fix: replace openAI with litellm to support multiple llm providers

.gitignore

@@ -262,3 +262,5 @@ CLAUDE.md
 tests/**/test_site
 tests/**/reports
 tests/**/benchmark_reports
+
+docs/**/data

docs/apps/linkdin/c4ai_insights.py

@@ -43,7 +43,7 @@ import numpy as np
 import pandas as pd
 import hashlib
 
-from openai import OpenAI                    # same SDK you pre-loaded
+from litellm import completion   #Support any LLM Provider
 
 # ───────────────────────────────────────────────────────────────────────────────
 # Utils
@@ -70,11 +70,12 @@ def dev_defaults() -> SimpleNamespace:
         out_dir="./insights_debug",
         embed_model="all-MiniLM-L6-v2",
         top_k=10,
-        openai_model="gpt-4.1",
+        llm_provider="openai/gpt-4.1", 
+        llm_api_key=None,
         max_llm_tokens=8000,
         llm_temperature=1.0,
-        workers=4,           # parallel processing
+        workers=4, 
-        stub=False,          # manual
+        stub=False
     )
 
 # ───────────────────────────────────────────────────────────────────────────────
@@ -166,7 +167,7 @@ def build_company_graph(companies, embeds:np.ndarray, top_k:int) -> Dict[str,Any
 # ───────────────────────────────────────────────────────────────────────────────
 # Org-chart via LLM
 # ───────────────────────────────────────────────────────────────────────────────
-async def infer_org_chart_llm(company, people, client:OpenAI, model_name:str, max_tokens:int, temperature:float, stub:bool):
+async def infer_org_chart_llm(company, people, llm_provider:str, api_key:str, max_tokens:int, temperature:float, stub:bool):
     if stub:
         # Tiny fake org-chart when debugging offline
         chief = random.choice(people)
@@ -202,15 +203,19 @@ Here is a JSON list of employees:
 Return JSON: {{ "nodes":[{{id,name,title,dept,yoe_total,yoe_current,seniority_score,decision_score,avatar_url,profile_url}}], "edges":[{{source,target,type,confidence}}] }}
 """}
     ]
-    resp = client.chat.completions.create(
+    resp = completion(
-        model=model_name,
+        model=llm_provider,
         messages=prompt,
         max_tokens=max_tokens,
         temperature=temperature,
-        response_format={"type":"json_object"}
+        response_format={"type":"json_object"},
+        api_key=api_key
     )
     chart = json.loads(resp.choices[0].message.content)
-    chart["meta"] = dict(model=model_name, generated_at=datetime.now(UTC).isoformat())
+    chart["meta"] = dict(
+        model=llm_provider,
+        generated_at=datetime.now(UTC).isoformat()
+    )
     return chart
 
 # ───────────────────────────────────────────────────────────────────────────────
@@ -270,15 +275,11 @@ async def run(opts):
     logging.info(f"[bold cyan]Loaded[/] {len(companies)} companies, {len(people)} people")
 
     logging.info("[bold]⇢[/] Embedding company descriptions…")
-    # embeds = embed_descriptions(companies, opts.embed_model, opts)
+    embeds = embed_descriptions(companies, opts.embed_model, opts)
     
     logging.info("[bold]⇢[/] Building similarity graph")
-    # company_graph = build_company_graph(companies, embeds, opts.top_k)
+    company_graph = build_company_graph(companies, embeds, opts.top_k)
-    # dump_json(company_graph, out_dir/"company_graph.json")
+    dump_json(company_graph, out_dir/"company_graph.json")
-
-    # OpenAI client (only built if not debugging)
-    stub = bool(opts.stub)
-    client = OpenAI() if not stub else None
 
     # Filter companies that need processing
     to_process = []
@@ -311,14 +312,13 @@ async def run(opts):
             async def process_one(comp):
                 handle = comp["handle"].strip("/").replace("/","_")
                 persons = [p for p in people if p["company_handle"].strip("/") == comp["handle"].strip("/")]
-                
                 chart = await infer_org_chart_llm(
                     comp, persons,
-                    client=client if client else OpenAI(api_key="sk-debug"),
+                    llm_provider=opts.llm_provider,
-                    model_name=opts.openai_model,
+                    api_key=getattr(opts, 'llm_api_key', None),
                     max_tokens=opts.max_llm_tokens,
                     temperature=opts.llm_temperature,
-                    stub=stub,
+                    stub=opts.stub or False,
                 )
                 chart["meta"]["company"] = comp["name"]
                 
@@ -354,18 +354,21 @@ def build_arg_parser():
     p = argparse.ArgumentParser(description="Build graphs & visualisation from Stage-1 output")
     p.add_argument("--in",       dest="in_dir",  required=False, help="Stage-1 output dir", default=".")
     p.add_argument("--out",      dest="out_dir", required=False, help="Destination dir",   default=".")
-    p.add_argument("--embed_model", default="all-MiniLM-L6-v2")
+    p.add_argument("--embed-model", default="all-MiniLM-L6-v2")
-    p.add_argument("--top_k", type=int, default=10, help="Top-k neighbours per company")
+    p.add_argument("--top-k", type=int, default=10, help="Top-k neighbours per company")
-    p.add_argument("--openai_model", default="gpt-4.1")
+    p.add_argument("--llm-provider", default="openai/gpt-4.1", 
-    p.add_argument("--max_llm_tokens", type=int, default=8024)
+                  help="LLM model to use in format 'provider/model_name' (e.g., 'anthropic/claude-3')")
-    p.add_argument("--llm_temperature", type=float, default=1.0)
+    p.add_argument("--llm-api-key", help="API key for LLM provider (defaults to env vars)")
+    p.add_argument("--max-llm-tokens", type=int, default=8024)
+    p.add_argument("--llm-temperature", type=float, default=1.0)
     p.add_argument("--stub", action="store_true", help="Skip OpenAI call and generate tiny fake org charts")
     p.add_argument("--workers", type=int, default=4, help="Number of parallel workers for LLM inference")
     return p
 
 def main():
     dbg = dev_defaults()
-    opts = dbg if True else build_arg_parser().parse_args()
+    # opts = dbg if True else build_arg_parser().parse_args()
+    opts = build_arg_parser().parse_args()
     asyncio.run(run(opts))
 
 if __name__ == "__main__":