Fix graphrag : "role" user (#2273)

### What problem does this PR solve?

#2270 

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

agent/component/jin10.py

@@ -100,8 +100,8 @@ class Jin10(ComponentBase, ABC):
                 if self._param.symbols_datatype == "quotes":
                     for i in response['data']:
                         i['Selling Price'] = i['a']
-                        i['buying price'] = i['b']
-                        i['commodity code'] = i['c']
+                        i['Buying Price'] = i['b']
+                        i['Commodity Code'] = i['c']
                         i['Stock Exchange'] = i['e']
                         i['Highest Price'] = i['h']
                         i['Yesterday’s Closing Price'] = i['hc']

graphrag/claim_extractor.py

@@ -170,7 +170,7 @@ class ClaimExtractor:
                     }
         text = perform_variable_replacements(self._extraction_prompt, variables=variables)
         gen_conf = {"temperature": 0.5}
-        results = self._llm.chat(text, [], gen_conf)
+        results = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
         claims = results.strip().removesuffix(completion_delimiter)
         history = [{"role": "system", "content": text}, {"role": "assistant", "content": results}]
 

graphrag/community_reports_extractor.py

@@ -76,7 +76,7 @@ class CommunityReportsExtractor:
                 text = perform_variable_replacements(self._extraction_prompt, variables=prompt_variables)
                 gen_conf = {"temperature": 0.3}
                 try:
-                    response = self._llm.chat(text, [], gen_conf)
+                    response = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
                     token_count += num_tokens_from_string(text + response)
                     response = re.sub(r"^[^\{]*", "", response)
                     response = re.sub(r"[^\}]*$", "", response)
@@ -125,4 +125,5 @@ class CommunityReportsExtractor:
         report_sections = "\n\n".join(
             f"## {finding_summary(f)}\n\n{finding_explanation(f)}" for f in findings
         )
-        return f"# {title}\n\n{summary}\n\n{report_sections}"
+     
+        return f"# {title}\n\n{summary}\n\n{report_sections}"

graphrag/entity_resolution.py

@@ -125,7 +125,7 @@ class EntityResolution:
                     }
                     text = perform_variable_replacements(self._resolution_prompt, variables=variables)
 
-                    response = self._llm.chat(text, [], gen_conf)
+                    response = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
                     result = self._process_results(len(candidate_resolution_i[1]), response,
                                                    prompt_variables.get(self._record_delimiter_key,
                                                                         DEFAULT_RECORD_DELIMITER),

graphrag/graph_extractor.py

@@ -163,7 +163,7 @@ class GraphExtractor:
         token_count = 0
         text = perform_variable_replacements(self._extraction_prompt, variables=variables)
         gen_conf = {"temperature": 0.3}
-        response = self._llm.chat(text, [], gen_conf)
+        response = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
         token_count = num_tokens_from_string(text + response)
 
         results = response or ""

graphrag/mind_map_extractor.py

@@ -180,7 +180,7 @@ class MindMapExtractor:
         }
         text = perform_variable_replacements(self._mind_map_prompt, variables=variables)
         gen_conf = {"temperature": 0.5}
-        response = self._llm.chat(text, [], gen_conf)
+        response = self._llm.chat(text, [{"role": "user", "content": "Output:"}], gen_conf)
         response = re.sub(r"```[^\n]*", "", response)
         print(response)
         print("---------------------------------------------------\n", self._todict(markdown_to_json.dictify(response)))