community[minor]: Enable retrieval api calls in PebbloRetrievalQA (#21958)

Description: Enable app discovery and Prompt/Response apis in
PebbloSafeRetrieval
Documentation: NA
Unit test: N/A

---------

Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -3,9 +3,13 @@ Pebblo Retrieval Chain with Identity & Semantic Enforcement for question-answeri
 against a vector database.
 """
 
+import datetime
 import inspect
+import logging
+from http import HTTPStatus
 from typing import Any, Dict, List, Optional
 
+import requests  # type: ignore
 from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain_core.callbacks import (
@@ -22,9 +26,21 @@ from langchain_community.chains.pebblo_retrieval.enforcement_filters import (
     set_enforcement_filters,
 )
 from langchain_community.chains.pebblo_retrieval.models import (
+    App,
     AuthContext,
+    Qa,
     SemanticContext,
 )
+from langchain_community.chains.pebblo_retrieval.utilities import (
+    APP_DISCOVER_URL,
+    CLASSIFIER_URL,
+    PEBBLO_CLOUD_URL,
+    PLUGIN_VERSION,
+    PROMPT_URL,
+    get_runtime,
+)
+
+logger = logging.getLogger(__name__)
 
 
 class PebbloRetrievalQA(Chain):
@@ -46,6 +62,20 @@ class PebbloRetrievalQA(Chain):
     """Authentication context for identity enforcement."""
     semantic_context_key: str = "semantic_context"  #: :meta private:
     """Semantic context for semantic enforcement."""
+    app_name: str  #: :meta private:
+    """App name."""
+    owner: str  #: :meta private:
+    """Owner of app."""
+    description: str  #: :meta private:
+    """Description of app."""
+    api_key: Optional[str] = None  #: :meta private:
+    """Pebblo cloud API key for app."""
+    classifier_url: str = CLASSIFIER_URL  #: :meta private:
+    """Classifier endpoint."""
+    _discover_sent = False  #: :meta private:
+    """Flag to check if discover payload has been sent."""
+    _prompt_sent = False  #: :meta private:
+    """Flag to check if prompt payload has been sent."""
 
     def _call(
         self,
@@ -63,10 +93,11 @@ class PebbloRetrievalQA(Chain):
         res = indexqa({'query': 'This is my query'})
         answer, docs = res['result'], res['source_documents']
         """
+        prompt_time = datetime.datetime.now().isoformat()
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
         question = inputs[self.input_key]
-        auth_context = inputs.get(self.auth_context_key)
-        semantic_context = inputs.get(self.semantic_context_key)
+        auth_context = inputs.get(self.auth_context_key, {})
+        semantic_context = inputs.get(self.semantic_context_key, {})
         accepts_run_manager = (
             "run_manager" in inspect.signature(self._get_docs).parameters
         )
@@ -80,6 +111,32 @@ class PebbloRetrievalQA(Chain):
             input_documents=docs, question=question, callbacks=_run_manager.get_child()
         )
 
+        qa = {
+            "name": self.app_name,
+            "context": [
+                {
+                    "retrieved_from": doc.metadata.get("source"),
+                    "doc": doc.page_content,
+                    "vector_db": self.retriever.vectorstore.__class__.__name__,
+                }
+                for doc in docs
+                if isinstance(doc, Document)
+            ],
+            "prompt": {"data": question},
+            "response": {
+                "data": answer,
+            },
+            "prompt_time": prompt_time,
+            "user": auth_context.user_id if auth_context else "unknown",
+            "user_identities": auth_context.user_auth
+            if "user_auth" in dict(auth_context)
+            else []
+            if auth_context
+            else [],
+        }
+        qa_payload = Qa(**qa)
+        self._send_prompt(qa_payload)
+
         if self.return_source_documents:
             return {self.output_key: answer, "source_documents": docs}
         else:
@@ -158,8 +215,13 @@ class PebbloRetrievalQA(Chain):
     def from_chain_type(
         cls,
         llm: BaseLanguageModel,
+        app_name: str,
+        description: str,
+        owner: str,
         chain_type: str = "stuff",
         chain_type_kwargs: Optional[dict] = None,
+        api_key: Optional[str] = None,
+        classifier_url: str = CLASSIFIER_URL,
         **kwargs: Any,
     ) -> "PebbloRetrievalQA":
         """Load chain from chain type."""
@@ -169,7 +231,29 @@ class PebbloRetrievalQA(Chain):
         combine_documents_chain = load_qa_chain(
             llm, chain_type=chain_type, **_chain_type_kwargs
         )
-        return cls(combine_documents_chain=combine_documents_chain, **kwargs)
+
+        # generate app
+        app = PebbloRetrievalQA._get_app_details(
+            app_name=app_name,
+            description=description,
+            owner=owner,
+            llm=llm,
+            **kwargs,
+        )
+
+        PebbloRetrievalQA._send_discover(
+            app, api_key=api_key, classifier_url=classifier_url
+        )
+
+        return cls(
+            combine_documents_chain=combine_documents_chain,
+            app_name=app_name,
+            owner=owner,
+            description=description,
+            api_key=api_key,
+            classifier_url=classifier_url,
+            **kwargs,
+        )
 
     @validator("retriever", pre=True, always=True)
     def validate_vectorstore(
@@ -216,3 +300,182 @@ class PebbloRetrievalQA(Chain):
         return await self.retriever.aget_relevant_documents(
             question, callbacks=run_manager.get_child()
         )
+
+    @staticmethod
+    def _get_app_details(app_name, owner, description, llm, **kwargs) -> App:  # type: ignore
+        """Fetch app details. Internal method.
+        Returns:
+            App: App details.
+        """
+        framework, runtime = get_runtime()
+        chains = PebbloRetrievalQA.get_chain_details(llm, **kwargs)
+        app = App(
+            name=app_name,
+            owner=owner,
+            description=description,
+            runtime=runtime,
+            framework=framework,
+            chains=chains,
+            plugin_version=PLUGIN_VERSION,
+        )
+        return app
+
+    @staticmethod
+    def _send_discover(app, api_key, classifier_url) -> None:  # type: ignore
+        """Send app discovery payload to pebblo-server. Internal method."""
+        headers = {
+            "Accept": "application/json",
+            "Content-Type": "application/json",
+        }
+        payload = app.dict(exclude_unset=True)
+        app_discover_url = f"{classifier_url}{APP_DISCOVER_URL}"
+        try:
+            pebblo_resp = requests.post(
+                app_discover_url, headers=headers, json=payload, timeout=20
+            )
+            logger.debug("discover-payload: %s", payload)
+            logger.debug(
+                "send_discover[local]: request url %s, body %s len %s\
+                    response status %s body %s",
+                pebblo_resp.request.url,
+                str(pebblo_resp.request.body),
+                str(len(pebblo_resp.request.body if pebblo_resp.request.body else [])),
+                str(pebblo_resp.status_code),
+                pebblo_resp.json(),
+            )
+            if pebblo_resp.status_code in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]:
+                PebbloRetrievalQA.set_discover_sent()
+            else:
+                logger.warning(
+                    f"Received unexpected HTTP response code: {pebblo_resp.status_code}"
+                )
+        except requests.exceptions.RequestException:
+            logger.warning("Unable to reach pebblo server.")
+        except Exception as e:
+            logger.warning("An Exception caught in _send_discover: local %s", e)
+
+        if api_key:
+            try:
+                headers.update({"x-api-key": api_key})
+                pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{APP_DISCOVER_URL}"
+                pebblo_cloud_response = requests.post(
+                    pebblo_cloud_url, headers=headers, json=payload, timeout=20
+                )
+
+                logger.debug(
+                    "send_discover[cloud]: request url %s, body %s len %s\
+                        response status %s body %s",
+                    pebblo_cloud_response.request.url,
+                    str(pebblo_cloud_response.request.body),
+                    str(
+                        len(
+                            pebblo_cloud_response.request.body
+                            if pebblo_cloud_response.request.body
+                            else []
+                        )
+                    ),
+                    str(pebblo_cloud_response.status_code),
+                    pebblo_cloud_response.json(),
+                )
+            except requests.exceptions.RequestException:
+                logger.warning("Unable to reach Pebblo cloud server.")
+            except Exception as e:
+                logger.warning("An Exception caught in _send_discover: cloud %s", e)
+
+    @classmethod
+    def set_discover_sent(cls) -> None:
+        cls._discover_sent = True
+
+    @classmethod
+    def set_prompt_sent(cls) -> None:
+        cls._prompt_sent = True
+
+    def _send_prompt(self, qa_payload: Qa) -> None:
+        headers = {
+            "Accept": "application/json",
+            "Content-Type": "application/json",
+        }
+        app_discover_url = f"{self.classifier_url}{PROMPT_URL}"
+        try:
+            pebblo_resp = requests.post(
+                app_discover_url, headers=headers, json=qa_payload.dict(), timeout=20
+            )
+            logger.debug("prompt-payload: %s", qa_payload)
+            logger.debug(
+                "send_prompt[local]: request url %s, body %s len %s\
+                    response status %s body %s",
+                pebblo_resp.request.url,
+                str(pebblo_resp.request.body),
+                str(len(pebblo_resp.request.body if pebblo_resp.request.body else [])),
+                str(pebblo_resp.status_code),
+                pebblo_resp.json(),
+            )
+            if pebblo_resp.status_code in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]:
+                PebbloRetrievalQA.set_prompt_sent()
+            else:
+                logger.warning(
+                    f"Received unexpected HTTP response code: {pebblo_resp.status_code}"
+                )
+        except requests.exceptions.RequestException:
+            logger.warning("Unable to reach pebblo server.")
+        except Exception as e:
+            logger.warning("An Exception caught in _send_discover: local %s", e)
+
+        if self.api_key:
+            try:
+                headers.update({"x-api-key": self.api_key})
+                pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{PROMPT_URL}"
+                pebblo_cloud_response = requests.post(
+                    pebblo_cloud_url,
+                    headers=headers,
+                    json=qa_payload.dict(),
+                    timeout=20,
+                )
+
+                logger.debug(
+                    "send_prompt[cloud]: request url %s, body %s len %s\
+                        response status %s body %s",
+                    pebblo_cloud_response.request.url,
+                    str(pebblo_cloud_response.request.body),
+                    str(
+                        len(
+                            pebblo_cloud_response.request.body
+                            if pebblo_cloud_response.request.body
+                            else []
+                        )
+                    ),
+                    str(pebblo_cloud_response.status_code),
+                    pebblo_cloud_response.json(),
+                )
+            except requests.exceptions.RequestException:
+                logger.warning("Unable to reach Pebblo cloud server.")
+            except Exception as e:
+                logger.warning("An Exception caught in _send_prompt: cloud %s", e)
+
+    @classmethod
+    def get_chain_details(cls, llm, **kwargs):  # type: ignore
+        llm_dict = llm.__dict__
+        chain = [
+            {
+                "name": cls.__name__,
+                "model": {
+                    "name": llm_dict.get("model_name", llm_dict.get("model")),
+                    "vendor": llm.__class__.__name__,
+                },
+                "vector_dbs": [
+                    {
+                        "name": kwargs["retriever"].vectorstore.__class__.__name__,
+                        "embedding_model": str(
+                            kwargs["retriever"].vectorstore._embeddings.model
+                        )
+                        if hasattr(kwargs["retriever"].vectorstore, "_embeddings")
+                        else (
+                            str(kwargs["retriever"].vectorstore._embedding.model)
+                            if hasattr(kwargs["retriever"].vectorstore, "_embedding")
+                            else None
+                        ),
+                    }
+                ],
+            }
+        ]
+        return chain

libs/community/langchain_community/chains/pebblo_retrieval/enforcement_filters.py

@@ -1,13 +1,13 @@
 """
 Identity & Semantic Enforcement filters for PebbloRetrievalQA chain:
 
-This module contains methods for applying Identity and Semantic Enforcement filters 
-in the PebbloRetrievalQA chain. 
-These filters are used to control the retrieval of documents based on authorization and 
-semantic context. 
-The Identity Enforcement filter ensures that only authorized identities can access 
-certain documents, while the Semantic Enforcement filter controls document retrieval 
-based on semantic context. 
+This module contains methods for applying Identity and Semantic Enforcement filters
+in the PebbloRetrievalQA chain.
+These filters are used to control the retrieval of documents based on authorization and
+semantic context.
+The Identity Enforcement filter ensures that only authorized identities can access
+certain documents, while the Semantic Enforcement filter controls document retrieval
+based on semantic context.
 
 The methods in this module are designed to work with different types of vector stores.
 """

libs/community/langchain_community/chains/pebblo_retrieval/models.py

@@ -60,3 +60,86 @@ class ChainInput(BaseModel):
         base_dict["auth_context"] = self.auth_context
         base_dict["semantic_context"] = self.semantic_context
         return base_dict
+
+
+class Runtime(BaseModel):
+    """
+    OS, language details
+    """
+
+    type: Optional[str] = ""
+    host: str
+    path: str
+    ip: Optional[str] = ""
+    platform: str
+    os: str
+    os_version: str
+    language: str
+    language_version: str
+    runtime: Optional[str] = ""
+
+
+class Framework(BaseModel):
+    """
+    Langchain framework details
+    """
+
+    name: str
+    version: str
+
+
+class Model(BaseModel):
+    vendor: Optional[str]
+    name: Optional[str]
+
+
+class PkgInfo(BaseModel):
+    project_home_page: Optional[str]
+    documentation_url: Optional[str]
+    pypi_url: Optional[str]
+    liscence_type: Optional[str]
+    installed_via: Optional[str]
+    location: Optional[str]
+
+
+class VectorDB(BaseModel):
+    name: Optional[str] = None
+    version: Optional[str] = None
+    location: Optional[str] = None
+    embedding_model: Optional[str] = None
+
+
+class Chains(BaseModel):
+    name: str
+    model: Optional[Model]
+    vector_dbs: Optional[List[VectorDB]]
+
+
+class App(BaseModel):
+    name: str
+    owner: str
+    description: Optional[str]
+    runtime: Runtime
+    framework: Framework
+    chains: List[Chains]
+    plugin_version: str
+
+
+class Context(BaseModel):
+    retrieved_from: Optional[str]
+    doc: Optional[str]
+    vector_db: str
+
+
+class Prompt(BaseModel):
+    data: str
+
+
+class Qa(BaseModel):
+    name: str
+    context: List[Optional[Context]]
+    prompt: Prompt
+    response: Prompt
+    prompt_time: str
+    user: str
+    user_identities: Optional[List[str]]

libs/community/langchain_community/chains/pebblo_retrieval/utilities.py

@@ -0,0 +1,65 @@
+import logging
+import os
+import platform
+from typing import Tuple
+
+from langchain_core.env import get_runtime_environment
+
+from langchain_community.chains.pebblo_retrieval.models import Framework, Runtime
+
+logger = logging.getLogger(__name__)
+
+PLUGIN_VERSION = "0.1.1"
+
+CLASSIFIER_URL = os.getenv("PEBBLO_CLASSIFIER_URL", "http://localhost:8000")
+PEBBLO_CLOUD_URL = os.getenv("PEBBLO_CLOUD_URL", "https://api.daxa.ai")
+
+PROMPT_URL = "/v1/prompt"
+APP_DISCOVER_URL = "/v1/app/discover"
+
+
+def get_runtime() -> Tuple[Framework, Runtime]:
+    """Fetch the current Framework and Runtime details.
+
+    Returns:
+        Tuple[Framework, Runtime]: Framework and Runtime for the current app instance.
+    """
+    runtime_env = get_runtime_environment()
+    framework = Framework(
+        name="langchain", version=runtime_env.get("library_version", None)
+    )
+    uname = platform.uname()
+    runtime = Runtime(
+        host=uname.node,
+        path=os.environ["PWD"],
+        platform=runtime_env.get("platform", "unknown"),
+        os=uname.system,
+        os_version=uname.version,
+        ip=get_ip(),
+        language=runtime_env.get("runtime", "unknown"),
+        language_version=runtime_env.get("runtime_version", "unknown"),
+    )
+
+    if "Darwin" in runtime.os:
+        runtime.type = "desktop"
+        runtime.runtime = "Mac OSX"
+
+    logger.debug(f"framework {framework}")
+    logger.debug(f"runtime {runtime}")
+    return framework, runtime
+
+
+def get_ip() -> str:
+    """Fetch local runtime ip address.
+
+    Returns:
+        str: IP address
+    """
+    import socket  # lazy imports
+
+    host = socket.gethostname()
+    try:
+        public_ip = socket.gethostbyname(host)
+    except Exception:
+        public_ip = socket.gethostbyname("localhost")
+    return public_ip

libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py

@@ -72,7 +72,12 @@ def pebblo_retrieval_qa(retriever: FakeRetriever) -> PebbloRetrievalQA:
     Create a PebbloRetrievalQA instance
     """
     pebblo_retrieval_qa = PebbloRetrievalQA.from_chain_type(
-        llm=FakeLLM(), chain_type="stuff", retriever=retriever
+        llm=FakeLLM(),
+        chain_type="stuff",
+        retriever=retriever,
+        owner="owner",
+        description="description",
+        app_name="app_name",
     )
 
     return pebblo_retrieval_qa
@@ -114,6 +119,9 @@ def test_validate_vectorstore(
         llm=FakeLLM(),
         chain_type="stuff",
         retriever=retriever,
+        owner="owner",
+        description="description",
+        app_name="app_name",
     )
 
     # validate_vectorstore method should raise a ValueError for unsupported vectorstores
@@ -122,6 +130,9 @@ def test_validate_vectorstore(
             llm=FakeLLM(),
             chain_type="stuff",
             retriever=unsupported_retriever,
+            owner="owner",
+            description="description",
+            app_name="app_name",
         )
     assert (
         "Vectorstore must be an instance of one of the supported vectorstores"