two version of R2R are here HEAD master

1 files changed, 194 insertions, 0 deletions

diff --git a/R2R/r2r/examples/scripts/advanced_kg_cookbook.py b/R2R/r2r/examples/scripts/advanced_kg_cookbook.py
new file mode 100755
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+++ b/R2R/r2r/examples/scripts/advanced_kg_cookbook.py
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+import json
+import os
+
+import fire
+import requests
+from bs4 import BeautifulSoup, Comment
+
+from r2r import (
+    EntityType,
+    R2RClient,
+    R2RPromptProvider,
+    Relation,
+    update_kg_prompt,
+)
+
+
+def escape_braces(text):
+    return text.replace("{", "{{").replace("}", "}}")
+
+
+def get_all_yc_co_directory_urls():
+    this_file_path = os.path.abspath(os.path.dirname(__file__))
+    yc_company_dump_path = os.path.join(
+        this_file_path, "..", "data", "yc_companies.txt"
+    )
+
+    with open(yc_company_dump_path, "r") as f:
+        urls = f.readlines()
+    urls = [url.strip() for url in urls]
+    return {url.split("/")[-1]: url for url in urls}
+
+
+# Function to fetch and clean HTML content
+def fetch_and_clean_yc_co_data(url):
+    # Fetch the HTML content from the URL
+    response = requests.get(url)
+    response.raise_for_status()  # Raise an error for bad status codes
+    html_content = response.text
+
+    # Parse the HTML content with BeautifulSoup
+    soup = BeautifulSoup(html_content, "html.parser")
+
+    # Remove all <script>, <style>, <meta>, <link>, <header>, <nav>, and <footer> elements
+    for element in soup(
+        ["script", "style", "meta", "link", "header", "nav", "footer"]
+    ):
+        element.decompose()
+
+    # Remove comments
+    for comment in soup.findAll(text=lambda text: isinstance(text, Comment)):
+        comment.extract()
+
+    # Select the main content (you can adjust the selector based on the structure of your target pages)
+    main_content = soup.select_one("main") or soup.body
+
+    if main_content:
+        spans = main_content.find_all(["span", "a"])
+
+        proc_spans = []
+        for span in spans:
+            proc_spans.append(span.get_text(separator=" ", strip=True))
+        span_text = "\n".join(proc_spans)
+
+        # Extract the text content from the main content
+        paragraphs = main_content.find_all(
+            ["p", "h1", "h2", "h3", "h4", "h5", "h6", "li"]
+        )
+        cleaned_text = (
+            "### Bulk:\n\n"
+            + "\n\n".join(
+                paragraph.get_text(separator=" ", strip=True)
+                for paragraph in paragraphs
+            )
+            + "\n\n### Metadata:\n\n"
+            + span_text
+        )
+
+        return cleaned_text
+    else:
+        return "Main content not found"
+
+
+def execute_query(provider, query, params={}):
+    print(f"Executing query: {query}")
+    with provider.client.session(database=provider._database) as session:
+        result = session.run(query, params)
+        return [record.data() for record in result]
+
+
+def main(
+    max_entries=50,
+    local_mode=True,
+    base_url="http://localhost:8000",
+):
+
+    # Specify the entity types for the KG extraction prompt
+    entity_types = [
+        EntityType("COMPANY"),
+        EntityType("SCHOOL"),
+        EntityType("LOCATION"),
+        EntityType("PERSON"),
+        EntityType("DATE"),
+        EntityType("OTHER"),
+        EntityType("QUANTITY"),
+        EntityType("EVENT"),
+        EntityType("INDUSTRY"),
+        EntityType("MEDIA"),
+    ]
+
+    # Specify the relations for the KG construction
+    relations = [
+        # Founder Relations
+        Relation("EDUCATED_AT"),
+        Relation("WORKED_AT"),
+        Relation("FOUNDED"),
+        # Company relations
+        Relation("RAISED"),
+        Relation("REVENUE"),
+        Relation("TEAM_SIZE"),
+        Relation("LOCATION"),
+        Relation("ACQUIRED_BY"),
+        Relation("ANNOUNCED"),
+        Relation("INDUSTRY"),
+        # Product relations
+        Relation("PRODUCT"),
+        Relation("FEATURES"),
+        Relation("TECHNOLOGY"),
+        # Additional relations
+        Relation("HAS"),
+        Relation("AS_OF"),
+        Relation("PARTICIPATED"),
+        Relation("ASSOCIATED"),
+    ]
+
+    client = R2RClient(base_url=base_url)
+    r2r_prompts = R2RPromptProvider()
+
+    prompt_base = (
+        "zero_shot_ner_kg_extraction"
+        if local_mode
+        else "few_shot_ner_kg_extraction"
+    )
+
+    update_kg_prompt(client, r2r_prompts, prompt_base, entity_types, relations)
+
+    url_map = get_all_yc_co_directory_urls()
+
+    i = 0
+    # Ingest and clean the data for each company
+    for company, url in url_map.items():
+        company_data = fetch_and_clean_yc_co_data(url)
+        if i >= max_entries:
+            break
+        i += 1
+
+        try:
+            # Ingest as a text document
+            file_name = f"{company}.txt"
+            with open(file_name, "w") as f:
+                f.write(company_data)
+
+            client.ingest_files(
+                [file_name],
+                metadatas=[{"title": company}],
+            )
+            os.remove(file_name)
+        except:
+            continue
+
+    print(client.inspect_knowledge_graph(1_000)["results"])
+
+    if not local_mode:
+
+        update_kg_prompt(
+            client, r2r_prompts, "kg_agent", entity_types, relations
+        )
+
+        result = client.search(
+            query="Find up to 10 founders that worked at Google",
+            use_kg_search=True,
+        )["results"]
+
+        print("result:\n", result)
+        print("Search Result:\n", result["kg_search_results"])
+
+        result = client.rag(
+            query="Find up to 10 founders that worked at Google",
+            use_kg_search=True,
+        )
+        print("RAG Result:\n", result)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)