Adding GroqSearch Space

researcher.py

@@ -0,0 +1,100 @@
+from config import *
+import os
+from dotenv import load_dotenv, find_dotenv
+import json
+import requests
+from langchain_groq import ChatGroq
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.document_loaders.url import UnstructuredURLLoader
+from langchain.vectorstores.faiss import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import os
+load_dotenv(find_dotenv())
+
+class Researcher:
+
+    def __init__(self):
+        self.serper_api_key = os.getenv("SERPER_API_KEY")
+        self.groq_api_key = os.getenv("GROQ_API_KEY")
+        self.prompt_template = PromptTemplate(
+            template=PROMPT_TEMPLATE,
+            input_variables=INPUT_VARIABLES
+        )
+        self.text_splitter = RecursiveCharacterTextSplitter(
+            separators=SEPARATORS,
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=CHUNK_OVERLAP
+        )
+        self.llm = ChatGroq(temperature=0.5, model_name="mixtral-8x7b-32768", groq_api_key=self.groq_api_key)
+        self.hfembeddings = HuggingFaceEmbeddings(
+                            model_name=EMBEDDER, 
+                            model_kwargs={'device': 'cpu'}
+                        )
+
+    def search_articles(self, query):
+        url = "https://google.serper.dev/search"
+        data = json.dumps({"q": query})
+
+        headers = {
+        'X-API-KEY': self.serper_api_key,
+        'Content-Type': 'application/json'
+        }
+
+        try:
+            response = requests.post(url, headers=headers, data=data)
+            response.raise_for_status()  # Raises an HTTPError for bad responses
+            return response.json()
+        except requests.exceptions.HTTPError as e:
+            print(f"HTTP Error: {e}")
+        except requests.exceptions.ConnectionError as e:
+            print(f"Connection Error: {e}")
+        except requests.exceptions.Timeout as e:
+            print("Timeout Error:", e)
+        except requests.exceptions.RequestException as e:
+            print("Unexpected Error:", e)
+        return {}  # Return an empty dict in case of failure
+
+    
+    def research_answerer(self):
+    
+        research_qa_chain = RetrievalQA.from_chain_type(
+                llm=self.llm,
+                chain_type=CHAIN_TYPE,
+                retriever= self.db.as_retriever(search_kwargs=SEARCH_KWARGS),
+                return_source_documents=True,
+                verbose=True,
+                chain_type_kwargs={"prompt": self.prompt_template}
+            )
+        return research_qa_chain
+
+    def get_urls(self, articles):
+        urls = []
+        try:
+            urls.append(articles["answerBox"]["link"])
+        except:
+            pass
+        for i in range(0, min(3, len(articles["organic"]))):
+            urls.append(articles["organic"][i]["link"])
+        return urls
+    
+    def get_content_from_urls(self, urls):
+        loader = UnstructuredURLLoader(urls=urls)
+        research_content = loader.load()
+        return research_content
+    
+    def research_given_query(self, research_objective, research_content):
+        
+        docs = self.text_splitter.split_documents(research_content)
+        self.db = FAISS.from_documents(documents=docs, embedding=self.hfembeddings)
+        bot = self.research_answerer()
+        research_out =bot({"query": research_objective})
+        return research_out["result"]
+
+    def research(self, query):
+        search_articles = self.search_articles(query)
+        urls = self.get_urls(search_articles)
+        research_content = self.get_content_from_urls(urls)
+        answer = self.research_given_query(query, research_content)
+        return answer