add extract nodes from text prompt

graphiti_core/prompts/extract_nodes.py

@@ -24,12 +24,14 @@ class Prompt(Protocol):
     v1: PromptVersion
     v2: PromptVersion
     extract_json: PromptVersion
+    extract_text: PromptVersion
 
 
 class Versions(TypedDict):
     v1: PromptFunction
     v2: PromptFunction
     extract_json: PromptFunction
+    extract_text: PromptFunction
 
 
 def v1(context: dict[str, Any]) -> list[Message]:
@@ -144,4 +146,44 @@ def extract_json(context: dict[str, Any]) -> list[Message]:
     ]
 
 
-versions: Versions = {'v1': v1, 'v2': v2, 'extract_json': extract_json}
+def extract_text(context: dict[str, Any]) -> list[Message]:
+    sys_prompt = """You are an AI assistant that extracts entity nodes from conversational text. Your primary task is to identify and extract the speaker and other significant entities mentioned in the conversation."""
+
+    user_prompt = f"""
+Given the following conversation, extract entity nodes from the CURRENT MESSAGE that are explicitly or implicitly mentioned:
+
+Conversation:
+{json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)}
+<CURRENT MESSAGE>
+{context["episode_content"]}
+
+Guidelines:
+2. Extract significant entities, concepts, or actors mentioned in the conversation.
+3. Provide concise but informative summaries for each extracted node.
+4. Avoid creating nodes for relationships or actions.
+5. Avoid creating nodes for temporal information like dates, times or years (these will be added to edges later).
+6. Be as explicit as possible in your node names, using full names and avoiding abbreviations.
+
+Respond with a JSON object in the following format:
+{{
+    "extracted_nodes": [
+        {{
+            "name": "Unique identifier for the node (use the speaker's name for speaker nodes)",
+            "labels": ["Entity", "OptionalAdditionalLabel"],
+            "summary": "Brief summary of the node's role or significance"
+        }}
+    ]
+}}
+"""
+    return [
+        Message(role='system', content=sys_prompt),
+        Message(role='user', content=user_prompt),
+    ]
+
+
+versions: Versions = {
+    'v1': v1,
+    'v2': v2,
+    'extract_json': extract_json,
+    'extract_text': extract_text,
+}

graphiti_core/utils/maintenance/node_operations.py

@@ -48,6 +48,29 @@ async def extract_message_nodes(
     return extracted_node_data
 
 
+async def extract_text_nodes(
+    llm_client: LLMClient, episode: EpisodicNode, previous_episodes: list[EpisodicNode]
+) -> list[dict[str, Any]]:
+    # Prepare context for LLM
+    context = {
+        'episode_content': episode.content,
+        'episode_timestamp': episode.valid_at.isoformat(),
+        'previous_episodes': [
+            {
+                'content': ep.content,
+                'timestamp': ep.valid_at.isoformat(),
+            }
+            for ep in previous_episodes
+        ],
+    }
+
+    llm_response = await llm_client.generate_response(
+        prompt_library.extract_nodes.extract_text(context)
+    )
+    extracted_node_data = llm_response.get('extracted_nodes', [])
+    return extracted_node_data
+
+
 async def extract_json_nodes(
     llm_client: LLMClient,
     episode: EpisodicNode,
@@ -73,8 +96,10 @@ async def extract_nodes(
 ) -> list[EntityNode]:
     start = time()
     extracted_node_data: list[dict[str, Any]] = []
-    if episode.source in [EpisodeType.message, EpisodeType.text]:
+    if episode.source == EpisodeType.message:
         extracted_node_data = await extract_message_nodes(llm_client, episode, previous_episodes)
+    elif episode.source == EpisodeType.text:
+        extracted_node_data = await extract_text_nodes(llm_client, episode, previous_episodes)
     elif episode.source == EpisodeType.json:
         extracted_node_data = await extract_json_nodes(llm_client, episode)