), self._batch_size),
            show_progress=True,
            desc="Uploading documents",
        )
        for i in iterable_docs:
            i_end = min(i + self._batch_size, len(docs_to_upload))
            self._s.post(
                f"{self._endpoint_url}/upsert",
                headers=headers,
                json={"documents": docs_to_upload[i:i_end]},
            )

        return [result.node_id for result in nodes]

    def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
        """
        Delete nodes using with ref_doc_id.

        Args:
            ref_doc_id (str): The doc_id of the document to delete.

        """
        headers = {"Authorization": f"Bearer {self._bearer_token}"}
        self._s.post(
            f"{self._endpoint_url}/delete",
            headers=headers,
            json={"ids": [ref_doc_id]},
        )

    def query(
        self,
        query: VectorStoreQuery,
        **kwargs: Any,
    ) -> VectorStoreQueryResult:
        """Get nodes for response."""
        if query.filters is not None:
            raise ValueError("Metadata filters not implemented for ChatGPT Plugin yet.")

        if query.query_str is None:
            raise ValueError("query_str must be provided")
        headers = {"Authorization": f"Bearer {self._bearer_token}"}
        # TODO: add metadata filter
        queries = [{"query": query.query_str, "top_k": query.similarity_top_k}]
        res = requests.post(
            f"{self._endpoint_url}/query", headers=headers, json={"queries": queries}
        )

        nodes = []
        similarities = []
        ids = []
        for query_result in res.json()["results"]:
            for result in query_result["results"]:
                result_id = result["id"]
                result_txt = result["text"]
                result_score = result["score"]
                result_ref_doc_id = result["source_id"]
                node = TextNode(
                    id_=result_id,
                    text=result_txt,
                    relationships={
                        NodeRelationship.SOURCE: RelatedNodeInfo(
                            node_id=result_ref_doc_id
                        )
                    },
                )
                nodes.append(node)
                similarities.append(result_score)
                ids.append(result_id)

            # NOTE: there should only be one query
            break

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

client property

client: None

Get client.

add

add(nodes: List[BaseNode], **add_kwargs: Any) -> List[str]

Add nodes to index.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/llama_index/vector_stores/chatgpt_plugin/base.py

def add(
    self,
    nodes: List[BaseNode],
    **add_kwargs: Any,
) -> List[str]:
    """Add nodes to index."""
    headers = {"Authorization": f"Bearer {self._bearer_token}"}

    docs_to_upload = convert_docs_to_json(nodes)
    iterable_docs = get_tqdm_iterable(
        range(0, len(docs_to_upload), self._batch_size),
        show_progress=True,
        desc="Uploading documents",
    )
    for i in iterable_docs:
        i_end = min(i + self._batch_size, len(docs_to_upload))
        self._s.post(
            f"{self._endpoint_url}/upsert",
            headers=headers,
            json={"documents": docs_to_upload[i:i_end]},
        )

    return [result.node_id for result in nodes]

delete

delete(ref_doc_id: str, **delete_kwargs: Any) -> None

Delete nodes using with ref_doc_id.

Parameters:

Name	Type	Description	Default
ref_doc_id	str	The doc_id of the document to delete.	required

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/llama_index/vector_stores/chatgpt_plugin/base.py

def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
    """
    Delete nodes using with ref_doc_id.

    Args:
        ref_doc_id (str): The doc_id of the document to delete.

    """
    headers = {"Authorization": f"Bearer {self._bearer_token}"}
    self._s.post(
        f"{self._endpoint_url}/delete",
        headers=headers,
        json={"ids": [ref_doc_id]},
    )

query

query(query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult

Get nodes for response.

Source code in llama-index-integrations/vector_stores/llama-index-vector-stores-chatgpt-plugin/llama_index/vector_stores/chatgpt_plugin/base.py

def query(
    self,
    query: VectorStoreQuery,
    **kwargs: Any,
) -> VectorStoreQueryResult:
    """Get nodes for response."""
    if query.filters is not None:
        raise ValueError("Metadata filters not implemented for ChatGPT Plugin yet.")

    if query.query_str is None:
        raise ValueError("query_str must be provided")
    headers = {"Authorization": f"Bearer {self._bearer_token}"}
    # TODO: add metadata filter
    queries = [{"query": query.query_str, "top_k": query.similarity_top_k}]
    res = requests.post(
        f"{self._endpoint_url}/query", headers=headers, json={"queries": queries}
    )

    nodes = []
    similarities = []
    ids = []
    for query_result in res.json()["results"]:
        for result in query_result["results"]:
            result_id = result["id"]
            result_txt = result["text"]
            result_score = result["score"]
            result_ref_doc_id = result["source_id"]
            node = TextNode(
                id_=result_id,
                text=result_txt,
                relationships={
                    NodeRelationship.SOURCE: RelatedNodeInfo(
                        node_id=result_ref_doc_id
                    )
                },
            )
            nodes.append(node)
            similarities.append(result_score)
            ids.append(result_id)

        # NOTE: there should only be one query
        break

    return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)