File size: 6,474 Bytes
56f7920
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
import os
from typing import List

from llama_index.embeddings.nebius import NebiusEmbedding
from llama_index.llms.nebius import NebiusLLM
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from pymongo import MongoClient
from pymongo.operations import SearchIndexModel

llm = NebiusLLM(
    model="meta-llama/Llama-3.3-70B-Instruct-fast", api_key=os.getenv("NEBIUS_API_KEY")
)
embed_model = NebiusEmbedding(
    model_name="BAAI/bge-en-icl",
    api_key=os.getenv("NEBIUS_API_KEY"),
    embed_batch_size=10,
)

MONGO_DB_URI = os.getenv("MONGO_DB_URI")
mongo_client = MongoClient(MONGO_DB_URI)

# Database and collection names
DB_NAME = "docmcp"
COLLECTION_NAME = "doc_rag"
REPOS_COLLECTION_NAME = "ingested_repos"

VS_INDEX_NAME = "vector_index"
FTS_INDEX_NAME = "fts_index"
vs_model = SearchIndexModel(
    definition={
        "fields": [
            {
                "type": "vector",
                "path": "embedding",
                "numDimensions": 4096,
                "similarity": "cosine",
            },
            {"type": "filter", "path": "metadata.repo"},
        ]
    },
    name=VS_INDEX_NAME,
    type="vectorSearch",
)

fts_model = SearchIndexModel(
    definition={"mappings": {"dynamic": False, "fields": {"text": {"type": "string"}}}},
    name=FTS_INDEX_NAME,
    type="search",
)


def get_vector_store():
    collection = mongo_client[DB_NAME][COLLECTION_NAME]

    vector_store = MongoDBAtlasVectorSearch(
        mongodb_client=mongo_client,
        db_name=DB_NAME,
        collection_name=COLLECTION_NAME,
        vector_index_name=VS_INDEX_NAME,
        fulltext_index_name=FTS_INDEX_NAME,
        embedding_key="embedding",
        text_key="text",
    )

    collection.create_search_indexes(models=[vs_model, fts_model])

    return vector_store


def get_repos_collection():
    return mongo_client[DB_NAME][REPOS_COLLECTION_NAME]


def store_ingested_repo(repo_name: str, ingested_files: List[str]) -> bool:
    try:
        repos_collection = get_repos_collection()

        # Simple document format
        repo_doc = {
            "_id": repo_name,  # Use repo name as unique ID
            "repo_name": repo_name,
            "ingested_files": ingested_files,
            "file_count": len(ingested_files),
        }

        # Upsert the document (update if exists, insert if not)
        repos_collection.replace_one({"_id": repo_name}, repo_doc, upsert=True)

        print(f"βœ… Stored repository: {repo_name} with {len(ingested_files)} files")
        return True

    except Exception as e:
        print(f"❌ Error storing repository data: {e}")
        return False


def get_available_repos():
    try:
        repos_collection = get_repos_collection()

        # Get all repository names
        repos = repos_collection.find({}, {"repo_name": 1})
        repo_list = [repo["repo_name"] for repo in repos]

        if repo_list:
            return sorted(repo_list)
        else:
            # Fallback to hardcoded list if no repos in database
            return []

    except Exception as e:
        print(f"Error getting repos from database: {e}")
        # Fallback to hardcoded list
        return []


def get_repo_details():
    """Get detailed information about all repositories"""
    try:
        repos_collection = get_repos_collection()
        
        # Get all repository details
        repos = repos_collection.find({})
        repo_details = []
        
        for repo in repos:
            repo_info = {
                "repo_name": repo.get("repo_name", "Unknown"),
                "file_count": repo.get("file_count", 0),
                "last_updated": repo.get("last_updated", "Unknown"),
                "ingested_files": repo.get("ingested_files", [])
            }
            repo_details.append(repo_info)
        
        return repo_details
        
    except Exception as e:
        print(f"Error getting repo details: {e}")
        return []

def delete_repository_data(repo_name):
    try:
        result = {
            "success": False,
            "message": "",
            "vector_docs_deleted": 0,
            "repo_record_deleted": False,
        }

        # Delete from vector store (documents with this repo metadata)
        collection = mongo_client[DB_NAME][COLLECTION_NAME]
        vector_delete_result = collection.delete_many({"metadata.repo": repo_name})
        result["vector_docs_deleted"] = vector_delete_result.deleted_count

        # Delete from repos tracking collection
        repos_collection = get_repos_collection()
        repo_delete_result = repos_collection.delete_one({"_id": repo_name})
        result["repo_record_deleted"] = repo_delete_result.deleted_count > 0

        if result["vector_docs_deleted"] > 0 or result["repo_record_deleted"]:
            result["success"] = True
            result["message"] = f"βœ… Successfully deleted repository '{repo_name}'"
            if result["vector_docs_deleted"] > 0:
                result["message"] += (
                    f" ({result['vector_docs_deleted']} documents removed)"
                )
        else:
            result["message"] = (
                f"⚠️ Repository '{repo_name}' not found or already deleted"
            )

        print(result["message"])
        return result

    except Exception as e:
        error_msg = f"❌ Error deleting repository '{repo_name}': {str(e)}"
        print(error_msg)
        return {
            "success": False,
            "message": error_msg,
            "vector_docs_deleted": 0,
            "repo_record_deleted": False,
        }


def get_repository_stats():
    try:
        repos_collection = get_repos_collection()
        collection = mongo_client[DB_NAME][COLLECTION_NAME]

        # Count total repositories
        total_repos = repos_collection.count_documents({})

        # Count total documents in vector store
        total_docs = collection.count_documents({})

        # Get total files across all repos
        total_files = 0
        repos = repos_collection.find({}, {"file_count": 1})
        for repo in repos:
            total_files += repo.get("file_count", 0)

        return {
            "total_repositories": total_repos,
            "total_documents": total_docs,
            "total_files": total_files,
        }

    except Exception as e:
        print(f"Error getting repository stats: {e}")
        return {"total_repositories": 0, "total_documents": 0, "total_files": 0}