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Update sherlock2.py
Browse files- sherlock2.py +9 -7
sherlock2.py
CHANGED
@@ -89,13 +89,13 @@ def search_and_scrape_wikipedia(keywords, max_topics_per_query=3, react_model='g
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**Thought 1:** I need to search Wikipedia for information related to "{question}".
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**Action 1:**
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**Observation 1:** {observation} # This will be filled in during the process
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# ... (Further Thought-Action-Observation steps as needed)
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**Action N:**
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"""
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search_history = set() # Keep track of explored topics to avoid redundancy
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@@ -103,7 +103,8 @@ def search_and_scrape_wikipedia(keywords, max_topics_per_query=3, react_model='g
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react_model = genai.GenerativeModel(react_model) # Initialize the generative model
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for query in keywords:
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for search_term in search_terms:
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if search_term in search_history:
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@@ -117,19 +118,20 @@ def search_and_scrape_wikipedia(keywords, max_topics_per_query=3, react_model='g
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# Perform ReAct-based search and extraction
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while True:
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response = react_model.generate_content([react_prompt], stop_sequences=["
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# Extract action and observation from the response
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action, observation = re.findall(r"<(.*?)>(.*?)
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# Update the ReAct prompt with the observation
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react_prompt = react_prompt.replace("{observation}", observation.strip(), 1) # Replace only the first occurrence
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if action == "finish":
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answer = observation.strip() # Extract the final answer
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break # Exit the loop when
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page
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url = page.url
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additional_sources = []
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**Thought 1:** I need to search Wikipedia for information related to "{question}".
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**Action 1:**
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**Observation 1:** {observation} # This will be filled in during the process
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# ... (Further Thought-Action-Observation steps as needed)
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**Action N:** # The final answer will be extracted from here
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"""
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search_history = set() # Keep track of explored topics to avoid redundancy
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react_model = genai.GenerativeModel(react_model) # Initialize the generative model
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for query in keywords:
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# Search Wikipedia (modified line)
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search_terms = wikipedia.search(query, results=max_topics_per_query, suggestion=False, srsearch=query)
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for search_term in search_terms:
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if search_term in search_history:
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# Perform ReAct-based search and extraction
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while True:
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response = react_model.generate_content([react_prompt], stop_sequences=[""])
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# Extract action and observation from the response
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action, observation = re.findall(r"<(.*?)>(.*?)", response.text)[-1] # Get the last action and observation
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# Update the ReAct prompt with the observation
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react_prompt = react_prompt.replace("{observation}", observation.strip(), 1) # Replace only the first occurrence
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if action == "finish":
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answer = observation.strip() # Extract the final answer
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break # Exit the loop when is encountered
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# Get Wikipedia page and URL (modified line)
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page = wikipedia.page(search_term, auto_suggest=False)
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url = page.url
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additional_sources = []
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