test: quivr server install and upload sample linkedin data

Author: PurposeAchiever6

backend/core/.env.apitest

@@ -0,0 +1 @@
+OPENAI_API_KEY="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"

backend/core/babbages_calculating_engine.txt

@@ -42,9 +42,16 @@ def checkGithub(self):
         else:
             return False
 
+    def checkLinkedIn(self):
+        if "linkedin.com" in self.url:
+            return True
+        else:
+            return False
+
 
 def slugify(text):
-    text = unicodedata.normalize("NFKD", text).encode("ascii", "ignore").decode("utf-8")
+    text = unicodedata.normalize("NFKD", text).encode(
+        "ascii", "ignore").decode("utf-8")
     text = re.sub(r"[^\w\s-]", "", text).strip().lower()
     text = re.sub(r"[-\s]+", "-", text)
     return text

backend/core/crawl/crawler.py

@@ -22,9 +22,6 @@ Authorization: Bearer {api_key}
 
 Replace `{api_key}` with the generated API key obtained from the frontend
 
-You can find more information in the [Authentication](/docs/backend/api/getting_started) section of the documentation.
-
-
 
 # How to use the API
 

backend/core/doc/api_doc.md

@@ -0,0 +1,19 @@
+## API authentication
+
+## Personality
+ - Generate questions
+ - Assess personality from Test Results
+
+## Brain
+ - Creat new brain
+ - Get all brains
+
+## Upload
+ - File Upload
+
+## Crawl
+ - Website crawl
+
+## Chat
+ - create new chat room
+

backend/core/doc/openapi_test_direction.md

@@ -169,7 +169,7 @@ async def _acall_chain(self, chain, question, history):
             }
         )
 
-    async def generate_stream(self, question: str) -> AsyncIterable:
+    async def generate_stream(self, question: str, memory=None) -> AsyncIterable:
         """
         Generate a streaming answer to a given question by interacting with the language model.
         :param question: The question
@@ -196,7 +196,7 @@ async def generate_stream(self, question: str) -> AsyncIterable:
 
         # The Chain that combines the question and answer
         qa = ConversationalRetrievalChain(
-            retriever=self.vector_store.as_retriever(), combine_docs_chain=doc_chain, question_generator=standalone_question_generator)
+            retriever=self.vector_store.as_retriever(), combine_docs_chain=doc_chain, question_generator=standalone_question_generator, memory=memory)
 
         transformed_history = []
 

backend/core/llm/qa_base.py

@@ -15,6 +15,7 @@ class BrainEntity(BaseModel):
     openai_api_key: Optional[str]
     status: Optional[str]
     prompt_id: Optional[UUID]
+    linkedin: Optional[str]
     extraversion: Optional[int]
     neuroticism: Optional[int]
     conscientiousness: Optional[int]

backend/core/models/brain_entity.py

@@ -18,6 +18,7 @@ class CreateBrainProperties(BaseModel):
     max_tokens: Optional[int] = 256
     openai_api_key: Optional[str] = None
     prompt_id: Optional[UUID] = None
+    linkedin: Optional[str] = None
     extraversion: Optional[int] = None
     neuroticism: Optional[int] = None
     conscientiousness: Optional[int] = None

backend/core/models/databases/supabase/brains.py

@@ -0,0 +1,11 @@
+from typing import Any, List, Optional
+from pydantic import BaseModel
+
+class TestResult(BaseModel):
+    trait: Optional[str] = "Extraversion"
+    positive: bool = True,
+    question: Optional[str] = ""
+    answer: Optional[int] = 0
+
+# class TestResultList(BaseModel):
+#     results: List[TestResult] = []